We develop theoretical and practical large scale scientific machine learning tools for understanding global-scale atmospheric processes and high-dimensional engineering turbulence.

Publications: https://scholar.google.com/citations?user=wtHkCRIAAAAJ&hl=en

Use ML methods to analyze geodetic time series and their derivatives.

A visualization layer allowing the upload, access control, and streaming of 3D research data (LiDAR, photogrammetry...) from research data repositories sadgsdgdsg

Compute for Alex Cloninger's Lab. Admin: Alex Cloninger

Creating multiscale models of cell dynamics in skeletal muscle, using agent-based modeling.

Repo for Ashton to play around with different data visualizations for Nautilus

The Adaptive Computing and Embedded Systems (ACES) Lab, lead by Prof. Farinaz Koushanfar, focuses on making intelligent data-intensive embedded computing applications and systems. The added intelligence is to satisfy security and robustness, energy-efficiency, timeliness, IP protection rights, design automation, and many more requirements of emerging technologies.

Publications: Zhang, X., Samragh, M., Hussain, S., Huang, K., & Koushanfar, F. (2023). Scalable Binary Neural Network applications in Oblivious Inference. Javaheripi, M., Rouhani, B. D., & Koushanfar, F. (2021). SWANN: Small-World Architecture for Fast Convergence of Neural Networks.

We are working on developing new technologies and cross-stack solutions to improve the performance and energy efficiency of computer systems for emerging applications.

Publications:

Temporary testing environment for k8s experimental resources

As the scale, complexity, and variety of data grows (aka Big Data), the use of machine learning (ML) and artificial intelligence (AI) techniques to make sense of, and interact with, such data — collectively called predictive data analytics, statistical data analytics, ML-based data analytics, or simply advanced data analytics (also ADA!) — is increasingly critical for data-driven applications in the enterprise, Web, science, and other domains. Alas, building and deploying ML/AI-powered data analytics applications still involves far too many bottlenecks that slow down the lifecycle of such applications, raise costs, frustrate many application users, and in some cases, make high-quality data-driven decision making almost impossible. The mission of the ADALab is to democratize advanced data analytics by making it dramatically easier, faster, and cheaper to build and deploy ML/AI-powered data analytics applications throughout their lifecycle.

Publications: CHAP-child: An open source method for estimating sit-to-stand transitions and sedentary bout patterns from hip accelerometers among children. Jordan A. Carlson et al. (15 authors). International Journal of Behavioral Nutrition and Physical Activity 2022. CHAP-Adult: A Reliable and Valid Algorithm to Classify Sitting and Measure Sitting Patterns Using Data from Hip-Worn Accelerometers in Adults Aged 35+. John Bellettiere et al. (14 authors). Journal for the Measurement of Physical Behaviour 2022. Cerebro: A Layered Data Platform for Scalable Deep Learning. Arun Kumar, Supun Nakandala, Yuhao Zhang, Side Li, Advitya Gemawat, and Kabir Nagrecha. CIDR 2021. The CNN Hip Accelerometer Posture (CHAP) Method for Classifying Sitting Patterns from Hip Accelerometers: A Validation Study. Mikael Anne Greenwood-Hickman, Supun Nakandala, Marta M. Jankowska, Fatima Tuz-Zahra, John Bellettiere, Jordan Carlson, Paul R. Hibbing, Jingjing Zou, Andrea Z. LaCroix, Arun Kumar, and Loki Natarajan. Medicine and Science in Sports and Exercise Journal, 2021. Application of Convolutional Neural Network Algorithms for Advancing Sedentary and Activity Bout Classification. Supun Nakandala, Marta Jankowska, Fatima Tuz-Zahra, John Bellettiere, Jordan Carlson, Andrea LaCroix, Sheri Hartman, Dori Rosenberg, Jingjing Zou, Arun Kumar, and Loki Natarajan. Journal for the Measurement of Physical Behaviour, 2021. Cerebro: A Data System for Optimized Deep Learning Model Selection. Supun Nakandala, Yuhao Zhang, and Arun Kumar. VLDB 2020. Panorama: A Data System for Unbounded Vocabulary Querying over Video. Yuhao Zhang and Arun Kumar. VLDB 2020. Vista: Optimized System for Declarative Feature Transfer from Deep CNNs at Scale. Supun Nakandala and Arun Kumar. ACM SIGMOD 2020.

Deep CNNs are now the preferred way to perform image analytics in many domains including healthcare, e-commerce, security, and sociology. However, one of the main criticisms pointed against deep CNNs is the black box nature of how they make predictions. To explain CNN predictions one of the widely used approaches in the practical literature is the occlusion based explanation approach (OBE for short). However, OBE experiments are highly time consuming as they need to perform large number of re-inference requests. In this project, we apply incremental and approximate inference optimizations to accelerate the OBE workload. Our work is inspired by the long line of work in incremental view maintenance, multi-query optimization, and approximate query processing techniques in the context of relational data management systems.

Publications: Incremental and Approximate Inference for Faster Occlusion-based Deep CNN Explanations, SIGMOD 2019 Demonstration of Krypton: Optimized CNN Inference for Occlusion-based Deep CNN Explanations Allen Ordookhanians, Xin Li, Supun Nakandala, and Arun Kumar, SIGMOD 2019

Predicting Eating Events for Initiating Interventions for Obese Individuals In this project, we explore the feasibility of predicting individuals' eating behavior from GIS and traveling information. Predictions from these trained models will be used to initiate interventions (e.g., SMS messages, automated phone calls) to avoid obese individuals consuming unhealthy food.

In this project, we are developing a new system to accelerate the model selection process of Deep Learning models while ensuring the reproducibility of the training process. Existing approaches for accelerating Deep Learning model selection either incur high resource costs (e.g., network overheads, storage costs) or are not reproducible due to the inherent randomness of the physical world. Cerebro alleviates these limitations by treating the model selection process as a multi-task optimization problem and yields the best efficiency while ensuring reproducibility.

Publications: Supun Nakandala, Yuhao Zhang, and Arun Kumar. 2019. Cerebro: Efficient and Reproducible Model Selection on Deep Learning Systems. In International Workshop on Data Management for End-to-End Machine Learning (DEEM’19), June 30, 2019, Amsterdam, Netherlands. ACM, New York, NY, USA

Admiralty federation tool - federation between kubernetes clusters

Ashton Graves' scratch space for testing things on nautilus

Training big language models to do language modeling

We are creating an AI surrogate models for accelerating the transport simulation problems.

An exploratory research project which aims to develop fast molecular dynamics models for drug discovery with AI. We plan to develop novel AI methods to accelerate drug design and synthesize.

Publications: LIMO: Latent Inceptionism for Targeted Molecule Generation Peter Eckmann, Kunyang Sun, Bo Zhao, Mudong Feng, Michael Gilson, Rose Yu International Conference on Machine Learning (ICML), 2022

The ai-os workspace on Nautilus is used to train and evaluate deep learning models applied to large O(100Tb) remote-sensing datasets of Oceanography. The primary code base is called ulmo and now contains a probabilistic autoencoder and a contrastive learning model. The datasets include sea surface temperature imagery from the MODIS and VIIRS sensors, ocean model outputs from ECCO, and (most recently) sea surface height outputs from PODAAC. A variety of undergraduate and graduate students have accessed this workspace.

Publications: https://www.mdpi.com/2072-4292/13/4/744

Namespace for project that involves inferring physical models (ex. Hamiltonians, equations of motions) from data using interpretable machine learning methods

The Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, a program of Schmidt Sciences at UC San Diego led by Faculty Director Tara Javidi and Education Associate Director Ilkay Altıntaş, leverages UC San Diego’s place at the forefront of Artificial Intelligence research to train the next generation of scientific leaders pioneering the use of AI in STEM. Established with the generous support of Schmidt Sciences, a philanthropic initiative of Eric and Wendy Schmidt, this prestigious program annually supports 10-20 Postdoctoral Fellows with a two-year fellowship as they learn and apply AI techniques to their research in the engineering, mathematical, and natural sciences. The program provides a unique, cross-campus ecosystem of training and scientific discovery consisting of three interconnected core components: Community formation of a diverse cohort of researchers and scholars from across the natural and engineering sciences with an interest in the use of AI methods to accelerate scientific discoveries; Training modules in the form of courses, seminars, and panels pulling from established pedagogical areas of excellence designed to provide postdoctoral fellows with core AI technical competencies; and Co-mentoring to bridge the gap between STEM and AI, including at least one STEM faculty as the primary mentor and nominator, and a second AI/method faculty in the role of co-mentor. Schmidt AI in Science Postdocs also receive comprehensive foundational training in scientific leadership, research ethics, professional development, and other critical skills to complement the Program’s AI curriculum. In addition, they are encouraged to engage with the DEI initiative or program of their choice at UC San Diego.

This project aims to explore the integration of deep-learning-based facial expression recognition technology to enhance the capabilities of AI tutoring systems. Our team has developed a robust facial expression recognition model capable of identifying emotional and cognitive states in real-time. By integrating it with an AI tutoring system, we will be able to track students' emotional and cognitive responses during learning, such as engagement, confusion, and frustration. This approach enables the AI tutor to dynamically adapt its teaching strategies, pacing, and interaction style based on the student's emotional and cognitive states. For example, if the system detects confusion or frustration, the AI tutor can slow down the pace and proactively ask the student whether they need further clarifications or assistance. Through rigorous testing, we aim to demonstrate that integrating facial expression recognition systems into AI tutors can lead to more personalized, effective, and empathetic learning experiences, ultimately improving student outcomes and satisfaction

This is the AIEA lab (led by Leilani Gilpin) namespace for the auditors subgroup.

This is the AIEA lab (led by Leilani Gilpin) namespace for the interns subgroup.

This is the namespace for the AIEA lab (led by Leilani Gilpin) and the slugbotics club collaboration.

Publications: https://arxiv.org/abs/2409.10532

AI For Mankind AI For Mankind is a 501(c)(3) nonprofit organization with the mission of mobilizing the tech community to work on world challenging problems using AI and Data. We organize tech talks, workshops, and hackathons. We want to build a grassroot community of volunteers creating solutions using AI and Data to bring positive impacts to society at large.

Developing and adapting large foundational models to various areas of scientific research

We are interested in researching minority health, health disparities, and/or social determinants of health using AI and ML to define better health outcomes among traditionally disadvantaged groups. We start defining and creating ML models for Alzheimer's patients and covid19 data, and now looking into Hispanic datasets. We are currently working with clinical information that involves individual patients (Hispanics-Latinos, Black-African American). Data Types involved: Narrative, numerical, recorded signals: ECG, photographs-images, and textual data( which we are building the required packages)

Publications: 1. https://www.mdpi.com/2076-3417/13/3/1656 2. https://link.springer.com/chapter/10.1007/978-3-031-18344-7_26 3. https://ieeexplore.ieee.org/abstract/document/9920863 4. https://www.mdpi.com/1999-4893/15/7/230 5. https://ieeexplore.ieee.org/abstract/document/9731851 6. https://ieeexplore.ieee.org/abstract/document/9373148 7. https://ieeexplore.ieee.org/abstract/document/9373229 8. https://ieeexplore.ieee.org/abstract/document/9170957 9. https://ieeexplore.ieee.org/abstract/document/8931830

This is a group through the Arthur C. Clarke Center for Human Imagination in Collaboration with the Defanti Group to explore uses of Nautilus for ML in education

This repo is for AIP 197 interns to learn Kubernetes and nautilus for trials and experiments with the PRP team.

Publications: None to date

Experiments and projects involving Sketchfab I/O to Unreal Engine. For Neil Smith's AIP 197

Analysis of Genomics Data, CRISPR Screen, scRNA-Seq, ATAC-seq

Namespace for projects in Prof Alon Orlitsky's lab. The current focus of these projects includes large language models.

Application-Layer Traffic Optimization (ALTO) IETF workgroup

2021-2022 Amaro Lab Spike Project.This project aims to bring the machine learning and data science principles gained in the Master’s of Advanced Studies in Data Science and Engineering program to the Amaro Lab’s SARS-CoV-2 spike simulation data. The goal is to develop a machine learning model to predict whether a spike protein is in the open or closed state, and then to visualize the most important features in the model to gain insight into which protein substructures are relevant to infection of human cells. Knowledge gleaned from this process could be used not only to gain a better understanding of the dynamics of the spike protein itself but also to identify potential targets for drug development in the treatment of the SARS-CoV-2 virus.

AI and games research, primarily exploratory usage for now. (updated 1/13/23)

Publications: None yet. (updated 1/13/23)

International Research and Education Network Connections Core program: Americas-Africa Lightpaths Express and Protect (AmLight-ExP) project

Publications: https://ciara.fiu.edu/publications.html

University of California Santa Cruz Applied Machine Learning Lab

Main Nautilus space for researchers from the American Museum of Natural History

Modelling of gravitational lensing data. This includes both light curves (from LSST and other surveys) of lensed quasars and supernovae, but also modelling of imaging data of galaxy-galaxy lenses.

Create high-definition visualizations and videos of micro-CT datasets for public science communication. The data is high-resolution scans of insects, comprising a broad, comparative study into insect internal anatomy (respiratory structures in particular) with representatives from over 25 insect orders. These visualizations will be used in public outreach presentations in large viewing spaces such as the planetarium dome. 3D polygonal models generated from micro-CT datasets can hundreds of millions polygons, requiring access to high performance rendering resources to achieve completed visualizations in a reasonable time frame.

1) Generate dynamic microlensing magnification maps, integrating variable quasar accretion disk 2D models. This allows us to simulate microlensing light curves in a self-consistent way including both microlensing and accretion disk models. 2) Investigate the interplay between solitonic core and its environment within a wave dark matter halo.

Machine learning to model time series of quasar variability. This involves training neural networks to fit multivariate time series with noisy and irregularly sampled data as well as perform parameter inference.

I will use the NRP for bioinformatics analyses for the NSF/FAPESP (jointly funded) “Dry Diagonal Dimensions Project” (https://www.brdd.ib.unicamp.br/). Specifically, I will use GPU nodes to run the program Cactus (https://github.com/ComparativeGenomicsToolkit/cactus) to align whole genomes of various species of lizards and snakes. Multispecies whole genome alignment is computationally demanding although other researchers have had success using the GPU-version of Cactus for this task. I do not have access to any other (non-NRP) GPUs for this analysis. Aligned genomes are necessary for functional and structural genome annotation, phylogenetic inference, and genome scans to identify genomic regions under selection and adaptive for arid environments. I only anticipate needing NRP resources for the genome alignment step.

Using the Nautilus cluster to assemble and align whole genomes

I will use the NRP for bioinformatics analyses for the NSF/FAPESP (jointly funded) “Dry Diagonal Dimensions Project” (https://www.brdd.ib.unicamp.br/). Specifically, I will use GPU nodes to run the program Cactus (https://github.com/ComparativeGenomicsToolkit/cactus) to align whole genomes of various species of lizards and snakes. Multispecies whole genome alignment is computationally demanding although other researchers have had success using the GPU-version of Cactus for this task. I do not have access to any other (non-NRP) GPUs for this analysis. Aligned genomes are necessary for functional and structural genome annotation, phylogenetic inference, and genome scans to identify genomic regions under selection and adaptive for arid environments. I only anticipate needing NRP resources for the genome alignment step.

I will use the NRP for bioinformatics analyses for the NSF/FAPESP (jointly funded) “Dry Diagonal Dimensions Project” (https://www.brdd.ib.unicamp.br/). Specifically, I will use GPU nodes to run the program Cactus (https://github.com/ComparativeGenomicsToolkit/cactus) to align whole genomes of various species of lizards and snakes. Multispecies whole genome alignment is computationally demanding although other researchers have had success using the GPU-version of Cactus for this task. I do not have access to any other (non-NRP) GPUs for this analysis. Aligned genomes are necessary for functional and structural genome annotation, phylogenetic inference, and genome scans to identify genomic regions under selection and adaptive for arid environments. I only anticipate needing NRP resources for the genome alignment step.

Whole-genome shotgun sequencing data of approximately 120-170 chameleon tissues for phylogenetic analysis and potentially downstream biogeography analysis

Namespace for AMNH Jupyterhub hosted on Nautilus. Used for testing by researchers

Namespace to run NDN experiments (collaboration AmLight-AMPATH/UFBA)

Alex Tseng's deep learning projects for the Kundaje lab

Publications: Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics [Preprint] [Code] Tseng AM, Shrikumar A, Kundaje A (Accepted to NeurIPS 2020)

annashch@stanford.edu namespace Deep learning models in genomics: Exploring new deep learning architectures to improve the classification accuracy of deep learning models in genomics. More generally, work focuses on leveraging deep learning for genomics in conjunction with interpretation techniques to extract novel insights about regulatory genomics. Decoding regulatory DNA sequence in keratinocyte differentiation: Development and differentiation are biological processes that involve cascades of transcription factors interacting with dynamic chromatin landscapes to produce cell-type specific transcriptional programs. Epidermal differentiation, in which a self-renewing progenitor keratinocyte becomes a terminally differentiated keratinocyte, is well suited for studying fine-grained changes in chromatin and transcription and addressing fundamental questions about the dynamic combinatorial logic of regulation. To answer these questions, genomic profiling of transcriptional state (using 3' RNA-seq) and chromatin state (using ATAC-seq and ChIP-seq on histone marks) was captured at 12 hour intervals across 6 days of in vitro differentiation of primary keratinocytes. We inferred transcriptional and epigenetic trajectories across time to elucidate dynamically coordinated modules of genes and regulatory elements. We then developed deep, multi-task convolutional neural networks to learn predictive DNA sequence drivers of chromatin dynamics. To discover motifs and coordinated motif sets (grammars) from the neural net, we used backpropagation methods to derive nucleotide level importance scores in regulatory elements across time that are then used to extract grammars that are predictive of accessibility. We use these grammars in conjunction with expression and chromosome conformation assays to annotate functional modules that define known and novel differentiation programs. The resulting framework provides a generalizable approach to dissecting dynamic maps of combinatorial regulation encoded in DNA sequence.

Using for Anshul for debugging the PyTorch and running code

This namespace is created for research work conducted by Dr. Akond Rahman's research group at Auburn University CSSE department

Managed by Anthony Furman as a part of HARE Labs at UCSC.

For use with horizontal scalability on a kubernettes cluster for fluid dynamics simulations

scratch pad for development of various lab projects.

AI powered analysis of EEG and airflow data to tackle open questions in the field of sleep apnea

precision medicine workflow

Approximate computing framework for enabling stochastic generative models on low-power devices for real-time applications. Exploring the effect of structured pruning on generative performance

Publications: AX-DBN: An Approximate Computing Framework for the Design of Low-Power Discriminative Deep Belief Networks

Large scale virtual screening of compounds. Indentification of first-in-lead compounds for different diseases.

Advanced Persistent Threat (APT) Detection. We inject the NSL-KDD dataset PCAPs into the interface

UBER Arachne installation

ArcGIS Enterprise for Kubernetes deployment and config

A namespace for the ct-image segmentation project for surgical scene understanding.

Namespace for data-driven motion planning algorithms including motion transformers network and motion planning networks.

ARchitectures for DAta group at UC Irvine. Specialized Architectures for Graph Neural Networks, Genome Sequence Alignment, Graph Mining, Precision Agriculture, and more.

namespace argocd where all ArgoCD resources will be installed

Namespace for project in Prof Arya Mazumdar's lab. The main focus of projects include statistical estimation, theoretical machine learning and federated learning.

Test namespace for Ashton to get familiar with Nautilus

Assel's new namespace, can be used for training and testing neural networks.

Assistive Sociotechnical Solutions for Individuals with Special needs using Technology (ASSIST) Lab is led by Professor Sri Kurniawan. The long term research agenda of ASSIST Lab is to help people with special needs (PSN) maintain a high quality of life through technology. PSN include people with disabilities, older persons, young children, people in the developing countries and people with low socioeconomic status and education; in general those who require special accommodation when learning and using technology.

Publications: Weekley, Jeffrey, and Sri Kurniawan. "Towards a Human-centered Tutorial Design for the Nautilus Cluster." Practice and Experience in Advanced Research Computing. 2023. 386-389

Testing in-network caches for ATLAS conditions and CVMFS.

This project focus on implementation of charged particle tracking pipeline as a Triton Inference Server. Clients implemented in ACTS will send track-finding requests to the Triton server and the server will return track candidates to the client after processing. The pipeline contains several track reconstruction algorithms. Because of the heterogeneity and dependency chain of the pipeline, we will explore different server settings to maximize the throughput of the pipeline, and we will study the scalability of the inference server and time reduction of the client.

Authentik is a OAUTH provider that will proxy CiLogon for Nautilus

Autogole RNP federated namespace

Autoslug is a UCSC computational club focusing on ML and Computer Vision techniques.

i) Building physics-based AI models on patient sequence data to identify pathways of drug resistance in HIV. ii) Using cryo-EM structural biology and image processing of HIV drug-resistant mutant proteins to rationalize the mechanisms of resistance.

This project explores 3D face reconstruction from casually captured images.

Publications: https://openaccess.thecvf.com/content/CVPR2022/html/Luo_How_Much_Does_Input_Data_Type_Impact_Final_Face_Model_CVPR_2022_paper.html

Project Name: A Platform for Mobile Citizen Science Apps with Client-side Machine Learning. In this project, we are developing an open-source software platform that allows a domain researcher to easily create a citizen science mobile app with client-side integrated machine learning models for collecting data with real-time analysis. The apps created with our platform can help the participants with machine learning enhanced guidance to recognize the correct data and increase the efficiency of the data collection process. Also training Nerf based models

Publications: https://scholar.google.ca/citations?view_op=view_citation&hl=en&user=zkUX0q0AAAAJ&sortby=pubdate&citation_for_view=zkUX0q0AAAAJ:Se3iqnhoufwC

Wildfire analysis with ML approaches.

Deep Learning-Based Rip Current Detection: In this project, we train fully supervised deep learning object detectors to detect rip currents.

Approximate computing framework for enabling deep generative models on low-power platforms.

Publications: AX-DBN: An Approximate Computing Framework for the Design of Low-Power Discriminative Deep Belief Networks PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems

namespace for developing workflow for training models for cms anomaly detection at the L1 trigger (AXOL1TL)

Publications: https://cds.cern.ch/record/2876546?ln=en

A place for CSUB Staff to explore NRP and Nautilus.

Working on Variant Calling for Long sequence gene data

Publications: None.

Use reinforcement learning to help videogame players navigate.

Using neural networks to detect binary black hole mergers from time domain gravitational wave strain

Underwater ocean acoustic modelling using ray tracing on Earth sized scales.

We study biologically plausible neural networks and learning rules

Publications: To be updated

Namespace to test atlas ripe software probes installation and deployment automation

Publications: https://cloudalbania.com/2023-05-implement-a-ripe-atlas-probe-in-kubernetes/

testing ssl binderhub extentions. We have binderhub instances running at SSL and want to deploy notebook servers in Nautilus.

Research related to biomedical and clinical natural language processing. Includes Information Extraction, Summarization, and Question Answering

Namespace dedicated to building software for the biocore github organization. Biocore stands for Collaboratively developed bioinformatics software.

Exploring the potential for AI to improve biodiversity research

Pilot namespace for NDSU CCAST testing and development.

PhD Project relating to the intersection of Radio Interferometry (Fourier Optics) and Machine Learning.

Machine Learning applications using Fermi LAT data.

University of California Santa Cruz BME Bootcamp for the Genomics Institute.

bowtie2-genomix is part of the CPU validation of the genimix aligner

Nautilus access for testing neural net and LSTM training.

The Braingeneers are developing the infrastructure to grow cortical organoids at scale and interface with them in order to record and stimulate neurons. This will enable the application of modern AI approaches to uncover how genetic changes enhanced human brain architecture and computing capacity during primate evolution as well as to better understand how neurons function towards back porting this into in-silico machine learning models.

Publications: https://www.biorxiv.org/content/10.1101/2021.07.29.453595v2 https://cenic.org/blog/prp-boosts-inter-campus-collaboration-on-brain-research https://www.nature.com/articles/s41593-024-01715-2 https://www.biorxiv.org/content/10.1101/2024.03.15.585237v1 DOI: 10.1371/journal.pone.0312438 DOI: 10.1101/2024.11.14.623530 DOI: 10.1101/2024.11.13.623525 DOI: 10.1101/2024.03.15.585237 DOI: 10.1016/j.celrep.2023.112318 DOI: 10.1016/j.heliyon.2022.e11596 DOI: 10.1016/j.iot.2022.100618 DOI: 10.1088/1741-2552/ac310a

Experiments for braTS Segmentation Dataset for MRI Dataset. Usually requires higher-end GPUs

Neural Network Quantization using Pytorch Brevitas library. Gradual removal of skip connections through careful knowledge distillation.

Publications: https://arxiv.org/abs/2102.01351

This is a POC space for Brown University testing. The plan is to test the space for AI/ML workloads and teach labs at Brown to use Nautilus.

Computer vision and robotics research for the Berkeley Vision Lab

The California Biomass Residue Emissions Characterization (C-BREC) model provides a life-cycle assessment framework for the use of California forest residues for electricity generation. The current extension of this model seeks to update input layers, expand use cases of forest residue and improve computation efficiency. (https://schatzcenter.org/cbrec/)

Publications: https://iopscience.iop.org/article/10.1088/1748-9326/acbd93

This namespace is for prototyping C2Hub, a platform for cybersecurity classroom. We will targeted to submit a proposal for this platform to NSF IUSE:EDU program. We will upload small amount of data and leverage the JuypterHub to demonstrate the flexibility of using Internet measurement datasets in undergraduate cybersecurity class.

Publications: https://cseweb.ucsd.edu/classes/wi23/cse291-e/syllabus.html

A project focused on sustainability of computer systems design, both in terms of operational and embedded carbon reduction (https://c3lab.net)

A project focused on sustainability of computer systems design, both in terms of operational and embedded carbon reduction (https://c3lab.net)

A project focused on sustainability of computer systems design, both in terms of operational and embedded carbon reduction (https://c3lab.net)

A project focused on sustainability of computer systems design, both in terms of operational and embedded carbon reduction (https://c3lab.net)

clabernetes oci://ghcr.io/srl-labs/clabernetes/clabernetes

c9s-vlan containerlab clabernetes namespace clabernetes-manager

We are building an open source model to predict how single nucleotide variants at the DNA level influence post-translational modifications (PTMs) at the protein level. Genome-wide association studies have been highly successful at identifying genes that differ in human disease and disorders, but little is known about how they influence biology. PTMs, such as glycosylation and serotonylation, are essential for protein function and influence myriad processes, from gene regulation to cell-cell communication. Our proposed PTM prediction model will use the powerful protein modeler AlphaFold, 3D point cloud refinement, and unsupervised transfer learning techniques to transform existing data into breakthroughs in functional genetics. This broadly relevant software will make it possible to see how disparate mutations converge to dysregulate the systems underlying these health conditions.

Publications: https://scholar.google.com/citations?user=EHGYFwYAAAAJ&hl=en

The main focus of our group is single cell systems biology. We use highly-multiplexed, super-resolution imaging to study gene regulatory networks in cells and organisms.

Limited deployment of Ark nodes within the current set of unique AS represented within the cluster

Research related to biology and ecology lab at California Polytechnic State University

The 3D Digital Herbarium is an innovative educational platform created by the Cal Poly Humboldt Library dedicated to bringing the intricate world of botany to life through state-of-the-art 3D modeling. At the heart of our mission is the desire to transform how students learn about flora, transcending traditional boundaries by offering an immersive, interactive experience. Our 3D Digital Herbarium is a unique resource, meticulously designed for botany students and enthusiasts alike. It features a diverse collection of flora, each represented in stunning three-dimensional detail. These models offer an unparalleled opportunity to study and appreciate the intricate structures and characteristics of various plant species, providing a level of detail that far surpasses what's available in textbooks or two-dimensional images.

Publications: https://3dherbarium.org/about

To train (fine-tune or query) an open language model with Humboldt (and areas around) data, and institutional datasets to utilize as for chat and other applications. Alignment Data not limited to Digital Commons & Special Collections data sources.

Using remotely sensed data on anthropogenic and environmental features and spatial locations of tagged common ravens to understand their habitat selection along the coast of Humboldt County. Common ravens predate heavily on the nests of the federally listed western snowy plover and are considered to be the main barrier preventing the stabilization of the plover population in Humboldt. This project involves creating focal statistics for large rasters that take upwards of 10 hours per task.

Use of social information for prospecting and nest site selection by Western Bluebirds (Sialia mexicana). Description: In this project, we’re seeking to create a behavioral index for prospecting Western Bluebirds at nest boxes in Napa Valley vineyards. Aim to use video footage of prospecting bluebirds, as well as visiting heterospecifics, to quantify behaviors exhibited in the presence of simulated social information.

James is using Jupyter for instruction. He is teaching several classes using Python in Jupyter notebooks.

Publications: http://gsp.humboldt.edu/JimsProfessional/Publications.html

Bethany is using JupyterHub for instruction. She is teaching several classes using Python in Jupyter notebooks.

Chinook salmon population simulation and modelling in the Klamath river.

Publications: https://fisheries.humboldt.edu/people/nicholas-som-phd

LLM research, butterfly effects of early prompts, cascading design and alignment.

Sea level rise and vulnerability in Humboldt Bay-analyze coastal hazards along highway 101 between Eureka and Arcata CA

Publications: https://sites.google.com/view/coastal-ludka/research/publications?authuser=0

Microglia are a special type of immune cell found only in the central nervous system. These multifaceted cells fight infections, repair damage, remove debris, and are central to maintaining brain health. We develop an automated system to quantify microglia in the brain using computer vision.

Publications: https://sites.google.com/humboldt.edu/kamilalarripa/research?authuser=0

Publications: https://scholar.google.com/citations?user=dE0sTi4AAAAJ&hl=en

Plant toolbox to generate plant lists. The plant lists are for restoration based on climate and soil for a given site.

Publications: https://ffrm.humboldt.edu/people/justin-luong

Reinforcement Learning experiment to improve the training efficiency of an AI model.

Publications: https://www.humboldt.edu/mathematics/rosanna-overholser

Test namespace for Cal Poly Humboldt and learning about the Nautilus Namespaces

This is an exploration into transformer architecture, centered around the idea of sparse attention. We are using the GPT-2 reproduction model. We want to implement a learnable attention mechanism to train with the rest of the model.

Statistical modeling for vertebrate ectotherm conservation

Publications: https://doi.org/10.1038/s41598-023-41677-2

This project seeks to design new DL algorithms for phylogenetics. We have already created a method called DEPP to update an existing tree using embeddings in Euclidean space. We are now working on methods for creating embeddings in hyperbolic spaces and divide-and-conquer methods for inferring species trees using deep learning.

Publications: Jiang, Yueyu, Metin Balaban, Qiyun Zhu, and Siavash Mirarab. “DEPP: Deep Learning Enables Extending Species Trees Using Single Genes.” Edited by Claudia Solis-Lemus. Systematic Biology, April 29, 2022, 2021.01.22.427808. https://doi.org/10.1093/sysbio/syac031. Jiang, Yueyu, Puoya Tabaghi, and Siavash Mirarab. “Phylogenetic Placement Problem: A Hyperbolic Embedding Approach.” In Comparative Genomics, edited by Lingling Jin and Dannie Durand, 68–85. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-031-06220-9_5.

California wave climate hindcast and projections, 1979-2024 + 1979-2014 + 2020-2050

A namespace for the CalState CO Chancellor's Office

Publications: "None"

CAPRI: Causal and Approximate Reasoning and Inference related R&D.

Publications: Stay tuned.

Causal and Probabilistic Reasoning and Inference

Publications: TBD

Causal and Probabilistic Reasoning and Inference

Publications: TBD

Various Spiking Neural Network (SNN) models for studying cognitive functions, such as visual motion perception and spatial memory, that are optimized through evolutionary algorithms.

Publications: Chen, K., Johnson, A., O., S.E., Xinyun, Z., D., D.J.K., A., N.D., and L., K.J. (2021). Differential Spatial Representations in Hippocampal CA1 and Subiculum Emerge in Evolved Spiking Neural Networks. Paper presented at: International Joint Conference on Neural Networks (IJCNN). Xing, J., Nagata, T., Zou, X., Neftci, E., and Krichmar, J.L. (2021). Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation. In Proceedings of the AAAI Conference on Artificial Intelligence, pp. 10452-10459.

CARLA Simulator

Server-based simulation cluster for Carla

The Collaboration for Astronomy Signal Processing and Electronics Research

This namespace is used for testing casper toolflow.

We will use this namespace for training multi-agent perception models for efficient robust collaborative perception

Publications: https://scholar.google.com/citations?user=Pni_ugMAAAAJ&hl=en&oi=ao

Our research focuses on trajectory path prediction for autonomous vehicles and the collaborative perception of the surrounding environment.

Publications: https://scholar.google.com/citations?user=Pni_ugMAAAAJ&hl=en

Exploring Cosmic Ray App (CREDO) image data with Kubernetes Federated AI Technology Enabler (KubeFATE)

Publications: None

This namespace is used for machine learning projects.

Container Data Importer for KubeVirt

The Discovery Program at the College of Computing, Data Science, and Society incubates and accelerates high-impact research in academic, government, non-profit, and industry projects worldwide while providing UC Berkeley students with real-world research experiences and mentorship opportunities. Students gain access to advanced computing resources, data science tools, and collaborative platforms to facilitate their work. Ultimately, the Discovery Program serves as a bridge between academia and real-world applications, fostering an ecosystem where students, mentors, and external partners collaborate to produce transformative research with global impact.

The Discovery Program at the College of Computing, Data Science, and Society incubates and accelerates high-impact research in academic, government, non-profit, and industry projects worldwide while providing UC Berkeley students with real-world research experiences and mentorship opportunities. Students gain access to advanced computing resources, data science tools, and collaborative platforms to facilitate their work. Ultimately, the Discovery Program serves as a bridge between academia and real-world applications, fostering an ecosystem where students, mentors, and external partners collaborate to produce transformative research with global impact.

The Discovery Program bridges academia and real-world applications, fostering an ecosystem where students, mentors, and external partners collaborate to produce transformative research with global impact.

CENIC SDN

cenic-air CENIC AI Resource NAIRR regional services

Namespace for the CENIC development team to experiment with the fine-tuning of open-source LLMs against CENIC data to create specialized chatbot applications for internal use. We intend to export data and fine-tune models against data sources such as Confluence and Jira (Issue summaries and comments). Future goals are to also export and fine-tune against data from Salesforce and various internal application databases.

JetStack cert manager deployment - provides automated certificate generation

cesium 3D Tile Processor

Neural Networks applied to Smart Connected Workers. This is an edge intelligent platform to integrate internet-of-things technologies with computing hardware, software, computational workflows for machine learning, and data ingestion, enabling SMMs to transition into smart manufacturing paradigms by leveraging the intelligence of their people. The platform leverages consumer-grade electronics and sensors (affordable and portable), customized software with open-source software packages (accessible), and existing communication network infrastructures (scalable). The project utilizes Nautilus Kubernetes Cluster. The software systems are implemented via Kubernetes orchestration of Docker containerization to ensure scalability and programmability.

Publications: SCW Journal Publication Part 1: Shijie Bian, Chen Li, Yongwei Fu, Yutian Ren, Tongzi Wu, Guann-Pyng Li, and Bingbing Li*, “Machine Learning-based Real-time Monitoring System for Smart Connected Worker to Improve Energy Efficiency”, Journal of Manufacturing Systems, 2021, Vol. 61: 66-76. https://doi.org/10.1016/j.jmsy.2021.08.009 SCW Journal Publication Part 2: Yoon Kim, Richard Donovan, Yutian Ren, Shijie Bian, Tongzi Wu, Shweta Purawat, Anthony Manzo, Ilkay Altintas, Bingbing Li, and Guann-Pyng Li*, “Smart Connected Worker Edge Platform for Smart Manufacturing: Part 1: Architecture and Platform Design”, Journal of Advanced Manufacturing and Processing, 2022, Vol. 4 (4): e10129. https://doi.org/10.1002/amp2.10129 SCW Journal Publication Part 3: Richard Donovan, Yoon Kim, Anthony J Manzo, Yutian Ren, Shijie Bian, Tongzi Wu, Shweta Purawat, Henry Helvajian, Marilee Wheaton, Bingbing Li, and Guann-Pyng Li*, “Smart Connected Worker Edge Platform for Smart Manufacturing: Part 2: Implementation and On-site Deployment Case Study”, Journal of Advanced Manufacturing and Processing, 2022, Vol. 4 (4): e10130. https://doi.org/10.1002/amp2.10130 SCW Journal Publication Part 4: Chen Li, Shijie Bian, Tongzi Wu, Richard P. Donovan, and Bingbing Li*, “Affordable Artificial Intelligence-Assisted Machine Supervision System for Small and Medium-Sized Manufacturers”, Sensors (JCR2024 Impact Factor: 3.4), 2022, Vol. 22 (16): 6246. https://doi.org/10.3390/s22166246 SCW Journal Publication Part 5: Bingbing Li, Tongzi Wu, Shijie Bian, John W. Sutherland*, “Predictive Model for Real-time Energy Disaggregation Using Long Short-term Memory”. CIRP Annals - Manufacturing Technology, 2023, Vol. 72 (1): 25-28. https://doi.org/10.1016/j.cirp.2023.04.066 SCW Conference Publication Part 1: Shijie Bian, Tiancheng Lin, Chen Li, Yongwei Fu, Mengrui Jiang, Tongzi Wu, Xiyi Hang, and Bingbing Li*, “Real-time Object Detection for Smart Connected Worker in 3D printing”, Proceedings of the 2021 International Conference on Computational Science (ICCS 2021), Krakow, Poland, June 16-18, 2021. https://doi.org/10.1007/978-3-030-77970-2_42

Deep learning approaches for 3D monocular optical & scene flow estimation.

Using structure from motion (SFM), we are able to reconstruct dense 3D point cloud models of coral reefs from photographic surveys. We are leveraging advances in 2D and 3D computer vision to increase the speed and accuracy of the annotation of these models for use in ecological and biological research.

Publications: https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2022.884317/full

remote vis sandbox

modeling chess decision-making via simulations and informational principles

The Chodera lab uses computation and experiment to develop quantitative, multiscale models of the effects of small molecules on biomolecular macromolecules and cellular pathways and understand the functional and therapeutic ramifications of mutations. The group utilizes physical models, rigorous statistical mechanics, and open source software development practices with overall goals of engineering novel therapeutics and tools for chemical biology, predicting resistance or susceptibility to therapy, and understanding the physical driving forces behind the emergence of drug resistance. We develop and use advanced algorithms for molecular dynamics simulations on GPUs and distributed computing platforms, in addition to high-throughput experiments to characterize biophysical interactions between small molecules and their targets.

Publications: "None"

A test namespace in PRP Nautilus.

Intersubject variability among individuals who use opioids is evident, driven by the neuroplastic changes resulting from chronic opioid use. These changes give rise to a range of affective states, including heightened stress, increased pain sensitivity, and the emergence of opioid cravings. Monitoring these affective states is critical to screen and alert at-risk users who are susceptible to developing opioid use disorder. While mobile and wearable sensor devices have demonstrated their ability to model these states broadly, they often fall short in effectively adapting to the nuanced intersubject variability observed in these changes. In response, we propose a hierarchical deep learning approach capable of dynamically identifying optimal user group segmentation for personalized prediction of levels of stress, pain, and craving based on heart rate variability data from a wearable wristband. We trained and evaluated our methods on a dataset collected from 51 subjects and around 2,000 clean samples, each of which contains hours of heart rate variability data and EMA responses. The empirical results show that the hierarchical model structure can significantly boost the performance by leveraging information from the physiological and demographic attributes.

CHRS Namespace

Cihang Xie's lab is working on computer vision and deep learning.

Clabernetes helm deployed NOS simulation framework

Used to host short workshops on research computing with kubernetes and the NRP.

Compute and storage in support of the Climate Analytics Lab, including large-scale inference over satellite imagery and climate model emulation.

Publications: - https://www.pnas.org/doi/full/10.1073/pnas.2206885119 - https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002954 - etc.

While the vulnerability of cycle rickshaw pullers due to extreme heat is well acknowledged, little attention has been paid to developing a Climate-Physiology Model that would inform us how physiology (e.g., skin temperature, cardiac cost, and heart rate variability) will be impacted by climate change in the future under different climate models. In this study, we address this gap by collecting weather and physiological data with a wearable computing platform from a pool of 25 rickshaw pullers from the city of Dhaka in Bangladesh. Employing statistical and regression-based analysis, we establish physiology models that link physiology with physical activity, weather, and season. Furthermore, we integrate three climate models into our analyses to quantify the projected physiological changes from the physiology models in the future.

Advancing climate science through innovative applications and fundamental developments of machine learning research. For example, generating more efficient and accurate weather forecasts and climate projections with skillful uncertainty quantification.

Publications: DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting, Salva Rühling Cachay, Bo Zhao, Hailey Joren, and Rose Yu, Advances in Neural Information Processing Systems (NeurIPS), 2023

Clockwork is the high-frequency time synchronization software alowing to achive sub-millisecond accuracy over long distance

Working towards large attention model development for vision task.

General namespace to run jobs for training various models

Cluster topology system namespace is the deployment for nebula studio graph database to support the traceroute tool

Namespace for regular cms users. Misc use.

Namespace dedicated to CMS services, on CMS hardware. A portion of the SoCal Cache runs here.

CMS ML activities, including machine-learned particle-flow reconstruction, particle graph autoencoders, autoencoders for data compression, model pruning/quantization/compression studies, ML for analysis (Higgs to WW, and LLP jet tagging)

Publications: Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs [https://arxiv.org/abs/2012.01563], MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks [https://arxiv.org/abs/2101.08578], Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics [https://arxiv.org/abs/2012.00173], The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics [https://arxiv.org/abs/2101.08320], Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference [https://arxiv.org/abs/2102.11289], Explaining machine-learned particle-flow reconstruction [https://arxiv.org/abs/2111.12840], Particle Cloud Generation with Message Passing Generative Adversarial Networks [https://arxiv.org/abs/2106.11535], Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance [https://arxiv.org/abs/2111.12849], Machine Learning for Particle Flow Reconstruction at CMS [https://arxiv.org/abs/2203.00330], hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices [https://arxiv.org/abs/2103.05579], A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC [https://arxiv.org/abs/2105.01683], A FAIR and AI-ready Higgs boson decay dataset [https://arxiv.org/abs/2108.02214]

For Higgs bosons -> Vector bosons graph neural network classifier development.

Publications: Search for nonresonant pair production of highly energetic Higgs bosons decaying to bottom quarks [https://arxiv.org/abs/2205.06667], More CMS publications in preparation

CMS users at UCSD.

Coder shifts software development from local machines to on-prem and public cloud infrastructure. Onboard new developers in minutes, build code on powerful servers—all while keeping source code and data secure behind your firewall.

Coder for UCSD and SDSC dev and networking. Similar to the coder namespace but not open.

Publications: None

Setup by Ashton to use for users needing space for developing images, particularly for docker in docker

Nautilus access for bird repopulation research and networking lab research, XAI testing, ACM Research Lab (PINN and medical imagery explainable classification)

Publications: "None"

For use by the students of Cognitive Robotics laboratory under Prof. Henrik I. Christensen.

Working on eye-tracking for image and video compression and its impact

Project Description: We are using a machine learning approach based on a 3D convolution neural network to segment and keep track of the entire life-cycle of earth science phenomena using NASA data.

As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can (1) learn by themselves continually in a self-motivated and self-initiated manner in the open world environment rather than being retrained offline periodically on the initiation of human engineers and (2) accommodate or adapt to unexpected or novel circumstances. As the real-world is an open environment that is full of unknowns or novelties, the ability to detect novelties (out-of-distribution cases), characterize them, accommodate/adapt to them, and gather ground-truth training data and continually or incrementally learn the unknowns/novelties is becoming critical in making the AI agent more and more knowledgeable, powerful, and self-sustainable over time. Our lab works on these challenging problems.

As Convolutional Neural Networks (CNNs) have become deeper and/or wider to achieve higher accuracies, the number of parameters associated with their convolutional layers has become much more significant. In this research project, we design general and straightforward methods for significantly reducing the number of parameters of convolutional layers of CNNs, during both the training and inference phases, without compromising the accuracy, training time or inference time.

Publications: Associative Convolutional Layers - https://arxiv.org/abs/1906.04309

Experiments on algorithms related to cooperative perception.

Elucidating the underlying mechanisms of coral adaptation to a changing ocean through genetic sequencing

Global and local stressors have caused a rapid decline of coral reefs across the world. To monitor the changes and take appropriate action large spatio-temporal surveys are needed. Data collection speeds are typically sufficient to meet this need but the subsequent image analysis remains slow as manual inspection of each photo is required. This creates a 'manual annotation bottleneck'. CoralNet reduces this bottleneck by allowing modern computer vision algorithms to be deployed alongside human experts. Often 50-100% automation can be achieved with minimal reduction in the quality of the final data-product. CoralNet, by its nature, also provides a platform for collaboration & sharing of data.

Familiarize faculty/student with the features and resources available on the national research platform, including how to access and navigate the platform using their institutional accounts. Enable faculty to utilize JupyterLab through the platform, guiding them on the process of creating and executing Jupyter notebooks for their research projects. Introduce faculty to containerization technology and guide them through the steps of creating and launching containers on the national research platform to support their research workflows.

Familiarize faculty/student with the features and resources available on the national research platform, including how to access and navigate the platform using their institutional accounts. Enable faculty to utilize JupyterLab through the platform, guiding them on the process of creating and executing Jupyter notebooks for their research projects. Introduce faculty to containerization technology and guide them through the steps of creating and launching containers on the national research platform to support their research workflows.

Familiarize faculty/student with the features and resources available on the national research platform, including how to access and navigate the platform using their institutional accounts. Enable faculty to utilize JupyterLab through the platform, guiding them on the process of creating and executing Jupyter notebooks for their research projects. Introduce faculty to containerization technology and guide them through the steps of creating and launching containers on the national research platform to support their research workflows.

Familiarize faculty/student with the features and resources available on the national research platform, including how to access and navigate the platform using their institutional accounts. Enable faculty to utilize JupyterLab through the platform, guiding them on the process of creating and executing Jupyter notebooks for their research projects. Introduce faculty to containerization technology and guide them through the steps of creating and launching containers on the national research platform to support their research workflows.

Coturn installation - the server passing through WebRTC audio and video traffic

The Creative Coding Lab in the Computational Media Department at the University of California Santa Cruz is a team of interdisciplinary researchers and artists focused on applied research in interaction and visualization, and also on the exploration of experimental and creative works based on current techniques in human-computer interaction, scientific and information visualization, graphics, computer vision, immersive environments, audio and machine learning. A core philosophy of UCSC Creative Coding is that by incorporating research methodologies from media arts, design, and computer science, we can develop novel solutions to interdisciplinary problems. Moreover, we believe that the creative outputs generated at the intersections of artistic and empirical research can meaningfully elucidate issues in science and technology relevant to contemporary culture. Check out our website for more information about our lab and ongoing projects: https://creativecoding.soe.ucsc.edu/news.php

Publications: D. Abramov, J. N. Burchett, O. Elek, C. Hummels, J. X. Prochaska and A. G. Forbes, "CosmoVis: An Interactive Visual Analysis Tool for Exploring Hydrodynamic Cosmological Simulations," in IEEE Transactions on Visualization and Computer Graphics, doi: 10.1109/TVCG.2022.3159630.

Juniper CRPD development environment container deployment

CRXEL focuses on grassroots creativity and learning while promoting research that combines artificial intelligence with interactive and participatory media to allow people to be more creative, informed and make better decisions in educational as well as entertainment environments

Research on the propagation of birds. Joint research between Computer Science and Ecology.

Research and teaching for Culver-Stockton College in Canton, MO

Publications: Setting Up Research Jobs in Nautilus Kubernetes Hyper-Cluster: https://github.com/scottg

Research and teaching for Culver-Stockton College in Canton, MO

Publications: Setting Up Research Jobs in Nautilus Kubernetes Hyper-Cluster: https://github.com/scottg

This is a namespace for analyzing the energy footprint of executing compute jobs. It is a part of the CSE 290-N Fall 2024 Class at UCSC/CSE.

Publications: https://asouza.io/publications

CSE 290N namespace for Ryia to test her project proposal

Publications: https://asouza.io

This is a namespace for analyzing the energy footprint of executing compute jobs. It is a part of the CSE 297 Fall 2024 Class at UCSC/CSE.

Publications: https://asouza.io/publications

Building tools, training, and patterns for Computational Social Science

The Technology Infrastructure for Data Exploration (TIDE) is designed to support cutting-edge research in machine learning and AI. The computing architecture is based on powerful graphics, high-performance processors, and ample storage to support research discovery. This JupyterHub instance acts as the front door for CSU researchers to the TIDE cluster.

This is the CSU TIDE Jupyterhub development environment

Biochemistry at California State University Channel Islands. This project space is for student-faculty teams performing molecular dynamics simulations to support on-campus experimental research of drug candidates for pancreatic cancer treatment.

All things machine learning (ML) at California State University Channel Islands. Includes faculty-student teams for research projects in various sciences (such as ML for laser-plasma physics), and students in the A.I. Club who are looking to dig deeper into the topic.

This namespace is designed as a specialized testing environment dedicated to facilitating the exploration of the Nautilus framework. Its primary purpose is to provide comprehensive support to campuses within the California State University (CSU) system, assisting them in the effective implementation of a Science DMZ architecture on the California Research and Education Network (CENIC) High-Performance Research network.

Focus on providing a proof of concept for the CSUF account course.

Testing Hadoop with clustering for CPSC 531 - Advanced Databases

Focus on testing new images and new concepts for windows vm.

California State University Fullerton faculty sandbox environment.

Research LLM Training to determine efficacy for Library Chatbot purposes, serve as a baseline for assessing provided alternatives.

Focus on providing a proof of concept CSUF teaching and learning space.

Focus on providing a proof of concept CSUF teaching and learning space.

Focus on providing a proof of concept CSUF teaching and learning space.

Namespace for John Wagenleitner for Nautlius testing to be able to support other researchers at our University.

This cluster was created for testing purposes to see how to support our research staff with NRP.

This is a test namespace because I can't get the NRP/Namespace Portal to provide a "config" file for anything other than the first namespace in the list (so I forced a namespace to be the *first* in the list.

My research lab is focused on developing and using computational methods to probe how protein structure and biochemical (protein-ligand and protein-protein) interactions of G protein-coupled receptors (GPCRs) determine cellular signaling and physiology, as well as how this knowledge can be used for the rational design of drugs targeting GPCR signaling pathways.

Publications: https://www.csun.edu/science-mathematics/chemistry-biochemistry/ravinder-abrol

This is an undergraduate student in biochemistry in Prof. Ravi Abrol's lab. She is doing sophisticated research in protein combinatorics (molecular simulations).

California State University Northridge’s (CSUN) Autonomy Research Center for STEAHM (ARCS) is a NASA-sponsored, chartered, Center of Excellence. Our mission is to combine transdisciplinary, university-wide knowledge and talent from faculty, students, and NASA scientists to conduct convergence research and collaboration using increasingly autonomous systems (IA).

Publications: Journal Publication Part 1: Shijie Bian, Daniele Grandi, Tianyang Liu, Pradeep K. Jayaraman, Karl Willis, Elliot Sadler, Bodia Borijin, Thomas Lu, Richard Otis, Nhut Ho, and Bingbing Li*, “HG-CAD: Hierarchical Graph Learning for Material Prediction and Recommendation in CAD”, Journal of Computing and Information Science in Engineering, 2024, Vol. 24 (1): 011007. https://doi.org/10.1115/1.4063226 Conference Publication Part 1: Shijie Bian, Daniele Grandi, Kaveh Hassani, Elliot Sadler, Bodia Borijin, Axel Fernandes, Andrew Wang, Thomas Lu, Richard Otis, Nhut Ho, Bingbing Li*, “Material Prediction for Design Automation Using Graph Representation Learning”, Proceedings of the ASME 2022 International Design Engineering Technical Conferences &Computers and Information in Engineering Conference (IDETC/CIE 2022), St. Louis, Missouri, U.S.A., August 14-17, 2022. https://doi.org/10.1115/DETC2022-88049 Conference Publication Part 2: Anthony Morales-Badajoz, Neville Elieh, April Diederich, Elliot Sadler, Jasmine Glover, Manoj Nizampatnam, Troy Israel, Andrew Wang, Larry Zhang, Annette Besnilian, Andreas George, Julie Miller, Xunfei Jiang, Bingbing Li*, “Astro Cultivators: Autonomous Growth System for Space Farming based on Machine Vision and Multi-Sensor Fusion”, Proceedings of ACM Cyber-Physical Systems and Internet of Things Week 2023 (CPS-IoT Week Workshops '23), San Antonio, Texas USA, May 9-12, 2023. https://doi.org/10.1145/3576914.3588338

We are mainly focused on developing deep learning models for the following image processing tasks. (1) Image denoising. Convoluion neural networks , vision transformers, and the hybrid models are investigated for image denoising. (2) Image reconstruction. Convoluion neural networks , vision transformers, and the hybrid models are investigated for CT and MRI image reconstruction.

Publications: “Real-time Object Detection for Smart Connected Worker in 3D printing” by Shijie Bian, Tiancheng Lin, Chen Li, Yongwei Fu, Mengrui Jiang, Tongzi Wu, Xiyi Hang, Bingbing Li*. Proceedings of the 2021 International Conference on Computational Science (ICCS 2021), Krakow, Poland, June 16-18, 2021.

Test out a few Natural Language Models Lamda2 and open source models (conjoint analysis, etc.)

We are mainly focused on developing deep learning models for the following image processing tasks. (1) Image denoising. Convoluion neural networks , vision transformers, and the hybrid models are investigated for image denoising. (2) Image reconstruction. Convoluion neural networks , vision transformers, and the hybrid models are investigated for CT and MRI image reconstruction.

Publications: Real-time Object Detection for Smart Connected Worker in 3D printing” by Shijie Bian, Tiancheng Lin, Chen Li, Yongwei Fu, Mengrui Jiang, Tongzi Wu, Xiyi Hang, Bingbing Li*. Proceedings of the 2021 International Conference on Computational Science (ICCS 2021), Krakow, Poland, June 16-18, 2021.

XACC Quantum emulation--https://xacc.readthedocs.io/en/latest/install.html

Dr. Ravi Abrol’s research lab is focused on developing and using computational methods to probe how protein structure and biochemical (protein-ligand and protein-protein) interactions of G protein-coupled receptors (GPCRs) determine cellular signaling and physiology, as well as how this knowledge can be used for the rational design of drugs targeting GPCR signaling pathways.

Publications: https://www.csun.edu/science-mathematics/chemistry-biochemistry/ravinder-abrol

testing purposes for Wayne Smith to help Zack Hillbruner and others on campus use NRP/Nautilus

This user wants to learn Linux and related data science software in a sandbox environment

CSUN--Engineering and Computer Science--CS faculty

California State University, Northridge - ECS-IT-TEST

Zack Hillbruner will be the longer-term contact for High-Performance Computing resources at California State University, Northridge. He works in the Central IT group. He works with others, including Wayne Smith.

This namespace is dedicated to AI research and training at CSUSB

JupyterHub for Dr. Jeremy Aikin at Cal State San Bernardino

xREAL is an interdisciplinary technology innovation hub that brings together faculty, students, staff, and industry partners and uses a variety of leading-edge technologies to design and develop immersive learning experiences that advance the scholarship of teaching and learning and have demonstrable pedagogical benefits. Our mission is to transform teaching and learning with leading-edge technologies by researching and designing surprising human-machine interactions that encourage the joy of discovery, provide educational insights, and contribute to the public good. Our mission is to transform teaching and learning with leading-edge technologies by researching and designing surprising human-machine interactions that encourage the joy of discovery, provide educational insights, and contribute to the public good.

Publications: https://www.csusb.edu/academic-technology-innovation/xreal-lab

The Vienna Ab initio Simulation Package (VASP) is a computer program for atomic scale materials modeling, e.g. electronic structure calculations and quantum-mechanical molecular dynamics, from first principles. https://www.vasp.at/index.php/about-vasp/59-about-vasp

Publications: "None"

This namespace is for Dr. Kimberley Cousins at Cal State San Bernardino

This project utilizes electronic health records (EHR) mostly available from public datasets, such as Agency for Healthcare Research and Quality's (AHRQ) Healthcare Cost and Utilization Project (HCUP) or California Office of Statewide Health Planning and Development (OSHPD) to uncover predictive patterns in patient or hospital level health-related outcomes.

This namespace is for TIDE grad assistant to develop and deploy software

The namespace will be used for Dr. Hamouda's F2022 course, IST 6110 & 6620 Workshop Research at Cal State San Bernardino.

Publications: "None"

This namespace is for JupyterHub dedicated to faculty research at California State University, San Bernardino

Publications: "None"

This namespace is for JupyterHub and stack developments and collaboration with other California State Universities

dedicated to AI supported Entrepreneurship research

This name space is dedicated to Prof. Jennifer Jin and her students in ML/AI research

This namespace is for JupyterHub dedicated to the members of California State University, San Bernardino for classroom and research

Publications: "None"

Professor Khan's research lab in AI/ML and computer vision

JupyterHub for Spring 2025 classes for Youngsu Kim

This project utilizes electronic health records (EHR) mostly available from public datasets, such as the Agency for Healthcare Research and Quality's (AHRQ) Healthcare Cost and Utilization Project (HCUP) or California Office of Statewide Health Planning and Development (OSHPD) to uncover predictive patterns in patient or hospital level health-related outcomes.

Publications: https://www.csusb.edu/academic-technologies-innovation/xreal-lab-and-high-performance-computing/high-performance-computing/projects

This namespace is for JupyterHub that will be used for CSUSB BIOL-5050, Dr. Bree Putman, in Fall 2023

This namespace is dedicated to research Information & Decision Sciences Department and collaborations with other institutions

for Professor Qiuxiao Chen's research in computer vision

The namespace is for Summer 2023 research at CSUSB

The Vienna Ab initio Simulation Package (VASP) is a computer program for atomic scale materials modelling, e.g. electronic structure calculations and quantum-mechanical molecular dynamics, from first principles. https://www.vasp.at/index.php/about-vasp/59-about-vasp

Publications: Functional materials are extended solids that respond to external stimuli such as electric or magnetic fields, light, or heat, by changing structure/polarity/magnetic anisotropy, and which may be tuned at the molecular level. The Center for Advanced Functional Materials at CSUSB, funded in part by an NSF-CREST grant (NSF 1914777) seeks to develop, discover, and apply new functional materials in crystalline, thin film, and polymeric forms. Dr. Cousins, and new faculty member Dr. Joyce Pham, contribute to this endeavor computationally using density functional theory, and molecular dynamics will be used to describe and study materials at the molecular level. A fundamental question that a computational investigation may help answer is “what gives rise to the observed structures,” which thus provides an avenue to understand and better design a material with desired properties. The insight gained from computation helps us optimize existing materials and predict new materials for study by our experimental colleagues. 1. Kimberley Cousins & Sarah Rodriguez* “Materials genome approach to functional materials discovery using the CSD” at the One Million Crystal Structures Symposium (oral presentation), American Chemical Society Fall 2019 National Meeting, San Diego, CA, August 26, 2019. Re-reported in a white paper by Wendy Warr, November, 2019. 2. Timothy Usher, Kimberley Cousins, Douglas Smith, Renwu Zhang, Eva Zurek, Stephen Ducharme, Sara Callori, Daniel Miller**, Paulo Costa**. “Materials Genome Approach to Organic Ferroelectrics and Piezoelectrics.” Int. J. Nanotechnol., Vol. 15, Nos. 8/9/10, 2018. 3. Bindi, L.; Pham, J.; Steinhardt, P.J. “Previously Unknown Quasicrystal Periodic Approximant Found in Space.” Scientific Reports 2018 (8), 16271, 1–7. 4. Pham, J.; Miller, G.J. “AAuAl (A = Ca, Sc, and Ti): Peierls Distortion, Atomic Coloring, and Structural Competition.” Inorganic Chemistry 2018 57 (7), 4039–4049. (Open access link via Ames Lab accepted manuscript digital repository) 5. Miller, G.J.; Pham. J.; Smetana, V.; Xie, W. “Unraveling Complex Intermetallics Using a Cluster Perspective.” Springer-Nature: Chapter Contribution to Commemorate Ken Wade’s 50-Year Anniversary Publication on the ‘Wade’s Rules’. Available 2021.

xREAL is an interdisciplinary technology innovation hub that brings together faculty, students, staff, and industry partners and uses a variety of leading-edge technologies to design and develop immersive learning experiences that advance the scholarship of teaching and learning and have demonstrable pedagogical benefits.

Publications: https://www.csusb.edu/academic-technologies-innovation/xreal-lab-and-innovation

For f2024 math2265 at California State University San Bernardino

Advanced AI Solutions for Healthcare and Decision Support for Prof. Yan Zhang

cvmfs driver

A Helm chart for the CVMFS-CSI driver, allowing the mounting of CVMFS repositories in Kubernetes environments. This chart will deploy the CSI driver as a DaemonSet, thus automatically scaling the driver on each cluster node.

This namespace is used for Kubernetes training purposes.

this is for CXL deivce application basad profiling, test, and managment.

This projects end goal is to create an immersive Cyber-Archaeologists' Warehouse inside UE5 for analysis and study of digitized assets from field excavations and survey. We see this as the next step to digitization, analysis and collaboration that allows us to begin building out the metaverse of the archaeological past.

Research on cyber policy issues and AI. We intend to explore a patent AI dataset wich more than 12 million rows. The idea is to look at the distribution of patents in the US related to AI and the changes in policy during hypes in technologies like ChatGPT

We investigate the output distributions of transformer implementations such as GPT-2 and BERT, with interest in applying them to both supervised and reinforcement learning domains including path planning, computer vision and natural language grounding.

To tune and optimize the OSG nodes for data transfer

Testing sandbox namespace for k8s interaction. Testing sandbox namespace for k8s interaction.

Testing containerized workload on the NRP and see how various resources can be utilized within a Kubernetes cluster. This will help us gain insight on the cluster design and management for future projects.

Dask operator provides the management for dask clusters in kubernetes

end to end testing of LIGO's data analysis workflow.

Publications: None for specific project

Repo for Ashton to play around with different data visualizations for Nautilus

This project aims to develop a machine learning model capable of detecting and categorizing defects in printed circuit boards (PCBs). The model will work with high-resolution images of the MAO and SCI4 boards to identify if a given fault is a "killer" defect or a benign one. Two primary approaches are in consideration: a supervised model trained on a limited set of both defect types and an image segmentation model to detect abnormalities relative to critical components.

Analysis of longitudinal continuous glucose monitoring data.

The goal of this project is to provide a system for storing and retrieving huge amounts of data, distributed among a large number of heterogenous server nodes, under a single virtual filesystem tree with a variety of standard access methods.

Design and testing of machine learning approaches to translate classical and academic Chinese into academic English.

Combining Simulated and Real Data for Near-Term Forecasting of Nonstationary Dynamic Processes, with applications to Traffic and COVID-19 forecasting. Developing hybrid physics-guided deep learning tools to forecast non-stationary, non-linear dynamics

Publications: Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems Rui Wang, Danielle Maddix, Christos Faloutsos, Yuyang Wang, Rose Yu Annual Conference on Learning for Dynamics and Control (L4DC), 2021 Traffic Forecasting using Vehicle-to-Vehicle Communication Steven Wong, Lejun Jiang, Robin Walters, Tamás G. Molnár, Gábor Orosz, Rose Yu Annual Conference on Learning for Dynamics and Control (L4DC), 2021

This namespace is created for research work under Rose group (deep-forecast) for point-process related research

Publications: [1] Zhou, Z. & Yu, R., Automatic Integration for Fast and Interpretable Neural Point Processes., Learning for Dynamics and Control (L4DC), 2023 [2] Zhou, Z., Yang, X., He, X., Rossi, R., Zhao, H., & Yu, R., Neural Point Process for Learning Spatiotemporal Event Dynamics., Learning for Dynamics and Control (L4DC), 2022

2023 DSE260 Capstone Project: Fire Simulation Data Analysis

Deep Synesthesia

Deep learning on epigenetics and gene regulation

The Feltus lab is running deep learning oncogenomics workflows on the Pacific Research Platform Kubernetes (K8s) cluster. The PRP K8s is allowing us to scale up our analyses by moving large genomics datasets between FIONA nodes and then screening tens of thousands of genes on GPUs for tumor biomarker discovery.

Training deep learning models for analysis of large-scale structure in the spatial distribution of galaxies

default kubernetes namespace, not used

Design Reasoning Lab Department of Computational Media UC Santa Cruz https://designreasoning.org/ (updated 14 March 2023)

Publications: (updated 14 March 2023)

ic design tools (Cadence / Synopsys), hardware simulations

Publications: Arpan Dutta, et al.,”HDnn PIM: Efficient in Memory Design of Hyperdimensional Computing with Feature Extraction,” GLVLSI’22. Minxuan Zhou, et al., “TransPIM: A Memory-based Acceleration via Software-Hardware Co-Design for Transformers," HPCA'22.

Used by David Parks - dfparks@ucsc.edu - University of California Santa Cruz

CRISPS cell centric image similarity projection tool for visualization namespace for collaborations with ANS

This research focuses on integrating controllable diffusion models into traffic simulation. The project aims to develop a model capable of simulating diverse traffic scenarios by incorporating real-world data like vehicle density and road conditions. A significant aspect is the dynamic control of model parameters, enabling the simulation of various traffic situations, such as peak-hour congestion and emergency scenarios. The goal is to improve the capabilities of traffic simulations for applications in autonomous vehicle development.

Digester adds the image digest to all deployed pods. More info at https://github.com/XenitAB/spegel/blob/main/docs/FAQ.md

Smoke Detection (formerly NVIDIA DIGITS) - Wildfire smoke detection from images using deep learning

Publications: A. Dewangan, Y. Pande, H.-W. Braun, F. Vernon, I. Perez, I. Altintas, G. Cottrell, and M. H. Nguyen. "FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection." Remote Sensing 14, no. 4 (2022): 1007. https://www.mdpi.com/2072- 4292/14/4/1007 A. Dewangan, Y. Pande, H.-W. Braun, F. Vernon, I. Perez, I. Altintas, G. Cottrell, and M. H. Nguyen. “FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection,” in NeurIPS Workshop on Tackling Climate Change with Machine Learning, 2021. https://www.climatechange.ai/papers/neurips2021/26

Test namespace for administrative stuff, update test

The project aims to accelerate the training of Deep Neural Networks on large datasets by parallelizing training across multiple GPUs. Initial target is a motion transfer application but the framework can be used to achieve significant speedups with other architectures and datasets.

Publications: A Manifold Learning Based Video Prediction Approach for Deep Motion Transfer International Conference on Computer Vision (ICCV) Workshops, 2021 Yuliang Cai, Sumit Mohan, Adithya Niranjan, Nilesh Jain, Alex Cloninger, Srinjoy Das

The overarching goal of our research is to effectively and efficiently discover knowledge from large amounts of digital data. In particular, we are interested in extracting information for knowledge graph construction; modeling hierarchical structures for natural language understanding; learning robust and accurate models in low resource settings.

Nvidia DOCA dpu-devops-kit for Bluefield2 DPU Ansible automation

Domain Adaptation: We are investigating methods for transfer learning and multi-task learning. The target is to use a well pre-trained model for other tasks with or without supervision and build the bridge between different domains. Currently, we focus on the problem about unsupervised domain adaptation for semantic segmentation and multi-domain learning for several classification datasets.

Publications: Bidirectional Learning for Domain Adaptation of Semantic Segmentation

This is a namespace dedicated to health data donated by individuals to the Smarr Lab at UCSD.

Post SC23 tutorial testing using a large shared Kubernetes cluster. We are currently running Kubernetes for specific projects but nothing general purpose at the moment.

drawio

Publications: NA

Kubernettes testing and deployment for DISE. We will use this as a Dev platform

This is a namespace for testing and deployment of jupyterhubs for serving reproducible scientific products with computing resources.

Namespace for Rankin lab used for NN model training and studies as well as tests of FPGA acceleration

Publications: https://arxiv.org/abs/2010.08556

dtnaas is the namespace for experiments of DTNaaS on prp platform. The solution is to support Data Transfer Node (DTN) services at network exchange for various types of data-intensive science workflows.

This is the namespace for Duke MLM Group. Here is the test name space to test the use of cluster and naive run of the code

Namespace for Dustin Wang for test system, like Transformers

Analyzing longitudinal blood-glucose levels from individuals with type 2 diabetes.

We integrate genomics and deep learning for ecDNA-driven cancer diagnostics.

Publications: - Chowdhry, S. et al. NAD metabolic dependency in cancer is shaped by gene amplification and enhancer remodeling. Nature (2019) - Rajkumar, U. et al. ecSeg: Semantic Segmentation of Metaphase Images containing Extrachromosomal DNA. iScience. (2019) - Turner, K. et al. Circular extrachromosomal DNA drives massive oncogene expression and chromatin remodeling. Nature. (2019) - Luebeck, J. et al AmpliconReconstructor: Integrated analysis of NGS and optical mapping resolves the complex structures of focal amplifications in cancer. bioRxiv. (2020) - Kim, H. et al. Frequent extrachromosomal oncogene amplification drives aggressive tumors, Nature Genetics (2020) - Lange et al., Principles of ecDNA random inheritance drive rapid genome change and therapy resistance in human cancers, Nature Genetics (2022) - Hung et al., EcDNA hubs drive cooperative intermolecular oncogene expression, Nature (2021)

We are working on designing ML based encoder and decoder for a communication system

Signal Transmission and Recording (STAR) group project at the Center for Memory and Recording Research (CMRR). In this project, we explore several topics in machine learning: 1) The application of machine learning to device optimization and failure prediction in storage devices. 2) The design and evaluation of neural-assisted algorithms to enhance the performance of digital communication and storage systems. 3) The sensitivity of machine learning models to errors in the model parameters, and the application of error correction coding techniques to provide robustness to such errors. 4) Generative models for simulating signals and sequences produced by magnetic recording and non-volatile memory devices.

With continued growth in the demands on the wireless spectrum for wireless communication, spectrum policies are evolving at a pace far more rapid than ever before. Central to efforts of spectrum modernization is a critical need to accurately measure spectrum activities across diverse, wide bands and across wide areas in a cost-effective and accurate manner, so that impacts of such changes can be carefully evaluated and acted upon in a data-driven manner. The focus of this project, SpecScape, is to design, implement, deploy, and make available low-cost kits that allow spectrum sensing and measurement. In particular, the team is building an end-to-end infrastructure that includes mobile sensors to measure spectrum activity, a supporting software ecosystem, a cloud-hosted infrastructure to manage collected measurements, and mechanisms by which users can access such information. The most significant broader impact of this project is that it provides a community-driven way to understand spectrum use across different spectrum bands -- across communications, astronomy, weather prediction, localization systems, etc. This information will aid researchers, industry practitioners, and governmental agencies, including policymakers. On the educational side, the team is involved in creating a hands-on wireless curriculum for undergraduates across multiple institutions (UW and UCSD), engaging undergraduates in research-related activities, and creating online courseware based on the spectrum sensing platform. The project also engages a broad audience through multiple dissemination channels of research outcomes and aims to encourage women and minority students to pursue STEM careers through opportunities in research activities.

Publications: [1] Bansal K, Rungta K, Bharadia D. RadSegNet: A Reliable Approach to Radar Camera Fusion. arXiv preprint arXiv:2208.03849. 2022 Aug 8. [2] Dureppagari HK, Dinesha U, Wu R, Ganji S, Ko WH, Shakkottai S, Bharadia D. Realtime intelligent control for NextG cellular radio access networks. InProceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services 2022 Jun 27 (pp. 567-568). [3] Givehchian H, Bhaskar N, Herrera ER, Soto HR, Dameff C, Bharadia D, Schulman A. Evaluating Physical-Layer BLE Location Tracking Attacks on Mobile Devices. In2022 IEEE Symposium on Security and Privacy (SP) 2022 May 22 (pp. 1690-1704). IEEE. [4] Arun A, Ayyalasomayajula R, Hunter W, Bharadia D. P2SLAM: Bearing Based WiFi SLAM for Indoor Robots. IEEE Robotics and Automation Letters. 2022 Jan 25;7(2):3326-33. [5] Wu Y, Ayyalasomayajula R, Bianco MJ, Bharadia D, Gerstoft P. Sound source localization based on multi-task learning and image translation network. The Journal of the Acoustical Society of America. 2021 Nov 5;150(5):3374-86. [6] Zhao M, Chang T, Arun A, Ayyalasomayajula R, Zhang C, Bharadia D. ULoc: Low-power, scalable and cm-accurate UWB-tag localization and tracking for indoor applications. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2021 Sep 14;5(3):1-31. [7] Wu Y, Ayyalasomayajula R, Bianco MJ, Bharadia D, Gerstoft P. Sslide: Sound source localization for indoors based on deep learning. InICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021 Jun 6 (pp. 4680-4684). IEEE.

Machine Learning for Physical Layer Communication, Sensing, and Inverse Problems.

This is my research group's work on AI-enabled Optimization.

Research into Trojan ML attacks

With continued growth in the demands on the wireless spectrum for wireless communication, spectrum policies are evolving at a pace far more rapid than ever before. Central to efforts of spectrum modernization is a critical need to accurately measure spectrum activities across diverse, wide bands and across wide areas in a cost-effective and accurate manner, so that impacts of such changes can be carefully evaluated and acted upon in a data-driven manner. The focus of this project, SpecScape, is to design, implement, deploy, and make available low-cost kits that allow spectrum sensing and measurement. In particular, the team is building an end-to-end infrastructure that includes mobile sensors to measure spectrum activity, a supporting software ecosystem, a cloud-hosted infrastructure to manage collected measurements, and mechanisms by which users can access such information.

With continued growth in the demands on the wireless spectrum for wireless communication, spectrum policies are evolving at a pace far more rapid than ever before. Central to efforts of spectrum modernization is a critical need to accurately measure spectrum activities across diverse, wide bands and across wide areas in a cost-effective and accurate manner, so that impacts of such changes can be carefully evaluated and acted upon in a data-driven manner. The focus of this project, SpecScape, is to design, implement, deploy, and make available low-cost kits that allow spectrum sensing and measurement. In particular, the team is building an end-to-end infrastructure that includes mobile sensors to measure spectrum activity, a supporting software ecosystem, a cloud-hosted infrastructure to manage collected measurements, and mechanisms by which users can access such information.

study on 3d presentation with self-supervision and for robotics. We will learn 3D representations from videos and use it for robotic manipulation tasks.

Publications: Rishabh Jangir*, Nicklas Hansen*, Sambaran Ghosal, Mohit Jain, Xiaolong Wang. Look Closer: Bridging Egocentric and Third-Person Views with Transformers for Robotic Manipulation. Robotics and Automation Letters (RA-L), 2022. Zihang Lai, Sifei Liu, Alexei A. Efros, Xiaolong Wang. Video Autoencoder: self-supervised disentanglement of static 3D structure and motion. International Conference on Computer Vision (ICCV), 2021 (Oral Presentation).

5G communication stack and networking development. These students will use high-performance GPUs to accelerate 5G data processing and set up standard-compliant nodes for performance testing and research.

Publications: Jain, Ish Kumar, Raghav Subbaraman, Tejas Harekrishna Sadarahalli, Xiangwei Shao, Hou-Wei Lin, and Dinesh Bharadia. "mMobile: Building a mmWave Testbed to Evaluate and Address Mobility Effects." In Proceedings of the 4th ACM Workshop on Millimeter-Wave Networks and Sensing Systems, pp. 1-6. 2020. [1] Bansal K, Rungta K, Bharadia D. RadSegNet: A Reliable Approach to Radar Camera Fusion. arXiv preprint arXiv:2208.03849. 2022 Aug 8. [2] Dureppagari HK, Dinesha U, Wu R, Ganji S, Ko WH, Shakkottai S, Bharadia D. Realtime intelligent control for NextG cellular radio access networks. InProceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services 2022 Jun 27 (pp. 567-568). [3] Givehchian H, Bhaskar N, Herrera ER, Soto HR, Dameff C, Bharadia D, Schulman A. Evaluating Physical-Layer BLE Location Tracking Attacks on Mobile Devices. In2022 IEEE Symposium on Security and Privacy (SP) 2022 May 22 (pp. 1690-1704). IEEE. [4] Arun A, Ayyalasomayajula R, Hunter W, Bharadia D. P2SLAM: Bearing Based WiFi SLAM for Indoor Robots. IEEE Robotics and Automation Letters. 2022 Jan 25;7(2):3326-33. [5] Wu Y, Ayyalasomayajula R, Bianco MJ, Bharadia D, Gerstoft P. Sound source localization based on multi-task learning and image translation network. The Journal of the Acoustical Society of America. 2021 Nov 5;150(5):3374-86. [6] Zhao M, Chang T, Arun A, Ayyalasomayajula R, Zhang C, Bharadia D. ULoc: Low-power, scalable and cm-accurate UWB-tag localization and tracking for indoor applications. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2021 Sep 14;5(3):1-31. [7] Wu Y, Ayyalasomayajula R, Bianco MJ, Bharadia D, Gerstoft P. Sslide: Sound source localization for indoors based on deep learning. InICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021 Jun 6 (pp. 4680-4684). IEEE.

In healthcare, due to data privacy and security issues, it is difficult to obtain a large amount of training data. Machine learning models, especially deep learning models, typically have a lot of weight parameters. Training large-sized models on small datasets can easily lead to overfitting, meaning that the models perform well on training data but generalize poorly on unseen test data. To address this problem, I and my students have been developing sample-efficient ML methods which can train highly- performant models on small-sized medical data. While current progress in AI for healthcare is encouraging, not too many clinical AI solutions are deployed in hospitals or actively utilized by physicians. A major problem is that existing clinical AI methods are less trustworthy. For example, existing approaches make clinical decisions in a black-box way, which renders the decisions difficult to understand and less transparent. Existing solutions are not robust to small perturbations or potentially adversarial attacks, which raises security and privacy concerns. As a result, physicians are reluctant to use these solutions since clinical decisions are mission- critical and must be made with high trust and reliability. To address these problems, I and my students have been developing trustworthy ML methods for healthcare, which are interpretable and robust against adversarial attacks.

Publications: Yijian Qin, Xin Wang, Ziwei Zhang, Pengtao Xie, Wenwu Zhu. Graph Neural Architecture Search Under Distribution Shifts. International Conference on Machine Learning (ICML), 2022. Pengtao Xie and Xuefeng Du. Performance-Aware Mutual Knowledge Distillation for Improving Neural Architecture Search. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. Yuren Mao, Zekai Wang, Weiwei Liu, Xuemin Lin, and Pengtao Xie. MetaWeighting: Learning to Weight Tasks in Multi-Task Text Classification. The 60th Annual Meeting of the Association for Computational Linguistics (ACL), Findings, 2022. Youwei Liang, Chongjian Ge, Zhan Tong, Yibing Song, Jue Wang, and Pengtao Xie. EViT: Expediting Vision Transformers via Token Reorganizations. International Conference on Learning Representations (ICLR), 2022. (Spotlight Presentation) Sai Ashish Somayajula, Linfeng Song and Pengtao Xie. A Multi-Level Optimization Framework for End-to-End Text Augmentation. Transactions of the Association for Computational Linguistics (TACL), 2022. Bhanu Garg, Li Zhang, Pradyumna Sridhara, Ramtin Hosseini, Eric Xing, and Pengtao Xie. Learning from Mistakes -- A Framework for Improving Neural Architecture Search. AAAI Conference on Artificial Intelligence (AAAI), 2022. Pengtao Xie, Jun Zhu, and Eric P. Xing. Diversity-promoting Bayesian Learning of Latent Variable Models. Conditionally accepted by the Journal of Machine Learning Research (JMLR). Jiayuan Huang, Yangkai Du, Shuting Tao, Kun Xu, and Pengtao Xie. Structured Self-Supervised Pretraining for Commonsense Knowledge Graph Completion. Transactions of the Association for Computational Linguistics (TACL), 2021. Xuehai He, Zhuo Cai, Wenlan Wei, Yichen Zhang, Luntian Mou, Eric Xing and Pengtao Xie. Towards Visual Question Answering on Pathology Images. The 59th Annual Meeting of the Association for Computational Linguistics (ACL), 2021. Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing and Pengtao Xie. On the Generation of Medical Dialogs for COVID-19. The 59th Annual Meeting of the Association for Computational Linguistics (ACL), 2021. Ramtin Hosseini, Xingyi Yang and Pengtao Xie. DSRNA: Differentiable Search of Robust Neural Architectures. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. Meng Zhou, Zechen Li and Pengtao Xie. Self-supervised Regularization for Text Classification. Transactions of the Association for Computational Linguistics (TACL), 2021. Jiaqi Zeng and Pengtao Xie. Contrastive Self-supervised Learning for Graph Representation Learning. AAAI Conference on Artificial Intelligence (AAAI), 2021. Seojin Bang, Pengtao Xie, Heewook Lee, Wei Wu, Eric Xing. Explaining Black-box Models Using A Deep Variational Information Bottleneck Approach. AAAI Conference on Artificial Intelligence (AAAI), 2021. Luntian Mou, Chao Zhou, Pengtao Xie, Pengfei Zhao, Ramesh Jain, Wen Gao, and Baocai Yin. Isotropic Self-supervised Learning for Driver Drowsiness Detection with Attention-based Multimodal Fusion. IEEE Transactions on Multimedia (TMM), 2021. Jeanne Vu, Ghiam Yamin, Zabrina Reyes, Alex Shin, Alexander Young, Irene Litvan, Pengtao Xie, Sebastian Obrzut. Assessment of Motor Dysfunction with Virtual Reality in Patients Undergoing [123I]FP-CIT SPECT/CT Brain Imaging. Tomography, 2021. G. Zeng, W. Yang, Z. Ju, Y. Yang, S. Wang, R. Zhang, M. Zhou, J. Zeng, X. Dong, R. Zhang, H. Fang, P. Zhu, S. Chen and Pengtao Xie. MedDialog: Large-scale Medical Dialogue Datasets. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020.

We are working on Collaborative or Cooperative Autonomous driving and sensing focusing on efficient processing (in terms of usage of data and compute) of image and videos for assisted and autonomous driving applications. The project is attempting to build deep learning algorithms which can effectively combine data from other autonomous systems for safety applications. We are trying to combine data from all the sensors on other cars for self-driving cars for safety applications. The sensors include LiDAR, Camera and radars, CAN data and so on.

Publications: [1] Bansal K, Rungta K, Bharadia D. RadSegNet: A Reliable Approach to Radar Camera Fusion. arXiv preprint arXiv:2208.03849. 2022 Aug 8. [2] Dureppagari HK, Dinesha U, Wu R, Ganji S, Ko WH, Shakkottai S, Bharadia D. Realtime intelligent control for NextG cellular radio access networks. InProceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services 2022 Jun 27 (pp. 567-568). [3] Givehchian H, Bhaskar N, Herrera ER, Soto HR, Dameff C, Bharadia D, Schulman A. Evaluating Physical-Layer BLE Location Tracking Attacks on Mobile Devices. In2022 IEEE Symposium on Security and Privacy (SP) 2022 May 22 (pp. 1690-1704). IEEE. [4] Arun A, Ayyalasomayajula R, Hunter W, Bharadia D. P2SLAM: Bearing Based WiFi SLAM for Indoor Robots. IEEE Robotics and Automation Letters. 2022 Jan 25;7(2):3326-33. [5] Wu Y, Ayyalasomayajula R, Bianco MJ, Bharadia D, Gerstoft P. Sound source localization based on multi-task learning and image translation network. The Journal of the Acoustical Society of America. 2021 Nov 5;150(5):3374-86. [6] Zhao M, Chang T, Arun A, Ayyalasomayajula R, Zhang C, Bharadia D. ULoc: Low-power, scalable and cm-accurate UWB-tag localization and tracking for indoor applications. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2021 Sep 14;5(3):1-31. [7] Wu Y, Ayyalasomayajula R, Bianco MJ, Bharadia D, Gerstoft P. Sslide: Sound source localization for indoors based on deep learning. InICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021 Jun 6 (pp. 4680-4684). IEEE.

The demand for wireless data traffic along with the scale of users grows exponentially, while the current approach to meet such scaling is to avoid interference. Unfortunately, interference avoidance techniques cannot accommodate these scaling needs. To support large-scale deployments, there is a need to enable multiple users to share the same spectrum. In other words, the interference must be mitigated and exploited rather than avoided. However, prior theoretical efforts have resulted in minimal gains in practice. This project bridges the gap between theory and practice. The PIs will jointly design and develop antennas and algorithms in PHY and MAC layers potentially enabling orders of magnitude more spectrum-efficient communications. The designed solutions would enable connectivity to all classes of devices, autonomous driving, health-care, and IoT, and will facilitate the connectivity cheaper for all segments of society. The project will result in training graduate students and fosters interdisciplinary research. The proposed activities are designed to engage students from all backgrounds focusing on those from underrepresented groups. The proposal focuses on developing Interference Alignment (IA) techniques that work at the finite Signal-to-Noise (SNR) regime. The proposal has two thrusts - one focused on the uplink and another focused on the downlink. The PIs plan to use quasi-randomly oriented antenna elements to accomplish the desired objectives. (1) Present new transmit and receive antenna hardware structures at base-stations to create favorable environments for interference management; (2) Develop new physical-layer algorithms that exploit the characteristics of the proposed antennas and increase by order(s) of magnitude the number of users sharing the same frequency slot, without needing channel information at the transmitter; (3) Develop PHY-layer algorithms and MAC-layer protocols to exploit the proposed antenna designs to enable interference alignment for a large number of users; (4) Develop novel algorithms that enable deploying the proposed research easily on existing next-generation cellular (5G) and WiFi (WiFi-6) protocols; (5) Evaluate the proposed techniques through simulations and real-world experiments with hardware and software implementation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

This namespace is dedicated to the development and validation of a novel ECG parameterization tool. The project focuses on achieving accurate and stable analysis of ECG waveform shapes across multiple datasets consisting of healthy individuals. Key objectives include benchmarking the tool against established datasets to validate the stability of waveform shape parameters over time and examining their relationships with demographic covariates such as age, sex, and BMI. This work is critical for advancing the reliability and applicability of ECG parameterization in clinical and research settings.

This namespace will facilitate collaboration for the Schmidt Sciences Oxford Research Software Engineering program as part of the Eric & Wendy Schmidt AI in Science Postdoctoral Research Fellowship program.

Public EDEX server for weather data

Edgeslab is a research lab run by Elena Zheleva in the Computer Science department at UIC. The research topics are primarily concerned with data science and relational learning.

Publications: https://www.cs.uic.edu/~elena/#lab

Our research will be focused on ML-algorithms in the context of education and documentation.

This namespace is dedicated to USRA's Earth from Space Institute (EfSI) for compute resources and research.

Publications: N/A

1. Automatic web-based workflow for structural mutation of protein residues based on total charge. 2. Machine learning techniques for automatic protein charge modulation, and structural verification of mutated protein structures and activities using Molecular Dynamics. 3. Machine learning techniques for gene editing and functional analysis of CRISPR/Cas9 and CRISPR/Cas12a proteins. 4. High performance and distributed computing applications using systems connected by optical networks. 5. GUI desktop containerization accelerated with NVIDIA GPUs.

Publications: https://github.com/selkies-project/docker-nvidia-glx-desktop https://github.com/selkies-project/docker-nvidia-egl-desktop

Official elasticsearch

Elastiflow, sflow, InMon Traffic Sentinel deployment

Educational activities related to showing students high availability services

This is part of NSF MRI associated with m3learning namespace

Engr131_exam namespace to deal with load balancing issues as a temporary fix for scaling

Publications: "None"

Engr131 exam isloated namespace for providing exam hub

Engr131 exam isloated namespace for providing exam hub

Engr131 exam isloated namespace for providing exam hub

ENGR131 Exam namespace. Needed to try and solve the scaling issues we have seen

Educational ENGR131 for the fall small class for introduction to python

Expanse federated namespace for testing the federation layer with toy workflows

Publications: “Towards a Dynamic Composability Approach for using Heterogeneous Systems in Remote Sensing”, Ilkay Altintas, Ismael Perez, Dmitry Mishin, Adrien Trouillaud, Christopher Irving, John Graham, Mahidhar Tatineni, Thomas DeFanti, Shawn Strande, Larry Smarr, Michael L. Norman. Proceedings of the IEEE 18th International Conference on e-Science, Salt Lake City, Utah, USA, October 11-14 2022. (Accepted)

Jupyter Lab environment for environmental data science resources for Universities Space Research Association (USRA)

This Namespace is dedicated to USRA's Environmental Analytics Group focusing on applications of Machine Learning in a variety of Earth science research such as wildfire, air quality, earthquake, floods, etc.

EOG >> Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines EOG near real-time processing pipeline prototyping

Our work focuses on learning representations of robot motion and perception capabilities as well as of the environment the robots are operating in. We are interested in system identification from trajectory data for robot modeling, 3D scene reconstruction from RGBD and LiDAR measurements, model-based reinforcement learning, and vision-language models for robot task planning and execution.

Publications: https://existentialrobotics.org/pages/publications.html

Our work focuses on learning representations of robot motion and perception capabilities as well as of the environment the robots are operating in. We are interested in system identification from trajectory data for robot modeling, 3D scene reconstruction from RGBD and LiDAR measurements, model-based reinforcement learning, and vision-language models for robot task planning and execution.

Publications: https://existentialrobotics.org/pages/publications.html

The Eviction Research Network collects, analyzes, & maps eviction data while helping other researchers map & analyze theirs. We specialize in using social & data science to analyze racial & gender disparities in eviction & urban theory to help explain our findings. Our goal is to expand public & scholarly knowledge on the prevalence & drivers of evictions in under-studied regions, highlight work from scholars in the field, & provide evidence-based research that legislatures, practitioners, & advocates can use to inform policy.

ESnet provides the high-bandwidth, reliable connections that link scientists at national laboratories and other research institutions.

Building AI-enabled data visualization interfaces: NAIRR Classroom

Testbed for Experimental Social Science Lab migration.

ESSRN is an initiative of UCs Berkeley, Davis, Irvine, and Santa Barbara and of the National Research Platform to build a network of social science laboratories serving as a national and international hub for experimental social science research. ESSRN will seamlessly accommodate lab, field, and online experiments on a vast and diverse multi-institutional subject pool, blurring the lines between the traditional lab and online recruitment platforms.

Etherpad system namespace provides etherpad installation - the collaborative markdown text editor.

EVL K8s research and application development towards the optiputer project and beyond. SAGE3 application and general UIC ML applications.

Publications: L. Long, T. Bargo, L. Renambot, M. Brown and A. E. Johnson, "Composable Infrastructures for an Academic Research Environment: Lessons Learned," 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Lyon, France, 2022, pp. 1209-1214, doi: 10.1109/IPDPSW55747.2022.00208. Composable Infrastructures for an Academic Research Environment: Lessons Learned, First Workshop on Composable Systems, COMPSYS 2022 Moving from Composable to Programmable, First Workshop on Composable Systems, COMPSYS 2022 PEARC22 - TITLE: CHI-in-a-Box: Reducing Operational Costs of Research Testbeds AUTHORS: Kate Keahey, Jason Anderson, Michael Sherman, Cody Hammock, Zhuo Zhen, Jenett Tillotson, Timothy Bargo, Lance Long, Taimoor Ul Islam, Sarath Babu and François Halbach Nurit Kirshenbaum, Kylie Davidson, Jesse Harden, Chris North, Dylan Kobayashi, Ryan Theriot, Roderick S. Tabalba, Michael L. Rogers, Mahdi Belcaid, Andrew T. Burks, Krishna N. Bharadwaj, Luc Renambot, Andrew E. Johnson, Lance Long, and Jason Leigh. 2021. Traces of Time through Space: Advantages of Creating Complex Canvases in Collaborative Meetings. Proc. ACM Hum.-Comput. Interact. 5, ISS, Article 502 (November 2021), 20 pages. DOI:https://doi.org/10.1145/3488552 K. Bharadwaj et al., "Securing Collaborative Work in Wide-band Display Environments," 2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC), 2021, pp. 26-34, doi: https://doi.org/10.1109/CIC52973.2021.00014. M. Ragonea, M. T. Saraya, L. Long, R. Shahbazian-Yassara, F. Mashayeka, V. Yurkiv, "Deep learning for mapping element distribution of high-entropy alloys in scanning transmission electron microscopy images,” Computational Materials Science, Volume 201, January 2022, 110905, https://doi.org/10.1016/j.commatsci.2021.110905.

This project focus on implementation of charged particle tracking pipeline as a Triton Inference Server. Clients implemented in ACTS will send track-finding requests to the Triton server and the server will return track candidates to the client after processing. The pipeline contains several track reconstruction algorithms. Because of the heterogeneity and dependency chain of the pipeline, we will explore different server settings to maximize the throughput of the pipeline, and we will study the scalability of the inference server and time reduction of the client.

working with eye-tracking and human attention. the project will deal with how human attention affects different deep learning models.

Secure Function-as-a-Service system based on Firecracker

System namespace - for falco security alerting setup on 5/6/24

This namespace contains software to develop machine learning algorithms to analyze Alzheimer's disease.

FAMU Research Group is carrying out a wide range of projects in cloud computing/storage, machine learning and data mining, cybersecurity, big data, social networks, wireless networks, and COVID-19, etc. We work to reduce the barrier to developing, managing applications that are able to process masses of data/jobs of users and model, analyze and optimize the performance of networks, and convert the information hidden in the large amounts of data into knowledge.

Publications: 1. J. Liu, H. Shen, H. Chi, H. Narman, Y. Yang, L. Cheng, and W. Chung. A Low-Cost Multi-Failure Resilient Replication Scheme for High Data Availability in Cloud Storage, IEEE/ACM Transactions on Networking (TON), 1-16, 2020. DOI: 10.1109/TNET.2020.3027814 2. J. Liu, H. Shen, L. Yu, H. S. Narman, J. Zhai, J. Hallstrom, and Y. He. Characterizing Data Deliverability of Greedy Routing in Wireless Sensor Networks, IEEE Transactions on Mobile Computing (TMC), 17(3), 543-559, 2018. DOI: 10.1109/TMC.2017.2737005 3. J. Liu and H. Shen, and H. S. Narman. Popularity-Aware Multi-Failure Resilient and Cost-Effective Replication for High Data Durability in Cloud Storage, IEEE Transactions on Parallel and Distributed Systems (TPDS), 30(10), 2355-2369, 2019. DOI: 10.1109/TPDS.2018.2873384 4. J. Liu, H. Shen, and L. Yu. Question Quality Analysis and Prediction in Community Question Answering Services with Coupled Mutual Reinforcement, IEEE Trans. Services Comput., 10(2), 286-301, 2017. DOI: 10.1109/TSC.2015.2446991. 5. J. Liu, H. Shen, H. Narman, W. Chung, and Z. Lin. A Survey of Mobile Crowdsensing Techniques: A Critical Component for The Internet of Things, ACM TCPS, 2(3), 18:1-26, 2018. DOI: 10.1145/3185504. 6. J. Liu, H. Shen, and L. Chen. CORP: Cooperative Opportunistic Resource Provisioning for Short-Lived Jobs in Cloud Systems, In Proc. of IEEE CLUSTER, 2016. DOI: 10.1109/CLUSTER.2016.65. 7. J. Liu and H. Shen. A Low-Cost Multi-Failure Resilient Replication Scheme for High Data Availability in Cloud Storage, In Proc. of HiPC, 2016. DOI: 10.1109/HiPC.2016.036. 8. J. Liu, H. Shen, and H. Narman. CCRP: Customized Cooperative Resource Provisioning for High Resource Utilization in Clouds, In Proc. of IEEE Big Data, 2016. DOI: 10.1109/BigData.2016.7840610. 9. J. Liu, H. Shen, A. Sarker, and W. Chung. Leveraging Dependency in Scheduling and Preemption for High Throughput in Data-Parallel Clusters, In IEEE CLUSTER, 2018. DOI: 10.1109/CLUSTER.2018.00054. 10. J. Liu, L. Yu, H. Shen, Y. He, and J. Hallstrom. Characterizing Data Deliverability of Greedy Routing in Wireless Sensor Networks, In Proc. of SECON, 2015. DOI: 10.1109/SAHCN.2015.7338328. 11. J. Liu and L. Cheng. SwiftS: A Dependency-Aware and Resource Efficient Scheduling for High Throughput in Clouds, In Proc. of INFOCOM WKSHPS, 2021. DOI:10.1109/INFOCOMWKSHPS51825.2021.9484459 12. J. Liu and H. Shen. Dependency-aware and Resource-efficient Scheduling for Heterogeneous Jobs in Clouds, In Proc. of IEEE CloudCom, 2016. DOI: 10.1109/CloudCom.2016.0032. 13 J. Liu, W. Chung, Y. Huang and C. Toraman. CrossSimON: A Novel Probabilistic Approach to Cross-Platform Online Social Network Simulation, In IEEE ISI, 2019. DOI: 10.1109/ISI.2019.8823276. 14. J. Liu, R. Alo, and Y. J. Parra Bautista. DeepTrace: Improving US Pandemic Health Care through Health Disparity Identification and Determinant Tracing, In IDSTA, 2020. DOI: 10.1109/IDSTA50958.2020.9264064. 15. J. Liu, R. Alo, and Y. J. Parra Bautista. DeepTrace: Improving Pandemic Health Care by Identifying Disparities and Determinants, In IEEE BigComp, 2020. DOI: 10.1109/BigComp51126.2021.00017.

Undergraduate Linear Algebra and Scientific Computing course

We plan to upload data (images) generated from experiments. Use that data as a source to train our machine learning algorithm. Store and transmit input and output data. The ML/AI component will be spearheaded by Jie Yan in the Department of Computer Science at Bowie State University (BSU) in Bowie, MD. BSU is Maryland's oldest historically black university and one of the ten oldest in the country. The student population is 61% female and 82% Black or African American. Co-PI Yan is the Director of the Computational Perception & Animation Lab. Her team will train baseline AI models and adaptive AI models. We propose to investigate how to use artificial intelligence (AI) models to monitor salt creep and related mineral deposit patterns resulting from evaporation and crystallization processes. This part of the research project aims to apply state-of-the-art AI to predict chemical composition and possibly experimental conditions from photos. It will also improve the current understanding of self-amplifying crystallization and the underlying physico-chemical processes that determine spread rate and deposit morphology.

Testing annex Htcondor using a "private" HTC cluster

ETL and Analyze FCC ULS public data files (land mobile radio systems for the entire U.S.)

FDT auto disk to disk testing

FFP microlensing for comparing to PBH microlensing

Namespace where rasa-bot for rocket chat will be implemented .

Protein folding via Folding@Home for COVID-19

freertr-dpdk http://freertr.org/ freeRouter is a free, open source router os process. it speaks routing protocols, and (re)encapsulates packets on interfaces.

Publications: https://sumitkumarjha.com/publications/

Data reduction and more in support of a VLT Large Programme to study the host galaxies of fast radio bursts

Exploratory work on using FPGA for Fusion simulation purposes. The main aim is to speed up FFT compute in the CGYRO code.

Fusion plasma simulation, mostly using CGYRO. Relies on many-node GPU resources.

Larry Smarr's Future Patient. nearly a decade and a half of health information for Dr. Larry Smarr. This repository contains a postgresql database and a jupyterLab instance for visualizations of Larry's health data.

Generative AI research for multimodal synthetic medical data generation

Publications: https://scholar.google.com/citations?user=gE-WTF8AAAAJ&hl=en

Research areas focus on: 1) Chemical models for the origins of life set in inland hydrothermal springs or pools in the early Archaean. 2) Lipid and amino acid chemistry subject to cycles of hydration and dehydration. 3) Application of computational and complexity theory models to emergent function within chemical systems. 4) Design of spacecraft missions for asteroid and comet sample return.

Publications: Damer, Bruce, and David Deamer. "Coupled phases and combinatorial selection in fluctuating hydrothermal pools: A scenario to guide experimental approaches to the origin of cellular life." Life 5.1 (2015): 872-887. Damer, Bruce, and David Deamer. "The hot spring hypothesis for an origin of life." Astrobiology 20.4 (2020): 429-452. Deamer, David, Bruce Damer, and Vladimir Kompanichenko. "Hydrothermal chemistry and the origin of cellular life." Astrobiology 19.12 (2019): 1523-1537. Damer, Bruce, et al. "The EvoGrid-A Framework for Distributed Artificial Chemistry Cameo Simulations Supporting Computational Origins of Life Endeavors." ALIFE. 2010.

Garage - An open-source distributed object storage service tailored for self-hosting

Gas detection from spectral images for remote sensing applications

Gatekeeper is a validating and mutating webhook that enforces CRD-based policies executed by Open Policy Agent, a policy engine for Cloud Native environments hosted by CNCF as a graduated project.

Generative AI lab space for Universities Space Research Association.

The Generative Design Studio namespace is established to provide researchers at UCSC interested in Diffusion models (e.g. Stable Diffusion) a place to access computational resources, share code and collaborate using Jupyter Notebooks.

Graph Convolution Neural Networks (GCNN) have emerged as state-of-the-art architectures for predictions tasks involving molecules. In this work, we do an overview of various molecular graph networks available in literature. This will be used to identify the architectures which are suitable for various material prediction tasks.

Our lab works on generating interpretable explanations from opaque ML models and using them to make more robust decisions. We use Nautilus for training/running DNNs, rules lists, and generative models to generate a novel dataset of failure cases for analysis.

Publications: None.

GitLab deployment

Namespace for gitlab runners for GitLab@UCSC (gitlab.soe.ucsc.edu).

Testing integration with Globus Online on kansan I2 node

gnmic (pronoun.: gee·en·em·eye·see) is a gNMI CLI client that provides full support for Capabilities, Get, Set and Subscribe RPCs with collector capabilities.

This chart deploys a gNMIc cluster. This application enables users to stream telemetry from gNMI capable routers.

gnmic-dev desktop environment using containerlab and noVNC

automated framework to be trained to act as the surrogate of the HLS tool. It can be used to expedite the design optimization process.

Publications: https://dl.acm.org/doi/abs/10.1145/3489517.3530409

Run goodness-of-fit procedure for FPCA models.

Namespace for experiments regarding usage of geometry priors in Machine Learning tasks .

Development and Learning Namespace for Carl Lundstedt, created 2/14/2024

JupyterHub instance for GP-ENGINE related compute

Research for Dr. Jordan Malof's lab at the University of Missouri

Deep learning research using remote sensing data for post-wildfire assessment

Research and teaching for Mizzou research lab

Research and teaching for Mizzou Journalism department

Research for the University of Missouri's CitySim AI lab

This namespace is going to be used to teach data science and analytics students how to use cloud computing to perform big data related task and computing

Teaching cloud computing and Kubernetes basics and principles and how to use them to facilitate deep learning research

Pods and jobs for the classification and detection of Boma settlements

Experiments for pretraining DNN models

Research and teaching activities for the Institution for Data Science and Informatics and the University of Missouri

THIS IS A TEST THIS IS A TEST THIS IS A TEST THIS IS A TEST THIS IS A TEST THIS IS A TEST THIS IS A TEST

Energy Simulation with Deep Learning Models

Research for the MINDFUL lab at the University of Missouri

Research for the Nano Particle research lab at the University of Missouri

Research for MU lab

Test Windows Virtual Machine with KubeVirt

Publications: None

Research for Lab at MU

Research for lab at MS&T

Build machine learning and deep learning models to evaluate the development of some diseases and also patient outcomes.

The research in this namespace research focuses on biomedical informatics, explainable AI, quantum computing, cybersecurity, and spatial Big Data analytics

Publications: Setting Up Research Jobs in Nautilus Kubernetes Hyper-Cluster: https://github.com/scottgs/Nautilus_Kubernetes_DeepLearning

Energy efficiency for electrical power plants using machine and deep learning methods.

Publications: "None"

Compute for Research at MU MINDFUL lab

Running JupyterHub for research related to quantum computing in University of Missouri

Publications: Setting Up Research Jobs in Nautilus Kubernetes Hyper-Cluster: https://github.com/scottgs/Nautilus_Kubernetes_DeepLearning

Our project leverages Artificial Intelligence, Big data analytics, and Machine learning to revolutionize transportation systems to enhance safety, resilience and efficiency.

Publications: IEEE Intelligent Transportation Systems, Transporation Research, IEEE/Computer Vision and Pattern Recognition

The Research Informatics Lab (RILab) at the University of Missouri is a hub of innovation in health informatics, focusing on enhancing patient care through advanced data analysis and informatics technologies. The lab's research encompasses developing support tools for complex healthcare decisions and pioneering patient data privacy and management methods. Significant contributions include predictive modeling for managing chronic diseases and creating tools to improve treatment outcomes.

NLP research, Network analysis of social media using machine learning and deep learning.

Test namespace for internal HCC NRP workshop on NRP, Kubernetes and JupyterHub.

Research JupyterHub instance for resarch under the GP-ENGINE grant

Namespace for developing training material for GP-ENGINE and Nautilus

namespace for people participating in gp-engine tutorials related to Kubernetes and Nautilus.

Namespace for Jupyter front end for gp-engine tutorials

Software engineering research projects; Deep learning models for software languages

Publications: https://scholar.google.com

Software engineering research projects; Deep learning models for software languages

Publications: https://scholar.google.com

Resources for UNT research and teaching

Research and teaching for Culver-Stockton College

Publications: Setting Up Research Jobs in Nautilus Kubernetes Hyper-Cluster: https://github.com/scottgs/Nautilus_Kubernetes_DeepLearning

JupyterHub demonstrations for institution deployment

JupyterLab instance for CS education and experimentation with Binder

Research and teaching for Mizzou lab

Research and teaching for Mizzou lab

Research and teaching for Mizzou lab

JupyterHub for Mizzou CS Dept

Research on remote sensing and health informatics to determine disease patterns

Publications: "None

Mizzou DSA Parallel Computing Course

Publications: Setting Up Research Jobs in Nautilus Kubernetes Hyper-Cluster: https://github.com/scottgs/Nautilus_Kubernetes_DeepLearning

Research and teaching for Mizzou courses

Publications: Setting Up Research Jobs in Nautilus Kubernetes Hyper-Cluster: https://github.com/scottgs/Nautilus_Kubernetes_DeepLearning

Research and teaching for Mizzou eecs department

Publications: Setting Up Research Jobs in Nautilus Kubernetes Hyper-Cluster: https://github.com/scottgs/Nautilus_Kubernetes_DeepLearning

Research and teaching for Mizzou college of engineering

Research and teaching for Mizzou lab

Poultry farm detection in aerial imagery in Missouri

Mizzou DSA Parallel Computing Course

Publications: Setting Up Research Jobs in Nautilus Kubernetes Hyper-Cluster: https://github.com/scottgs/Nautilus_Kubernetes_DeepLearning

Research and teaching for Mizzou lab

Namespace for JupyterHub running for Mizzou HPC.

Publications: Setting Up Research Jobs in Nautilus Kubernetes Hyper-Cluster: https://github.com/scottgs/Nautilus_Kubernetes_DeepLearning

Research for Mizzou lab

Publications:

Research for MU grad student

Publications: None

Research and teaching for Mizzou lab

Publications: Setting Up Research Jobs in Nautilus Kubernetes Hyper-Cluster: https://github.com/scottgs/Nautilus_Kubernetes_DeepLearning

Namespace to be used for research and development of Kubernetes workshop

Research and teaching for Mizzou eecs department

Publications: Setting Up Research Jobs in Nautilus Kubernetes Hyper-Cluster: https://github.com/scottgs/Nautilus_Kubernetes_DeepLearning

Research and teaching for Mizzou lab

Research and teaching for Mizzou eecs department

Publications: Setting Up Research Jobs in Nautilus Kubernetes Hyper-Cluster: https://github.com/scottgs/Nautilus_Kubernetes_DeepLearning

GPN Mizzou - NextGen Biomedical Informatics Lab - Khuder Alaboud Utilizing a data-driven approach to assess the impact of long-term medication use. Applying deep learning methods, such as the LSTM model, to predict individual-level medication usage patterns and capture the temporal dependencies and effectiveness of antidepressants, along with their associated adverse effects

Research and teaching for Mizzou lab

Publications: Setting Up Research Jobs in Nautilus Kubernetes Hyper-Cluster: https://github.com/scottgs/Nautilus_Kubernetes_DeepLearning

Research and teaching for Mizzou lab

Publications: Setting Up Research Jobs in Nautilus Kubernetes Hyper-Cluster: https://github.com/scottgs/Nautilus_Kubernetes_DeepLearning

Research and teaching for Mizzou lab

GPN Mizzou - NextGen Biomedical Informatics Lab - Khuder Alaboud Utilizing a data-driven approach to assess the impact of long-term medication use. Applying deep learning methods, such as LSTM model, to predict individual-level medication usage patterns and capture the temporal dependencies and effectiveness of antidepressants, along with their associated adverse effects

Namespace for Professor Shi-Jie Chen from University of Missouri Department of Physics and Astronomy.

Publications: Li, J., & Chen, S. (2023). RNAJP: enhanced RNA 3D structure predictions with noncanonical interactions and global topology sampling. Nucleic Acids Research, In press. Chen, S. J., Hassan, M., Jernigan, R. L., Jia, K., Kihara, D., Kloczkowski, A., Kotelnikov, S., Kozakov, D., Liang, J., Liwo, A., Matysiak, S., Meller, J., Micheletti, C., Mitchell, J. C., Mondal, S., Nussinov, R., Okazaki, K. I., Padhorny, D., Skolnick, J., Sosnick, T. S., Rose, G. D. (2023). Opinion: Protein folds vs. protein folding: Differing questions, different challenges. Proc Natl Acad Sci U S A., 120(1), e2214423119. https://doi.org/10.1073/pnas.2214423119 Lewis Rolband, Damian Beasock, Yang Wang, Yao-Gen Shu, Jonathan D. Dinman, Tamar Schlick, Yaoqi Zhou, Jeffrey S. Kieft, Shi-Jie Chen, Giovanni Bussi, Abdelghani Oukhaled, Xingfa Gao, Petr Å ulc, Daniel Binzel, Abhjeet S. Bhullar, Chenxi Liang, Peixuan Guo, Kirill A. Afonin. Biomotors, viral assembly, and RNA nanobiotechnology: Current achievements and future directions, Computational and Structural Biotechnology Journal, Volume 20, 2022, Pages 6120-6137, https://doi.org/10.1016/j.csbj.2022.11.007. Zhou, Y., & Chen, S. (2022). Graph deep learning locates magnesium ions in RNA. QRB Discovery, 3, E20. doi:10.1017/qrd.2022.17

Modeling visual reasoning processes to create the best model there is

Research and teaching for graduate student in HPDI Lab at the University of Missouri

Research and teaching for Mizzou lab

Research and Development namespace for Research Computing Support Services in the Division of IT at the University of Missouri

Publications: Setting Up Research Jobs in Nautilus Kubernetes Hyper-Cluster: https://github.com/scottgs/Nautilus_Kubernetes_DeepLearning

Research and teaching for Mizzou eecs department

Publications: Setting Up Research Jobs in Nautilus Kubernetes Hyper-Cluster: https://github.com/scottgs/Nautilus_Kubernetes_DeepLearning

Used for K8s training

Publications: Setting Up Research Jobs in Nautilus Kubernetes Hyper-Cluster: https://github.com/scottgs/Nautilus_Kubernetes_DeepLearning

Research and teaching for Mizzou ViGiR lab

Publications: Setting Up Research Jobs in Nautilus Kubernetes Hyper-Cluster: https://github.com/scottgs/Nautilus_Kubernetes_DeepLearning

Namespace for Dr. DeSouza's lab from the University of Missouri

Publications: None

JupyterHub environment for OU Machine Learning Practice and OU Deep Learning Practice classes.

OU Symbiotic Computing Laboratory School of Computer Science

Various testing efforts and socialization of the platform.

AI/ML GPU benchmarks for Tom DeFanti using NVIDIA MLCommons benchmarking suite. Specifically focusing on comparing A100 and our 4090 nodes.

Use ML (primarily graph ML and NLP) methods to extract and analyze information from unstructured text data.

Apache guacamole deployment - the VNC/RDP/Console client to use for remote IPMI management and accessing remote GUI apps

Gary's Unbelievable Research Unit (GURU) is carrying out a wide range of projects using deep learning. Recent projects include using deep learning to recognize speech and perform diarization in This American Life episodes, using deep learning to segment mouse cardiac MRI to speed animal research in heart disease in collaboration with the UCSD Medical School, mapping from small molecule NMR to a cluster space where similar molecular structures are near one another in the space to speed structure elucidation for natural products in collaboration with researchers at Scripps Institution of Oceanography, using GANS to modify faces to alter their first impressions, developing methods to speed up convergence of deep learning, and projects in computational cognitive neuroscience, modeling the human visual system using anatomical constraints in order to explain how the visual system works.

Publications: Henry Huanru Mao, Shuyang Li, Julian McAuley, Garrison W. Cottrell (2020) Speech Recognition and Multi-Speaker Diarization of Long Conversations. arXiv preprint arXiv:2005.08072. Hammad A. Ayyubi, Md. Mehrab Tanjim, Julian J. McAuley, Garrison W. Cottrell (2020) Generating Rationales in Visual Question Answering. arXiv preprint arXiv:2004.02032. Thomas Bachlechner, Bodhisattwa Prasad Majumder, Huanru Henry Mao, Garrison W Cottrell, Julian McAuley (2020) ReZero is all you need: Fast convergence at large depth. arXiv:2003.04887. Raphael Reher, Hyun Woo Kim, Chen Zhang, Huanru Henry Mao, Mingxun Wang, Louis-Félix Nothias, Andres Mauricio Caraballo-Rodriguez, Evgenia Glukhov, Bahar Teke, Tiago Leao, Kelsey L Alexander, Brendan M Duggan, Ezra L Van Everbroeck, Pieter C Dorrestein, Garrison W Cottrell, William H Gerwick (2020). A Convolutional Neural Network-Based Approach for the Rapid Characterization of Molecularly Diverse Natural Products. Journal of the American Chemical Society 142(9):4114-4120. (supplementary material). Yueying Li, Hao-Bing Yu, Yi Zhang, Tiago Leao, Evgenia Glukhov, Marsha L Pierce, Chen Zhang, Hyunwoo Kim, Huanru Henry Mao, Fang Fang, Garrison W Cottrell, Thomas F Murray, Lena Gerwick, Huashi Guan, William H Gerwick (2020) Pagoamide A, a Cyclic Depsipeptide Isolated from a Cultured Marine Chlorophyte, Derbesia sp., Using MS/MS-Based Molecular Networking. Journal of Natural Products 83(3):617-625. Mao, Huanru Henry, Majumder, Bodhisattwa Prasad, McAuley, Julian and Cottrell, Garrison W. (2019) Improving Neural Story Generation by Targeted Common Sense Grounding. In Empirical Methods in Natural Language Processing (EMNLP-19). Attala, Chad, Song, Amanda, Tam, Bartholomew, Rathis, Asmitha, and Cottrell, Garrison W. (2019) Modifying social dimensions of faces with ModifAE. In A.K. Goel, C.M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society (pp. 105-111). Montreal, QB: Cognitive Science Society. Donahue, Chris, Mao, Huanru Henry, Li, Yiting Ethan, Cottrell, Garrison W., and McAuley, Julian (2019) LakhNES: Improving multi-instrumental music generation with cross-domain pre-training. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR-19).

Gary's Unbelievable Research Unit (GURU) is carrying out a wide range of projects using deep learning. We have projects using deep learning to track dolphins in video in collaboration with comparative ethologists, segmenting mouse cardiac MRI to speed animal research in heart disease in collaboration with the UCSD Medical School, mapping from small molecule NMR to a cluster space where similar molecular structures are near one another in the space to speed structure elucidation for natural products in collaboration with researchers at Scripps Institution of Oceanography, and our own projects in computational cognitive neuroscience modeling the human visual system using anatomical constraintsin order to explain how the visual system works.

Research lab that studies the formation, lives, and explosive deaths of stars across cosmic time from their fossils as black holes and neutron stars.

Guacamole XGL desktop

Earth System Biogeochemistry Lab at UC Santa Cruz .............

Estimating 3D hand affordance on 3D objects. Given a single object point cloud, we want to estimate how human hand or robot hand can grasp the object.

Publications: Hanwen Jiang*, Shaowei Liu*, Jiashun Wang, Xiaolong Wang. Hand-Object Contact Consistency Reasoning for Human Grasps Generation. International Conference on Computer Vision (ICCV), 2021 (Oral Presentation).

Estimating 3D hand and object pose from a single image is an extremely challenging problem: hands and objects are often self-occluded during interactions, and the 3D annotations are scarce as even human cannot directly label the ground-truths from a single image perfectly. To tackle these challenges, we propose a unified framework for estimating the 3D hand and object poses with semi-supervised learning.

Publications: Jiashun Wang, Huazhe Xu, Jingwei Xu, Sifei Liu, Xiaolong Wang. Synthesizing Long-Term 3D Human Motion and Interaction in 3D Scenes, CVPR 2021 (https://arxiv.org/abs/2012.05522). Shaowei Liu*, Hanwen Jiang*, Jiarui Xu, Sifei Liu, Xiaolong Wang. Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Time. CVPR 2021.

Temporary namespace for moving objaverse-processed1 to haosu s3.

HaoSu Image Service: Frontend for HaoSu S3. Hosts scalable image processing services on HaoSu nodes.

HAProxy system install - the ingress controller providing SSL termination and proxying http requests

Namespace for Human Aware Robotics Exploration Lab at UCSC under professor Steve McGuire.