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Projects

Funded Projects

DeepDISC-Euclid: Detection, Instance Segmentation, and Classification for Euclid with Deep Learning

  • Investigators: Volodymyr Kindratenko (Co-PI)
  • Award period: 2025-2028
  • Award amount: $900,176
  • Funding source: NASA

REU Site: The future of discovery: training students to build and apply open-source machine learning models and tools

  • Investigators: Volodymyr Kindratenko (PI)
  • Award period: 2025 – 2028
  • Award amount: $465,000
  • Funding source: NSF-OAC award 2447843

Distributed Adaptive Runtime, Collective Communication, and Programming Models for Hybrid Cloud

  • Investigators: Volodymyr Kindratenko (Co-PI)
  • Award period: 2025-2026
  • Award amount: $266,625
  • Funding source: IBM Corp via IBM-Illinois Discovery Accelerator Institute

Chan Zuckerberg Biohub Spoke Award

  • Investigators: Volodymyr Kindratenko (PI)
  • Award period: 2024-2025
  • Award amount: $107,000
  • Funding Source: Chan Zuckerberg Biohub

AI Broad Search Chat Tool

  • Investigators: Volodymyr Kindratenko (Co-PI)
  • Award period: 2024-2025
  • Award amount: $98,000
  • Funding source: Vyriad, Inc.

Personalized Pregnancy Nutrition Project

  • Investigators: Volodymyr Kindratenko (Co-PI)
  • Award period: 2024-2025
  • Award amount: $75,000
  • Funding source: Illinois Science and Technology Coalition

Photocarboxylation: From Captured CO2 to Value-added Chemicals

  • Investigators: Volodymyr Kindratenko (PI)
  • Award period: 2025-2026
  • Award amount: $119,189
  • Funding source: UChicago – Argonne, LLC

AWS Public Sector Generative AI Impact Initiative: UIUC AI Chat

  • Investigators: Volodymyr Kindratenko (PI)
  • Award period: 2024-2025
  • Award amount: $5,000
  • Funding source: Amazon Web Services

Exploring the impact and potential of Generative AI in Engineering Education

  • Investigators: Abdu Alawini (PI), Volodymyr Kindratenko (Co-PI), Sotiria Koloutsou-Vakakis (Co-PI), Tomasz Kozlowski (Co-PI), Meredith Blumthal (Co-PI), Maryalice Wu (Co-PI)
  • Award period: 2024-2026
  • Award amount: $77,380
  • Funding source: Kern Family Foundation grant, UIUC Strategic Instructional Innovations Program

Collaborative Research: Frameworks: hpcGPT: Enhancing Computing Center User Support with HPC-enriched Generative AI

  • Investigators: Volodymyr Kindratenko (PI)
  • Award period: 2024-2027
  • Award amount: $360,000
  • Funding source: NSF-OAC Award 2411295

Collaborative Research: Frameworks: Diamond: Democratizing Large Neural Network Model Training for Science

  • Investigators: Volodymyr Kindratenko (PI)
  • Award period: 2023-2026
  • Award amount: $549,999
  • Funding source: NSF-OAC Award 2311768

DeepDISC-Roman: Detection, Instance Segmentation, and Classification for Roman with Deep Learning

  • Investigators: Volodymyr Kindratenko (Co-PI)
  • Award period: 2023-2025
  • Award amount: $300,000
  • Funding source: NASA Award 22-ROMAN22-0066

Frameworks for Cloud-based Parallel Applications Supporting Runtime Adaptivity and Resource Elasticity

  • Investigators: Volodymyr Kindratenko (Co-PI)
  • Award period: 2023-2025
  • Award amount: $636,000
  • Funding source: IBM Corp via IBM-Illinois Discovery Accelerator Institute

Category I: Bridging the Gap Between AI/ML Computing Demands and Today’s Capabilities

  • Investigators: Volodymyr Kindratenko (Co-PI)
  • Award period: 2023-2025
  • Award amount: $29,399,996
  • Funding source: NSF-OAC Award 2320345

Privacy Preserving Federated Learning (APPFL) toolkit

  • Investigators: Volodymyr Kindratenko (PI)
  • Award period: 2023-2025
  • Award amount: $135,336
  • Funding source: UChicago-Argonne, LLC

Concluded Projects

Development and adoption of resilient federated learning algorithms

  • Investigators: Volodymyr Kindratenko (PI)
  • Award period: 2024
  • Award amount: $28,667
  • Funding source: UChicago-Argonne LLC

Development of a chatbot platform for Plants of India website

  • Investigators: Volodymyr Kindratenko (PI)
  • Award period: 2024
  • Award amount: $10,000
  • Funding source: ATREE USA

A Versatile Platform for Enhanced Teaching, Computing, and Data-Driven Research

  • Investigators: Volodymyr Kindratenko (PI)
  • Award period: 2023-2024
  • Award amount: $30,000 (Azure credits)
  • Funding source: Accelerate Foundation Models Research Initiative, Microsoft

Addressing the Impact of Generative AI in Engineering Courses

  • Investigators: Volodymyr Kindratenko (PI)
  • Award period: 2023-2024
  • Award amount: $8,000
  • Funding source: Grants for Advancement of Teaching in Engineering (GATE) program at the Grainger College of Engineering

From Personalized Education to Scientific Discovery with AI: Rapid Deployment of AI Domain Experts

  • Investigators: Volodymyr Kindratenko (Co-PI)
  • Award period: 2023-2024
  • Award amount: $68,000
  • Funding source: Amazon-Illinois Center on AI for Interactive Conversational Experiences

Applying Artificial Intelligence to Achieve Personalized, Precision-Based Nutritional Monitoring and Guidance

  • Investigators: Volodymyr Kindratenko (PI)
  • Award period: 2023-2024
  • Award amount: $50,000
  • Funding source: Personalized Nutrition Initiative’s Seed Grant Program

Towards a Center for Intelligent Brain Mapping

  • Investigators: Volodymyr Kindratenko (Co-PI)
  • Award period: 2023-2024
  • Award amount: $75,000
  • Funding source: UIUC Strategic Research Initiative

AWS Promotional Credit

  • Investigators: Volodymyr Kindratenko (PI)
  • Award period: 2023-2024
  • Award amount: $10,000
  • Funding source: Amazon Web Services

AI-Powered Brain Tumor Segmentation

  • Investigators: Volodymyr Kindratenko (Co-PI)
  • Award period: 2023-2024
  • Award amount: $99,936
  • Funding source: Jump ARCHES

Towards a Center for Intelligent Brain Mapping

  • Investigators: Volodymyr Kindratenko (Co-PI)
  • Award period: 2023-2024
  • Award amount: $150,000
  • Funding source: Beckman Institute Research Seed Grants Program

Application of AI and Machine Learning in Managing PC Transportation Assets

  • Investigators: Volodymyr Kindratenko (Co-PI)
  • Award period: 2023-2024
  • Award amount: $2,000,000
  • Funding source: U.S. Department of Transportation

AI Teaching Assistant Pilot Project

  • Investigators: Volodymyr Kindratenko (PI)
  • Award period: 2023
  • Award amount: $14,000
  • Funding source: Geis College of Business

Predicting hyperlocal air pollutant concentrations using AI at the Edge

  • Investigators: Volodymyr Kindratenko (Co-PI)
  • Award period: 2022-2024
  • Award amount: $250,000
  • Funding source: DPI-Argonne Seed Grant

Enabling Cloud-native HPC Applications and Support for AI Workflows

  • Investigators: Volodymyr Kindratenko (PI)
  • Award period: 2021-2023
  • Award amount: $471,770
  • Funding source: IBM Corp via IBM-Illinois Discovery Accelerator Institute

Taming amphotericin B, the antifungal drug of last resort: making it safe and effective

  • Investigators: Volodymyr Kindratenko (Co-PI)
  • Award period: 2021
  • Award amount: $55,794
  • Funding source: NCSA Center-Directed Discretionary Research (CDDR)

Collaborative Research: Frameworks: Machine learning and FPGA computing for real-time applications in big-data physics experiments

  • Investigators: Volodymyr Kindratenko (PI)
  • Award period: 2019-2023
  • Award amount: $651,314
  • Funding source: NSF-CSSI

Collaborative Research: Advancing Science with Accelerated Machine Learning

  • Investigators: Volodymyr Kindratenko (PI)
  • Award period: 2019-2024
  • Award amount: $577,652
  • Funding source: NSF-HDR

Physics-informed neural network modeling of fluid flow in microporous materials

  • Faculty Fellow: Roman Makhnenko
  • NCSA Collaborators: Volodymyr Kindratenko, Seid Koric, Shirui Luo
  • Award Year: 2022-2023 (academic year)
  • Funding source: NCSA Center Directed Discretionary Research program

Early Detection and Prediction of Parkinsonism Powered by Multi-Modal Few-Shot Learning

  • Faculty Fellow: Yuxiong Wang
  • NCSA Collaborators: Volodymyr Kindratenko, Colleen Bushell, Maria Jaromin
  • Award Year: 2022-2023 (academic year)
  • Funding source: NCSA Center Directed Discretionary Research program

Synergistic Integration of AI with MR Spectroscopic Imaging to Unravel Molecular Fingerprints of Brain Function and Neurodegenerative Diseases

  • Faculty Fellows: Zhi-Pei Liang and Brad Sutton
  • NCSA Collaborators: Volodymyr Kindratenko, Colleen Bushell, Maria Jaromin
  • Award Year: 2022-2023 (academic year)
  • Funding source: NCSA Center Directed Discretionary Research program

Insurtech innovation and university-industry collaboration

  • Faculty Fellow: Zhiyu Quan
  • NCSA Collaborators: Volodymyr Kindratenko
  • Award Year: 2022-2023 (academic year)
  • Funding source: NCSA Center Directed Discretionary Research program

REU SITE: THE FUTURE OF DISCOVERY: TRAINING STUDENTS TO BUILD AND APPLY OPEN SOURCE MACHINE LEARNING MODELS AND TOOLS

This REU Site project gives participating students experience in many aspects of machine learning, ranging from developing open source machine learning models and tools to applying them in the real world.

  • Investigators: Volodymyr  Kindratenko  (PI), Daniel  Katz (Co-PI)
  • Start date: April 15, 2021; End date: March 31, 2024 (estimated)
  • Award amount: $405,000.
  • Funding source: NSF REU

Frameworks: Machine Learning and FPGA Computing for Real-Time Application in Big-Data Physics Experiments

This project is pushing the frontiers of deep learning at scale, demonstrating the versatility and scalability of these methods to accelerate and enable new physics in the Big Data era.

  • Investigators: Volodymyr Kindratenko (PI), Dan Katz (Co-PI)
  • Start date: October 1, 2019; End date: September 30, 2023
  • Award amount to date: $651,314
  • NSF programs: Office of Multidisciplinary Activities, Computational Physics, Software Institutes
  • Funding source: NSF CSSI

Advancing Science with Accelerated Machine Learning

This project focuses on the design of deep learning algorithms for real-time data analytics of time-series and image datasets using Field Programmable Gate Arrays to accelerate low-latency inference of machine learning algorithms.

  • Investigators: Zhizhen Zhao(PI), Mark Neubauer (Co-PI), Volodymyr Kindratenko (Co-PI), 
  • Start date: September 1, 2019; End date: August 31, 2023
  • Award amount to date: $600,311
  • NSF program: Cyberinfrastructure
  • Funding source: NSF HDR

FAIR Framework for Physics: Inspired Artificial Intelligence in High Energy Physics

This project will make artificial AI models and data more accessible and reusable to accelerate research in AI research and development.

  • Investigators: Eliu Huerta (PI), Mark Neubauer (Co-PI), Volodymyr Kindratenko (Co-PI), Zhizhen Zhao (Co-PI), Dan Katz (CO-PI), Roger Rusack (Co-PI), Philip Harris (Co-PI), Javier Duarte (Co-PI)
  • Start date: September 2020; End date: August 2023
  • Award amount to date: $2,200,000
  • Funding source: DOE FAIR

DEVELOPMENT OF AN INSTRUMENT FOR DEEP LEARNING RESEARCH

This project’s objective is to build a dedicated computer system for supporting deep learning applications, particularly training deep neural networks, in scalable and efficient ways.

  • Investigators: William Gropp (PI), Roy Campbell (Co-PI), Volodymyr Kindratenko (Co-PI), Jian Peng (Co-PI)
  • Start date: October 1, 2017; End date: September 30, 2021 (estimated)
  • Award amount: $2,721,983
  • Funding source: NSF MRI

AGNET: Weighing Black Holes with Deep Learning

A pilot program to develop a new, interdisciplinary approach combining astronomy Big Data with machine learning tools to build a deep learning algorithm to estimate the masses of supermassive black holes.

  • Investigators: Xin Liu (Faculty Fellow) and Volodymyr Kindratenko
  • Award year: 2020-2021
  • Funding source: NCSA Faculty Fellows

Solving Dairy Cattle Genetic Improvement Challenges using Deep Learning

Innovative AI tools will be developed to identify cattle that have the highest genetic potential for milk production and health status and make simplistic assumptions about the relationship between phenotypes and genotypes.

AIR POLLUTION PREDICTION USING TRAFFIC SURVEILLANCE CAMERA FOOTAGE AND DEEP LEARNING

Traffic-related air pollution is a major health burden in the United States; however, measuring pollution with traditional direct-measurement techniques at the required levels of granularity doesn’t scale well and has high demand in cost and labor. This project proposes creating a system using traffic camera footage and deep learning to predict traffic-related pollution concentrations. Initial results will help establish a larger project in cooperation with multiple metropolitan areas as a step toward a scalable system for hyperlocal pollution estimates.

  • Faculty Fellows: Mei Tessum (Agricultural and Biological Engineering), Christopher Tessum (Civil and Environmental Engineering)
  • NCSA Collaborators: Volodymyr Kindratenko and Dawei Mu
  • Award Year: 2021-2022 (academic year)
  • Funding source: NCSA Center Directed Discretionary Research program

TOWARD ULTRASOUND BRAIN IMAGING VIA FULL-WAVE ACOUSTIC SIMULATIONS AND DEEP LEARNING

Transcranial ultrasound could enable a broad variety of applications in brain imaging, including hemorrhage detection and stroke diagnosis, among others. Despite the great potential, it has not been widely used in adult brain imaging because their skulls cause severe phase aberration, leading to degraded ultrasound images. This project proposes using deep learning and a real-time pulse-echo ultrasound approach to estimate skull profile and speed of sound, allowing accurate skull aberration correction and establishing the feasibility of the proposed methods.

  • Faculty Fellow: Aiguo Han (Electrical and Computer Engineering)
  • NCSA Collaborator: Volodymyr Kindratenko
  • Year: 2021-2022 (academic year)
  • Funding source: NCSA Center Directed Discretionary Research program

USING MACHINE LEARNING TO PREDICT EYE MOVEMENTS IN SKILLED AND UNSKILLED READERS

Attaining reading proficiency is an issue for both adults and children. To understand the underlying processes of skilled reading, researchers track eye movements that gather visual information efficiently. Typically, people move their eyes while reading and can only clearly see 7-10 letters at a time. This project proposes developing a better deep learning model that integrates visual and linguistic data to predict eye movements. It will help inform reading interventions and education by giving more detailed profiles on skilled and unskilled readers.

  • Faculty Fellows: Jon Willits, Jessica Montag, Anastasia Stoops (Psychology)
  • NCSA Collaborators: Volodymyr Kindratenko, Eliu Huerta, Dawei Mu
  • Year: 2021-2022 (academic year)
  • Funding source: NCSA Center Directed Discretionary Research program

VOICE VITALS: NOVEL INFRASTRUCTURE FOR DISEASE SCREENING AND TREATMENT TRACKING

Many disease states, particularly in psychiatry, neurology, and cardiology, are often overlooked in our healthcare systems due to treatment barriers and untimely diagnoses. New disease screening methods are necessary to address these problems. This project proposes developing automated disease screening techniques that can infer clinical states, such as anxiety and manic depressive disorders, using machine learning, modeling, and human speech and language data. The team will integrate models with Clowder to demonstrate the automated annotation of speech/language/health data.

  • Faculty Fellow: Mary Pietrowicz (Applied Research Institute)
  • NCSA Collaborators: Chen Wang, Volodymyr Kindratenko, Colleen Bushell
  • Year: 2021-2022 (academic year)
  • Funding source: NCSA Center Directed Discretionary Research program