Projects

Funded Projects

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

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

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

Concluded Projects

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.

Investigators: Sandra Rodriguez-Zas (PI), Eliu Huerta (Co-PI)
Start date: July 1, 2020; End date: June 30, 2021
Award amount to date: $25,000
Funding source: Center for Digital Agriculture Seed Funding

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

TAMING AMPHOTERICIN B, THE ANTIFUNGAL DRUG OF LAST RESORT: MAKING IT SAFE AND EFFECTIVE

THIS PROJECT AIMS TO DEVELOP A NOVEL COMPUTATIONAL-EXPERIMENTAL WORKFLOW TO GUIDE SMALL MOLECULE DRUG DESIGN. THE TEAM WILL EMPLOY THE WEIGHTED ENSEMBLE ENHANCED SAMPLING APPROACH THAT USES A VAST NUMBER OF THE SHORT TRAJECTORIES TO SAMPLE THE ENERGY LANDSCAPE MORE EFFICIENTLY, DRIVEN BY A DEEP LEARNING FRAMEWORK.

Investigators: Taras Pogorelov (Faculty Affiliate) and Volodymyr Kindratenko
Year: 2021-2022 (academic year)
Funding source: NCSA Center Directed Discretionary Research program