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Funded Projects

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
  • Award Year: 2021-2022
  • 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: Eliu Huerta (PI), Volodymyr Kindratenko (Co-PI), Dan Katz (Co-PI)
  • Start date: October 1, 2019; End date: September 30, 2022
  • 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: Eliu Huerta (PI), Mark Neubauer (Co-PI), Volodymyr Kindratenko (Co-PI), Zhizhen Zhao (Co-PI)
  • Start date: September 1, 2019; End date: August 31, 2021
  • 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

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.