Quick Links:

C3.ai DTI Webpage

Events

Information on Call for Proposals

Proposal Matchmaking (Archive from 2020 CFP)

C3.ai DTI Training Materials

C3 Administration (password protected)

Have Questions? Please contact one of us:

• Jay Roloff, jayr@illinois.edu (Executive Director, c3.ai.DTI)
• R. Srikant, rsrikant@illinois.edu (Co-Director, c3.ai.DTI)
• Tandy Warnow, warnow@illinois.edu (Co-chief Scientist, c3.ai.DTI)

Announcing our Third Call for Proposals:

AI to Transform Cybersecurity and Secure Critical Infrastructure

It is anticipated that up to USD $10 million in Research Awards will be awarded from this Call for Proposals. Proposals can request funding of USD $100,000 to $1,000,000 for an initial period of one (1) year.

C3DTI will also make available up to USD $2 million in Azure Cloud computing resources, supercomputing resources at UIUC’s NCSA and LBNL’s NERSC, and free, unlimited access to the C3 AI Suite hosted on the Microsoft Azure Cloud. 

**** We will be offering a virtual session with information on the CFP including an opportunity to ask questions early in January. Please keep an eye out on you email and this wiki page for more information. ****

Proposals are Due February 7, 2022

Awards will be made in March 2022 with start dates of around June 1, 2022

Announcements

November 19, 3 pm CT

Tracking the Few and Far Between: Computational Strategies to Speed the Discovery of Low-frequency Genomic Variation in COVID-19

Nancy Amato, Head of the Department of Computer Science and Abel Bliss Professor of Engineering, University of Illinois at Urbana-Champaign

REGISTER FOR ZOOM WEBINAR

To date, the vast majority of COVID-19 genomic research has been focused on a high-level view of SARS-CoV-2 diversity, overlooking the diversity of the viral population that exists within each COVID-19 positive patient. While viral load of SARS-CoV-2 in an individual can exceed hundreds of thousands of copies, available genomic databases only contain a single consensus version of this diverse population, discarding any low-frequency mutations. The goal of our project, CoVariants, is to develop novel computational approaches to recover these discarded variants and allow for rapid characterization of within-host diversity of SARS-CoV-2 across tens of thousands of samples. Commonly used computational tools either are not designed for the detection of low-frequency variants within viral populations, or require significant computational resources per sample. In this talk, we will describe how new parallelization strategies and approximate statistical methods can reduce the computational requirements of a widely used existing approach by up to 400 percent while preserving 100 percent of the low-frequency genomic diversity. We will end our talk by highlighting how we plan to use these improved computational methods to provide insight into the biological underpinnings of SARS-CoV-2 transmissibility and severity compared to other coronaviruses.

Nancy M. Amato is Abel Bliss Professor and Department Head of Computer Science at the University of Illinois. She received undergraduate degrees in Mathematical Sciences and Economics from Stanford, and M.S. and Ph.D. degrees in Computer Science from UC Berkeley and the University of Illinois, respectively. Her research focuses on robotics motion planning, computational biology and geometry, and parallel computing. Amato received the 2019 IEEE RAS Leadership Award in Robotics and Automation, the 2014 CRA Habermann Award, and the inaugural NCWIT Harrold/Notkin Research and Graduate Mentoring Award in 2014. She is a Fellow of the AAAI, AAAS, ACM, and IEEE.

Lawrence Rauchwerger is a professor in the Department of Computer Science at the University of Illinois. Previously, he was the Eppright Professor of Computer Science and Engineering at Texas A&M University and co-Director of the Parasol Lab. He received an engineering degree from the Polytechnic Institute Bucharest, a M.S. in Electrical Engineering from Stanford University and a Ph.D. in Computer Science from the University of Illinois. His approach to auto-parallelization, thread-level speculation and parallel code development has influenced industrial products at corporations including IBM, Intel, and Sun. Rauchwerger is a Fellow of the AAAS and IEEE and has received an NSF CAREER Award, awards from IBM and Intel.

Quick Links:

C3.ai DTI Webpage

Events

Information on Call for Proposals

Proposal Matchmaking

C3.ai DTI Training Materials (password protected)

C3 Administration (password protected)

Have Questions? Please contact one of us:

• Jay Roloff, jayr@illinois.edu (Executive Director, c3.ai.DTI)
• R. Srikant, rsrikant@illinois.edu (Co-Director, c3.ai.DTI)
• Tandy Warnow, warnow@illinois.edu (Co-chief Scientist, c3.ai.DTI)