Quick Links:
Information on Call for Proposals
Proposal Matchmaking (Archive from 2020 CFP)
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)
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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
Colloquium on Digital Transformation ScienceTranslating Prior AI Research in Breast Cancer Imaging to Interrogate Thoracic Images of COVID-19
You are cordially invited to the launch of the Colloquium on Digital Transformation Science, Thursday, July 9, 1 pm PT/4 pm ET. Maryellen Giger, The University of Chicago's A.N. Pritzker Professor of Radiology, will give a lecture followed by a Q&A on her team's groundbreaking computational techniques for investigating medical images.Register here.
The COVID-19 pandemic presents a pressing public health need for computational techniques to augment the interpretation of medical images in their role for: surveillance and early detection of COVID-19 resurgence via monitoring of medical imaging data; detection, triaging, and differential diagnosis of COVID-19 patients; and prognosis, including prediction and monitoring of response, for use in patient management. While thoracic imaging, including chest radiography and computed tomography, are being re-examined for their role in patient management, the limitations for improved interpretation are partially due to the qualitative interpretation of the images. Professor Giger and her colleagues at University of Chicago and Argonne National Laboratory aim to develop machine intelligence methods to aid in the interrogation of medical images from COVID-19 patients. They draw on decades of AI development of medical images to quantify and explain the COVID-19 presentation on imaging, specifically through machine learning methods of interrogating cancer on multi-modality breast images for “virtual biopsies.”
Quick Links:
Information on Call for Proposals
Proposal Matchmaking Matchmaking
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)
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