CDAC Announces Autumn 2019 Discovery Grant Projects

January 06, 2020

The Center for Data and Computing announced its Autumn 2019 class of Discovery Grants, funding eight projects and one convening that bridge disciplines and push forward the frontiers of data science and artificial intelligence. CDAC Discovery Grants provide risk-tolerant seed funding for innovative data science projects intended to achieve a clear impact on major scientific, scholarly, and societal questions.

This round of projects spans from children’s books to the Large Hadron Collider, from ancient cuneiform tablets to wearable devices that detect disease in sweat. The program will fund researchers in medicine, economics, archeology, molecular engineering, computer science, and several other disciplines, working together to tackle complex problems that spill beyond traditional academic boundaries. Though the applications are diverse, the projects overlap in their deployment of cutting-edge data and technological approaches, such as computer vision, machine learning, remote sensing, explainable AI, and predictive analytics.

This round of grant recipients includes UChicago CS faculty Gordon Kindlmann, Michael Maire, Yuxin Chen, Sanjay Krishnan, and Pedro Lopes.

Learn more about each grant recipient below. You can also read about the inaugural cohort of Discovery Grant recipients, awarded in early 2019. Our next call for Discovery Grant proposals will be announced in mid-2020.

Measuring Messages about Race and Gender: Evidence from Children’s Experience

Anjali Adukia, Harris Public Policy
Hakizumwami Birali Runesha, Research Computing Center

Learning How To Measure Scientific Images

Gordon Kindlmann, Computer Science
William Irvine, Physics

Automated Prostate Cancer Detection using Hybrid Multi-dimensional MRI and Deep Learning

Aritrick Chatterjee, Radiology
Aytekin Oto, Radiology
Michael Maire, Computer Science

Towards a Data-driven Trigger System for the Large Hadron Collider

Yuxin Chen, Computer Science
David Miller, Physics

Leveraging Machine Learning and Satellite Imaging to Reduce Oil and Gas Methane Emissions

Thomas Covert, Booth School of Business
Michael Greenstone, Economics

Computer-Assisted Diagnosis of Indeterminate Thyroid Lesions

Xavier Keutgen, Endocrine Surgery
Maryellen Giger, Radiology/Medical Physics
Peter Angelos, Endocrine Surgery
David Sarne, Endocrinology

DeepScribe: Deciphering Cuneiform with Artificial Intelligence

Sanjay Krishnan, Computer Science
Susanne Paulus, Assyriology
Sandra Schloen, OCHRE Data Service, Oriental Institute
Miller Prosser, OCHRE Data Service, Oriental Institute

Health Monitoring Based on Wearable Sweat Sensors

Pedro Lopes, Computer Science
Sihong Wang, Pritzker School of Molecular Engineering

An anvi’o workshop at the University of Chicago

A. Murat Eren, Medicine