Announcements

• Colloquium on Digital Transformation Science

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  July 30, 3 pm CT Networked Epidemiology Models for Translating 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.”  Analysis and Control Carolyn Beck, Professor of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign; Tamer Ba?ar, the Swanlund Endowed Chair and CAS Professor of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign; and Rebecca Smith, Associate Professor, Department of Pathobiology, University of Illinois College of Veterinary Medicine REGISTER FOR ZOOM WEBINAR Spread of epidemics over large populations has been an important research area for several centuries, studied by epidemiologists, statisticians, mathematicians, and more recently data scientists. Over the past eight months or so, the science of epidemics has accelerated at an exponential rate due to the global threat caused by COVID-19. In addition, for quite some time, mathematical models of epidemics have been developed to help predict spread and inform policy makers as to what types of containment measures might be effective.  In this lecture, Carolyn Beck, Tamer Ba?ar, and Rebecca Smith will introduce several mathematical models within a networking (graph-theoretic) framework and discuss their work as well as others’ in: analyzing stability (or instability) of the equilibrium states (endemic and disease-free equilibria); optimally determining curing rates (through antidote control techniques); optimally modifying the network structure so as to mitigate spread; and developing algorithms to assimilate real- time testing data into networked epidemiological models. The speakers will discuss the plans of their project team—also comprised of Prashant Mehta (PI) and Matthew West of the University of Illinois at Urbana-Champaign and Philip E. Paré of Purdue University—in the development of models, algorithms, and software tools to support the state-level PCR (polymerase chain reaction) and serological testing efforts.

  
  

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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)