This project investigates fundamental techniques for building mathematical models that can be safely used to make trustworthy predictions and control decisions. Mathematical models form the foundation for modern Cyber-Physical Systems (CPS). Examples include vehicle models that predict how a car will move when brakes are applied, or physiological models that predict how the blood glucose levels change in a patient with type-1 diabetes when insulin is administered. The success of machine learning tools has yielded data-driven models such as neural networks. However, depending on how data is collected and the models are learned, it is possible to obtain models that violate fundamental physical, chemical, or physiological facts that can potentially threaten life and property. The approach of the project is to expose these model flaws through advanced analysis. The project seeks to broaden participation in computing through mentoring activities that will encourage undergraduate women and members of underrepresented minority groups to consider a career in research.
Abstract
Sriram Sankaranarayanan
<pre style="color: rgb(0, 0, 0); word-wrap: break-word; white-space: pre-wrap;">
Sriram Sankaranarayanan is an assistant professor of Computer Science
at the University of Colorado, Boulder. His research interests include
automatic techniques for reasoning about the behavior of computer and
cyber-physical systems. Sriram obtained a PhD in 2005 from Stanford
University where he was advised by Zohar Manna and Henny Sipma.
Subsequently he worked as a research staff member at NEC
research labs in Princeton, NJ. He has been on the faculty at CU
Boulder since 2009. Sriram has been the recipient of awards including
the President's Gold Medal from IIT Kharagpur (2000), Siebel
Scholarship (2005), the CAREER award from NSF (2009) and the Dean's
award for outstanding junior faculty for the College of Engineering at
CU Boulder (2012).</pre>
Performance Period: 10/01/2019 - 06/30/2024
Institution: University of Colorado at Boulder
Sponsor: NSF
Award Number: 1932189