Savita Sastry

About

Professor Savita Sastry is associated with the Bronfenbrenner Center for Translational Research at Cornell University. The center assists faculty in developing translational research projects by providing support such as proposal preparation assistance, training, technical support, and help in brokering collaborative relationships. The center also offers workshops on translational research, an intensive summer institute, and talks on current research topics. While specific details about Professor Sastry's individual research focus or background are not provided in the page text, her affiliation with the center indicates her involvement in translational research efforts aimed at applying research findings to real-world issues, supporting faculty in gaining access to diverse research participants and unique data sets, and fostering collaborative research initiatives.

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Statistics
• Mathematics
• Mathematical optimization
• Algorithm

Selected publications

• Toward verified artificial intelligence

  Communications of the ACM · 2022 · 184 citations

  Senior authorCorresponding

  Making AI more trustworthy with a formal methods-based approach to AI system verification and validation.

  PublisherOA PDFDOI

• Quantifying the Utility--Privacy Tradeoff in the Internet of Things

  ACM Transactions on Cyber-Physical Systems · 2018-04-30 · 20 citations

  articleOpen accessSenior author

  The Internet of Things (IoT) promises many advantages in the control and monitoring of physical systems from both efficacy and efficiency perspectives. However, in the wrong hands, the data might pose a privacy threat. In this article, we consider the tradeoff between the operational value of data collected in the IoT and the privacy of consumers. We present a general framework for quantifying this tradeoff in the IoT, and focus on a smart grid application for a proof of concept. In particular,…

  PublisherOA PDFDOI

• Human-robot interaction for truck platooning using hierarchical dynamic games

  2019-06-01 · 19 citations

  article

  This paper proposes a controller design framework for autonomous truck platoons to ensure safe interaction with a human-driven car. The interaction is modelled as a hierarchical dynamic game, played between the human driver and the nearest truck in the platoon. The hierarchical decomposition is temporal with a high-fidelity tactical horizon predicting immediate interactions and a low-fidelity strategic horizon estimating long-horizon behaviour. The hierarchical approach enables feasible computat…

  PublisherDOI

• Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning

  arXiv (Cornell University) · 2019-09-12 · 18 citations

  preprintOpen accessSenior author

  While most approaches to the problem of Inverse Reinforcement Learning (IRL) focus on estimating a reward function that best explains an expert agent's policy or demonstrated behavior on a control task, it is often the case that such behavior is more succinctly represented by a simple reward combined with a set of hard constraints. In this setting, the agent is attempting to maximize cumulative rewards subject to these given constraints on their behavior. We reformulate the problem of IRL on Mar…

  PublisherOA PDFDOI

• Science of design for societal-scale cyber-physical systems: challenges and opportunities

  Cyber-Physical Systems · 2019-06-04 · 14 citations

  article

  Emerging industrial platforms such as the Internet of Things (IoT), Industrial Internet (II) in the US and Industrie 4.0 in Europe have tremendously accelerated the development of new generations of Cyber-Physical Systems (CPS) that integrate humans and human organizations (H-CPS) with physical and computation processes and extend to societal-scale systems such as traffic networks, electric grids, or networks of autonomous systems where control is dynamically shifted between humans and machines.…

  PublisherDOI

Recent grants

• CPS: Frontiers: Collaborative Research: Foundations of Resilient CybEr-Physical Systems (FORCES)

  NSF · $3.6M · 2013–2022

• Team for Research in Ubiquitous Secure Technology (TRUST)

  NSF · $40.8M · 2005–2018

• Collaborative Research: Transferable, Hierarchical, Expressive, Optimal, Robust, Interpretable Networks

  NSF · $1.0M · 2020–2025

Frequent coauthors

• John Lygeros

  ETH Zurich

  74 shared

• Wolf Kohn

  University of Washington

  50 shared

• Anil Nerode

  50 shared

• Gerhard Goos

  RWTH Aachen University

  49 shared

• Robert Kohn

  ARC Centre of Excellence for Mathematical and Statistical Frontiers

  49 shared

• J. D. Murray

  African Studies Centre

  49 shared

• Stephen Wiggins

  United States Naval Academy

  49 shared

• Jerrold E. Marsden

  49 shared

Education

• PHD, EECS

  University of California, Berkeley

Similar researchers at University of California, Berkeley

• Robert Sternbergh-168
• Sander Kerstenh-106
• Jenna Lih-85