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

• When are safety filters safe? On minimum phase conditions of control barrier functions

  ArXiv.org · 2025-08-11

  preprintOpen access

  In emerging control applications involving multiple and complex tasks, safety filters are gaining prominence as a modular approach to enforcing safety constraints. Among various methods, control barrier functions (CBFs) are widely used for designing safety filters due to their simplicity, imposing a single linear constraint on the control input at each state. In this work, we focus on the internal dynamics of systems governed by CBF-constrained control laws. Our key observation is that, although CBFs guarantee safety by enforcing state constraints, they can inadvertently be "unsafe" by causing the internal state to diverge. We investigate the conditions under which the full system state, including the internal state, can remain bounded under a CBF-based safety filter. Drawing inspiration from the input-output linearization literature, where boundedness is ensured by minimum phase conditions, we propose a new set of CBF minimum phase conditions tailored to the structure imposed by the CBF constraint. A critical distinction from the original minimum phase conditions is that the internal dynamics in our setting is driven by a nonnegative virtual control input, which reflects the enforcement of the safety constraint. We include a range of numerical examples, including single-input, multi-input, linear, and nonlinear systems, validating both our analysis and the necessity of the proposed CBF minimum phase conditions.

  PublisherOA PDFDOI

• On Finding Local Nash Equilibria (and only Local Nash Equilibria) in Zero-Sum Games

  ACM / IMS Journal of Data Science · 2025-05-13 · 2 citations

  articleOpen access

  We propose local symplectic surgery , a two-timescale procedure for finding local Nash equilibria in two-player zero-sum games. We first show that previous gradient-based algorithms cannot guarantee convergence to local Nash equilibria due to the existence of non-Nash stationary points. By taking advantage of the differential structure of the game, we construct an algorithm for which the local Nash equilibria are the only attracting fixed points. Further, we show that the algorithm exhibits no oscillatory behavior in neighborhoods of equilibria and that it has the same per-iteration complexity as other recently proposed algorithms. Furthermore, we give convergence rates in structured classes of zero-sum games. We conclude by validating the algorithm on two numerical examples: a toy example with multiple Nash equilibria and a non-Nash equilibrium, and the training of a small generative adversarial network (GAN).

  PublisherDOI

• Privacy-Preserving Mechanisms for Coordinating Airspace Usage in Advanced Air Mobility

  ACM Journal on Autonomous Transportation Systems · 2025-04-25

  articleOpen accessSenior author

  Advanced Air Mobility (AAM) operations are expected to transform air transportation while challenging current air traffic management practices. By introducing a novel market-based mechanism, we address the problem of on-demand allocation of capacity-constrained airspace to AAM vehicles with heterogeneous and private valuations. We model airspace and air infrastructure as a collection of contiguous regions (or sectors) with constraints on the number of vehicles that simultaneously enter, stay, or exit each region. Vehicles request access to airspace with trajectories spanning multiple regions at different times. We use the graph structure of our airspace model to formulate the allocation problem as a path allocation problem on a time-extended graph. To ensure that the cost information of AAM vehicles remains private, we introduce a novel mechanism that allocates each vehicle a budget of “air-credits” (an artificial currency) and anonymously charges prices for traversing the edges of the time-extended graph. We seek to compute a competitive equilibrium that ensures that: (i) capacity constraints are satisfied, (ii) a strictly positive resource price implies that the sector capacity is fully utilized, and (iii) the allocation is integral and optimal for each AAM vehicle given current prices, without requiring access to individual vehicle utilities. However, a competitive equilibrium with integral allocations may not always exist. We provide sufficient conditions for the existence and computation of a fractional-competitive equilibrium, where allocations can be fractional. Building on these theoretical insights, we propose a distributed, iterative, two-step algorithm that: (1) computes a fractional competitive equilibrium, and (2) derives an integral allocation from this equilibrium. We validate the effectiveness of our approach in allocating trajectories for the emerging urban air mobility service of drone delivery.

  PublisherDOI

• Adaptive Incentive Design With Learning Agents

  IEEE Transactions on Automatic Control · 2025-01-01

  articleSenior author

  We propose an adaptive incentive mechanism that learns the optimal incentives in environments where players continuously update their strategies. Our mechanism updates incentives based on each player's externality, defined as the difference between the player's marginal cost and the operator's marginal cost at each time step. The proposed mechanism updates the incentives on a slower timescale compared to the players' learning dynamics, resulting in a two-timescale coupled dynamical system. Notably, this mechanism is agnostic to the specific learning dynamics used by players to update their strategies. We show that any fixed point of this adaptive incentive mechanism corresponds to the optimal incentive mechanism, ensuring that the Nash equilibrium coincides with the socially optimal strategy. Additionally, we provide sufficient conditions under which the adaptive mechanism converges to a fixed point. Our results apply to both atomic and non-atomic games. To demonstrate the effectiveness of our proposed mechanism, we verify the convergence conditions in two practically relevant classes of games: atomic aggregative games and non-atomic routing games.

  PublisherDOI

• LATTE-MV: Learning to Anticipate Table Tennis Hits from Monocular Videos

  2025-06-10

  articleSenior author

  Physical agility is a necessary skill in competitive table tennis, but by no means sufficient. Champions excel in this fast-paced and highly dynamic environment by anticipating their opponent’s intent – buying themselves the necessary time to react. In this work, we take one step towards designing such an anticipatory agent. Previous works have developed systems capable of real-time table tennis gameplay, though they often do not leverage anticipation. Among the works that forecast opponent actions, their approaches are limited by dataset size and variety. Our paper contributes (1) a scalable system for reconstructing monocular video of table tennis matches in 3D and (2) an uncertainty-aware controller that anticipates opponent actions. We demonstrate in simulation that our policy improves the ball return rate against high-speed hits from 49.9% to 59.0% as compared to a baseline non-anticipatory policy. Project website: https://sastry-group.github.io/LATTE-MV/

  PublisherDOI

• Convergence of Decentralized Actor-Critic Algorithm in General-sum Markov Games

  arXiv (Cornell University) · 2024-09-06

  preprintOpen accessSenior author

  Markov games provide a powerful framework for modeling strategic multi-agent interactions in dynamic environments. Traditionally, convergence properties of decentralized learning algorithms in these settings have been established only for special cases, such as Markov zero-sum and potential games, which do not fully capture real-world interactions. In this paper, we address this gap by studying the asymptotic properties of learning algorithms in general-sum Markov games. In particular, we focus on a decentralized algorithm where each agent adopts an actor-critic learning dynamic with asynchronous step sizes. This decentralized approach enables agents to operate independently, without requiring knowledge of others' strategies or payoffs. We introduce the concept of a Markov Near-Potential Function (MNPF) and demonstrate that it serves as an approximate Lyapunov function for the policy updates in the decentralized learning dynamics, which allows us to characterize the convergent set of strategies. We further strengthen our result under specific regularity conditions and with finite Nash equilibria.

  PublisherOA PDFDOI

• Parameter Estimation in Optimal Tolling for Traffic Networks Under the Markovian Traffic Equilibrium

  arXiv (Cornell University) · 2024-09-29

  preprintOpen accessSenior author

  Tolling, or congestion pricing, has emerged as an effective tool for preventing gridlock in traffic systems. However, tolls are currently mostly designed on route-based traffic assignment models (TAM), which may be unrealistic and computationally expensive. Existing approaches also impractically assume that the central tolling authority can access latency function parameters that characterize the time required to traverse each network arc (edge), as well as the entropy parameter $β$ that characterizes commuters' stochastic arc-selection decisions on the network. To address these issues, this work formulates an online learning algorithm that simultaneously refines estimates of linear arc latency functions and entropy parameters in an arc-based TAM, while implementing tolls on each arc to induce equilibrium flows that minimize overall congestion on the network. We prove that our algorithm incurs regret upper bounded by $O(\sqrt{T} \ln(T) |\arcsMod| \max\{|\nodesMod| \ln(|\arcsMod|/|\nodesMod|), B \})$, where $T$ denotes the total iteration count, $|\arcsMod|$ and $|\nodesMod|$ denote the total number of arcs and nodes in the network, respectively, and $B$ describes the number of arcs required to construct an estimate of $β$ (usually $\ll |I|$). Finally, we present numerical results on simulated traffic networks that validate our theoretical contributions.

  PublisherOA PDFDOI

• Incentive-Compatible Vertiport Reservation in Advanced Air Mobility: An Auction-Based Approach

  arXiv (Cornell University) · 2024-03-27

  preprintOpen accessSenior author

  The rise of advanced air mobility (AAM) is expected to become a multibillion-dollar industry in the near future. Market-based mechanisms are touted to be an integral part of AAM operations, which comprise heterogeneous operators with private valuations. In this work, we study the problem of designing a mechanism to coordinate the movement of electric vertical take-off and landing (eVTOL) aircraft, operated by multiple operators each having heterogeneous valuations associated with their fleet, between vertiports, while enforcing the arrival, departure, and parking constraints at vertiports. Particularly, we propose an incentive-compatible and individually rational vertiport reservation mechanism that maximizes a social welfare metric, which encapsulates the objective of maximizing the overall valuations of all operators while minimizing the congestion at vertiports. Additionally, we improve the computational tractability of designing the reservation mechanism by proposing a mixed binary linear programming approach that leverages the network flow structure.

  PublisherOA PDFDOI

• Adaptive Incentive Design with Learning Agents

  arXiv (Cornell University) · 2024-05-26

  preprintOpen accessSenior author

  We propose an adaptive incentive mechanism that learns the optimal incentives in environments where players continuously update their strategies. Our mechanism updates incentives based on each player's externality, defined as the difference between the player's marginal cost and the operator's marginal cost at each time step. The proposed mechanism updates the incentives on a slower timescale compared to the players' learning dynamics, resulting in a two-timescale coupled dynamical system. Notably, this mechanism is agnostic to the specific learning dynamics used by players to update their strategies. We show that any fixed point of this adaptive incentive mechanism corresponds to the optimal incentive mechanism, ensuring that the Nash equilibrium coincides with the socially optimal strategy. Additionally, we provide sufficient conditions under which the adaptive mechanism converges to a fixed point. Our results apply to both atomic and non-atomic games. To demonstrate the effectiveness of our proposed mechanism, we verify the convergence conditions in two practically relevant classes of games: atomic aggregative games and non-atomic routing games.

  PublisherOA PDFDOI

• Parameter Estimation in Optimal Tolling for Traffic Networks Under the Markovian Traffic Equilibrium

  2024-07-10 · 1 citations

  articleSenior author

  Tolling, or congestion pricing, has emerged as an effective tool for preventing gridlock in traffic systems. However, tolls are currently mostly designed on route-based traffic assignment models (TAM), which may be unrealistic and computationally expensive. Existing approaches also impractically assume that the central tolling authority can access latency function parameters that characterize the time required to traverse each network arc (edge), as well as the entropy parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\beta$</tex> that characterizes commuters' stochastic arc-selection decisions on the network. To address these issues, this work formulates an online learning algorithm that simultaneously refines estimates of linear arc latency functions and entropy parameters in an arc-based TAM, while implementing tolls on each arc to induce equilibrium flows that minimize overall congestion on the network. We prove that our algorithm incurs regret upper bounded by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(\sqrt{T}\ln(T)\vert A\vert \text{maxf}\vert \overline{I}\vert \ln(\vert A\vert /\vert I\vert).\ B\})$</tex>, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> denotes the total iteration count, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\vert A\vert$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\vert I\vert$</tex> denote the total number of arcs and nodes in the network, respectively, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$B$</tex> describes the number of arcs required to construct an estimate of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\beta$</tex>. Finally, we present numerical results on simulated traffic networks that validate our theoretical contributions.

  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

• ITR: Foundations of Hybrid and Embedded Software Systems

  NSF · $13.2M · 2002–2010

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