Date and Venue: 28-29 Aug 2026, at the SysCon Seminar Room 104, IIT Bombay
Ashish R. Hota, IIT Kharagpur
Controlling the spread of infectious diseases is a complex challenge that depends heavily on human behavior. When individuals make self-interested choices about adopting costly, partially effective protection, a fundamental tension arises between individual incentives and public health goals. This seminar explores the intersection of epidemiological dynamics, game theory, and information design to model and optimize these behavioral responses within a Susceptible-Infected-Susceptible (SIS) framework. We begin by examining how protection decisions evolve when individuals possess full information, and characterize equilibria of the coupled dynamics of epidemic states and behavioral strategies. We then transition to the more realistic scenario of incomplete information, where individuals must choose whether to protect themselves without knowing their true infection status (e.g., due to asymptomatic transmission). Within this context, we analyze how a principal can use noisy signals and action recommendations to guide rational, utility-maximizing agents towards reducing infection prevalence. Finally, we compare the efficacy of static versus dynamic signaling schemes along an epidemic trajectory, identifying conditions where partial information disclosure paradoxically outperforms full transparency. In summary, this talk provides a comprehensive overview of how strategic signaling can act as a powerful, non-pharmaceutical tool to minimize disease prevalence, while underscoring the delicate balance required when managing strategic populations under uncertainty.
Ashish R. Hota received the B.Tech. and M.Tech. (dual degree) degrees in Electrical Engineering from the Indian Institute of Technology (IIT) Kharagpur, Kharagpur, India, in 2012, and the Ph.D. degree in Electrical and Computer Engineering from Purdue University, West Lafayette, IN, USA, in 2017. He is currently an Associate Professor with the Department of Electrical Engineering, IIT Kharagpur. His research interests include areas of game theory, stochastic optimization, and control of network systems.
Presentation slides will be available after the talk.
Bharadwaj Satchidanandan, IIT Madras
We consider physical plants controlled by multiple actuators and sensors communicating over a network, where some sensors could be "malicious." A malicious sensor may not report the measurement that it observes truthfully. We address the problem of detecting malicious sensors in a system. We propose a general technique, called "Dynamic Watermarking,'' by which honest actuators in the system can detect the actions of malicious sensors, and disable closed-loop control based on their information
Bharadwaj Satchidanandan is an assistant professor of Electrical Engineering at Indian Institute of Technology Madras. He received his PhD from Texas A&M University where he was advised by Prof. P. R. Kumar, and was a postdoc at Massachusetts Institute of Technology where he was hosted by Prof. Munther Dahleh. His current research interests include Cyber-Physical Systems and their security, power systems, renewable energy integration, mechanism design, and unmanned aerial systems.
Presentation slides will be available after the talk.
Nilabha Saha, IIT Bombay
We propose an online, data-driven test for detecting adversarial actuator attacks in networked cyber-physical systems modeled as finite-state Markov decision processes (MDPs). The test exploits the ergodicity of the nominal closed-loop MDP: it compares, in total-variation distance, the nominal transition kernel against an empirical occupation measure built from a sliding window of observed state data. The scheme is agnostic to the attack mechanism, assuming only that the adversary's policy is stationary, randomised, and Markovian, and it requires no model of how the attack is carried out. Our main theoretical result is a closed-form detection threshold, governed by the pseudo-spectral gap of the nominal chain, together with a high-probability false-alarm bound obtained from concentration inequalities for Markov chains. We validate the test on a two-queue coupled MDP across a range of adversaries, including denial-of-service and stealthy parameter-drift attacks that fall outside the Markovian assumption, and we characterize the detection boundary as a function of attack intensity and observation-window length. The experiments also expose a structural limit of the approach: attacks that perturb only a small fraction of the state space can stay below the natural fluctuations of the global occupation measure and evade detection.
Nilabha Saha recently completed an integrated dual degree at IIT Bombay, earning a B.Tech. with Honours in Computer Science and Engineering and an M.Sc. in Mathematics, along with minors in Applied Statistics and Informatics and in Physics. He received the Institute Silver Medal as the top-ranking student in his graduating Mathematics batch. His research interests lie broadly in cryptography and pseudorandomness, with further interests in arithmetic geometry. He has held research internships in symmetric-key cryptanalysis at EPFL's LASEC and on the cryptanalysis of the extension-field Legendre PRF at KU Leuven's COSIC group. The work presented here, on detecting adversarial attacks in networked Markov decision processes, was carried out in collaboration with Souvik Das, Prof. Debasish Chatterjee, and Prof. Parthanil Roy. He will join Hudson River Trading, Singapore, as an Algo Engineer in September 2026.
Presentation slides will be available after the talk.
A Trust-Budget Perspective on Scheduled Misbehavior in Resilient Consensus
Priyank Srivastava, IIT Delhi
Consensus algorithms let a network of agents agree on a value using only local interactions, and are the building blocks of distributed estimation, coordination, and control. However, a single persistently misbehaving agent can drive the network to any value it chooses, and classical defenses, which operate on transmitted data alone, tolerate malicious agents only up to roughly half the network's connectivity. Recent work on trust-based resilient consensus relaxes this restriction and proves convergence and correct classification, using the fact that in cyberphysical systems, physics-based measurements such as wireless fingerprints yield trust observations that an adversary cannot forge.
In this talk, we consider an adversary that stops trying to defeat these trust measurements and instead only chooses when to misbehave, sending nothing an honest agent would not, and we analyze the permissible misbehavior through a trust budget. We start with an ideal noiseless setting and show that for each of these defenses the budget grows without bound, enabling the adversary to inflict finite but permanent damage without any sustained misbehavior. We conclude with the effect of measurement noise in typical operation as well as the worst case.
Priyank Srivastava is an Assistant Professor of Electrical Engineering at IIT Delhi, working on distributed algorithms, optimization, and resilience in networked control systems.
Presentation slides will be available after the talk.
Data assimilation: where data science meets dynamical systems
Amit Apte, IISER Pune
The earth is a complex, chaotic, spatio-temporally multiscale, high (infinite) dimensional dynamical system. Our models and the observational data of the earth reflect these characteristics. Data assimilation is the science, or the art, of combining data with models in order to estimate the past and future state (e.g. weather / climate prediction) along with the uncertainty in this estimate. Starting with the Bayesian formulation of data assimilation, I will identify the key mathematical challenges resulting from the inherent nonlinearities and high-dimensionality of the problem. I will discuss our work on quantifying the role of dynamical instabilities and connections with ideas from control theory, notably nonlinear observability.
Amit Apte is an applied mathematician working with research interests in dynamical systems, data assimilation problems in earth sciences, and most recently, dynamics of the Indian summer monsoon. Currently he is Professor and Chair of the Department of Data Science at IISER Pune, before which he was a faculty member at the International Centre for Theoretical Sciences (ICTS-TIFR) and the Centre For Applicable Mathematics (TIFR-CAM). He obtained his PhD at the University of Texas at Austin and was a postdoctoral fellow at The Statistical and Applied Mathematical Sciences Institute (SAMSI), UNC-Chapel Hill, and Mathematical Sciences Research Institute (MSRI).
Presentation slides will be available after the talk.