Abstract: Many optimization problems today are solved not by one computer but by a network of agents, each possessing only part of the data and communicating only with its neighbors. How fast can such a network optimize? And what limits that speed?

This talk explores distributed optimization through two complementary viewpoints. First, we show how continuous-time primal–dual dynamics can exhibit accelerated convergence through an exact energy conservation law, and why this acceleration may disappear after seemingly natural discretization. A lower-bound result reveals a fundamental limitation of a broad family of single-loop schemes and motivates algorithms that deliberately trade additional communication for acceleration. Second, we develop an accelerated gradient-tracking viewpoint in which the average network dynamics become an inexact version of Nesterov acceleration. Chebyshev filtering suppresses the resulting network error, leading to near-optimal convergence guarantees and robustness under suitable changes in network topology.

The broader theme is that in networked optimization, computation, communication, acceleration, and robustness cannot be designed independently. The talk will develop these ideas intuitively, with examples and geometric interpretations, before presenting the main theoretical insights.

Bio:  Mayank Baranwal is a Senior Scientist with the Tata Consultancy Services (TCS) research division in Mumbai. He also holds an Adjunct appointment with the Systems and Control group at the Indian Institute of Technology, Bombay (IITB). Before joining TCS, he was a postdoctoral scholar in the Department of Electrical and Computer Engineering at the University of Michigan, Ann Arbor. He received his Bachelor in Mechanical Engineering in 2011 from the Indian Institute of Technology, Kanpur (IITK), an MS in Mechanical Science and Engineering in 2014, an MS in Mathematics in 2015, and PhD in Mechanical Science and Engineering in 2018, all from the University of Illinois at Urbana-Champaign (UIUC). His research interests are modeling, optimization, control, and inference in network systems with applications to distributed optimization, supply-chain networks, power networks, control of microgrids, bioinformatics, computational biology, and deep learning theory. Mayank is a recipient of the Institute Silver Medal in 2011 (IIT Kanpur), the ME Outstanding Publication Award in 2017 (the University of Illinois), the Young Scientist Award in 2022 (Tata Consultancy Services), and the AI Research Award in 2024 (Nasscom AI). He is also a Young Associate with the Indian National Academy of Engineering (INAE). 

Abstract: Conventional active phased arrays rely on multiple power-hungry radio frequency (RF) chains, high-speed data converters, and complex analog phase shifters, making them very expensive. This talk will introduce Space-Time-Coding (STC) digital metasurfaces as a low-cost, highly scalable alternative. We will explore how applying temporal modulation directly at the carrier level allows for the dynamic manipulation of harmonic beams. Next, the talk will discuss a fundamental paradigm shift in transceiver design. It will demonstrate how STC metasurfaces can operate entirely in the wave domain to simultaneously embed data and break physical reciprocity for highly secure communications. 

Bio: Dr. Debidas Kundu is an Assistant Professor in the Department of Electrical Engineering at Indian Institute of Technology (IIT) Bombay. He received his Ph.D. degree from the Dept. of Electronics and Electrical Communication Engineering at the Indian Institute of Technology Kharagpur, India, in June 2018. He received the DST-INSPIRE Faculty Award and subsequently joined the ECE Department of IIT Roorkee as an INSPIRE Faculty member in January 2019. From April 2022 to April 2023, he worked as a visiting researcher (Post-Doc) at Carleton University, Canada. Prior to joining IIT Bombay, he was an Assistant Professor at Indian Institute of Technology Delhi. He is a recipient of the Young Scientist Award from URSI-RCRS and URSI-GASS in 2020 and 2023, respectively and DRDO Dare to Dream Award in 2026. His current research mainly includes analytical techniques in electromagnetics, metasurfaces, reconfigurable intelligent surfaces (RIS), electromagnetic wave scattering and polarization control, and microwave engineering. 

Abstract: Indian Rummy is a widely played imperfect-information card game with a game tree estimated to contain 10^{170} decision nodes. Unlike other card games such as Poker, Bridge, and Dou Dizhu, Indian Rummy is strongly shaped by meld structures---sets and sequences---and by continuously evolving hand states produced by sequential "pick" and "discard" actions. These properties make it difficult to directly apply standard abstraction and equilibrium-computation methods to Indian Rummy. The recurring decisions in two-player Indian Rummy are whether to play or drop, whether to pick from the open or closed pile, whether to declare, and which card to discard. Beyond individual games, tournament play also introduces long-horizon complexity because decision making must account for the current score and the number of games remaining.

We propose RUDRA (a Rummy Utility-DRiven Agent), which addresses the intractability of Indian Rummy by decomposing it into subproblems. RUDRA integrates four main elements.  First, we develop a high-performance C++ environment for simulating games, which includes a fast engine to compute optimal card groupings. Second, building on this engine, we introduce RUDRA-PS, a mixture-of-experts policy trained via policy search. Third, we introduce RUDRA-SIM, a simulation-based planner that combines Monte Carlo simulation with Bayesian opponent hand prediction and RUDRA-PS to evaluate candidate actions over plausible hidden states. Fourth, we introduce RUDRA-EID to compute the initial play/drop strategy in tournaments by solving a finite-horizon imperfect-information extensive-form game. Using learned hand-strength abstractions, the game is reduced from 10^{23} states to approximately 10^{8} decision nodes and solved by combining dynamic programming and linear programming.

RUDRA combines RUDRA-SIM for pick and discard actions with an RUDRA-EID for equilibrium initial drop strategy in the tournament. We validate it by playing against state-of-the-art baselines and also through controlled human evaluations on a custom-built online gameplay platform. In five separate 320-game tournaments against experienced Indian Rummy players, RUDRA won all five tournaments, achieving an average advantage of $1.96$ points per game. These results establish RUDRA as a state-of-the-art agent for Indian Rummy. We expect both the general approach of RUDRA (to decompose a massive imperfect-information game into strategically meaningful subproblems) and its specific modules (for optimisation, learning, inference, simulation, and abstraction) to benefit agent-development for large imperfect-information games.

This work is based on the Ph.D. thesis of Santhosh Kumar Guguloth. 

Bio: Shivaram Kalyanakrishnan is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Bombay. His research interests include artificial intelligence and machine learning, spanning topics such as sequential decision making, multi-agent learning, multi-armed bandits, and humanoid robotics. Kalyanakrishnan received a Ph.D. in computer science from the University of Texas at Austin. Subsequently, he was a Research Scientist at Yahoo Labs Bangalore and an INSPIRE Faculty Fellow at the Indian Institute of Science, Bengaluru. Kalyanakrishnan works on both theoretical and applied problems. He heads e-Yantra, a large-scale outreach programme that annually trains thousands of students in robotics

Abstract: Data centre networks require new transport protocols in view of the large bandwidth and low RTT in the network together with strict latency requirement for distributed compute. Low latency can be achieved by stall free scheduling between a sender receiver pair to ensure minimal buffering at the intermediate switches. The main technical challenge in such protocols is to establish large number of matched sender receiver pairs for concurrent message transfer. We propose a novel distributed bipartite matching algorithm with local information that improves the mean matching size in random bipartite graphs that model the data centre network communication.

Bio: Parimal Parag is currently an associate professor in department of electrical communication engineering at Indian Institute of Science at Bangalore. He was working as senior systems engineer in R&D at ASSIA Inc. from October 2011 to November 2014. He received his B. Tech. and M. Tech. degrees fromIndian Institute of Technology Madras in fall 2004; and the PhD degree from Texas A&M University in fall 2011. He was at Stanford University and Los Alamos National Laboratory, in autumn of 2010 and summer of 2007, respectively.

His research interests are in design, performance evaluation, and control of large distributed andnetworked intelligent systems applying mathematical tools from queueing theory, information theory, coding theory, and optimization methods.