I am an assistant professor in the Department of Electrical and Computer Engineering at Texas A&M University. I work in the areas of statistical learning theory, stochastic control and game theory, with a focus on problems in cyber-physical systems, intelligent transportation systems and renewable energy systems. Before joining TAMU, I was a postdoctoral researcher in the EECS department at UC Berkeley, working with Prof. Pravin Varaiya and Prof. Kameshwar Poolla. I received my PhD from University of Southern California (USC) in 2014, working with Prof. Rahul Jain.
I am looking for motivated graduate students to join my research group! Prospective students may have an undergraduate or Master’s degree in electrical engineering, computer science, mechanical engineering, industrial engineering, mathematics, statistics, or a related area. I am especially looking for students with a strong background in mathematics, to work on some interesting research problems in statistical learning and control theory. I also have some interesting projects on data-driven learning and optimization.
If you are interested, please send me an email with your CV.
Convergence of dramatic increase in the available data and processing power, enabled by ubiquitous sensing and computing capabilities is rapidly changing engineered systems. Cyber-Physical Systems (CPS) refers to such systems with tightly integrated computational, control and physical capabilities, like transportation networks, smart cities, autonomous vehicles, sensor networks, power grids and healthcare systems. However, designing and implementing CPS involve an array of complex and challenging tasks: learning and making inference from data, designing scalable optimization and control methods, and developing decentralized and adaptive decision making algorithms.
My research aims to address these challenging questions in the context of cyber-physical systems, while contributing to the fields such as statistical learning, control theory and game theory. I am especially interested in many applications in the areas of multi-agent systems, intelligent transportation systems and renewable energy systems.
Some specific topics are:
Theory: Sequential Learning, Online Convex Optimization, Multi-Armed Bandits, Reinforcement Learning, Recurrent Neural Networks.
Applications: I am interested in developing decentralized learning algorithms for multi-agent systems, motivated by the problems in robotics and UAVs. I am also working on developing data-driven learning and optimization algorithms for intelligent transportation systems. For example, how do we exploit the historical data and high-resolution real time data to predict future traffic flow and signal timings? I use machine learning techniques to develop scalable learning and prediction algorithms in this context. These algorithms can be used to develop traffic control and routing protocols in transportation networks, to optimize the throughput and reduce queues. I am also interested in using tools from learning theory to model consumer behavior and preferences, especially in the context of energy systems and online markets.
Theory: Markov Decision Processes, Approximate Dynamic Programming, Decentralized Control, Optimal Control.
Applications: How do we systematically design a scalable distributed control architecture that will give provable stability, robustness and high performance in the presence of uncertainty and stochasticity? This is the fundamental question that I am interested in. In particular, I am working on developing decentralized control algorithms for multi-agent systems, motivated by the problems in robotics and UAVs. I am also interested in control problems in the context of intelligent transportation systems. Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and Vehicle-to-Cloud (V2C) communication capabilities have the potential to dramatically change the transportation systems operation in terms of improving road safety, alleviating traffic congestion and reducing fuel consumption. However, this requires innovative (decentralized) control design coupled with scalable learning and prediction algorithms. Some of my recent works are in this context.
Theory: Stochastic Games, Mechanism Design, Learning in Games
Applications: My recent interest is in sharing economy models. In my past works, I have analyzed sharing economy models in the context of electricity networks. I plan to continue my work on sharing economy and extend it to other important application areas. Mechanism design for renewable energy systems, especially for demand response, is another area that I am interested in.
Dileep Kalathil, Ram Rajagopal, “Learning Demand Response with Bandits”, for submission to IEEE Transactions on Smart Grids, August, 2017. PDF
Deepan Muthirayan, Dileep Kalathil, Kameshwar Poolla, Pravin Varaiya, “Mechanism Design for Self-Reporting Baselines in Demand Response”, for submission to IEEE Transactions on Smart Grids, August, 2017. PDF
Dileep Kalathil, Deepan Muthirayan, Dai Wang, Kameshwar Poolla, Pravin Varaiya, “Selling Demand Response Using Options”, for submission to IEEE Transactions on Smart Grid, August, 2017. PDF
Jonathan Mather, Enrique Baeyens, Dileep Kalathil, Kameshwar Poolla, “The Geometry of Locational Marginal Prices”, for submission to IEEE Transactions on Power Systems, August, 2017. PDF
Dileep Kalathil, Chenye Wu, Kameshwar Poolla, Pravin Varaiya, “Sharing Economy for the Smart Grid”, accepted to IEEE Transactions on Smart Grids, August, 2017. PDF
Naumaan Nayyar, Dileep Kalathil, Rahul Jain, “On Regret-Optimal Learning in Decentralized Multi-player Multi-armed Bandits”, IEEE Transactions on Control of Network Systems, December, 2016. PDF
Naumaan Nayyar, Dileep Kalathil, Rahul Jain, “Optimal Decentralized Control with Asymmetric One-Step Delayed Information Sharing”, IEEE Transactions on Control of Network Systems, December, 2016. PDF
Dileep Kalathil, Vivek Borkar, Rahul Jain, “Approachability in Stackelberg Stochastic Games with Vector Costs”, Dynamic Games and Applications, 7(3):422-42, September, 2017. PDF
(Authors are in the alphabetical order) William Haskel, Rahul Jain, Dileep Kalathil, “Empirical Dynamic Programming”, Mathematics of Operations Research, 41(2):402 - 429, January, 2016. PDF
Dileep Kalathil, Naumaan Nayyar, Rahul Jain, “Decentralized Learning for Multi-Player Multi-Armed Bandits”, IEEE Transactions on Information Theory, 60(4):2331-2345, April, 2014. PDF
Dileep Kalathil, Rahul Jain, “Spectrum Sharing through Contracts for Cognitive Radios”, IEEE Transactions on Mobile Computing, 12(10):1999-2011, October, 2013. PDF
Please check my CV.