Research

Scientific objectives

Understanding how to act and make decisions in dynamic, complex and uncertain environments that often contain multiple agents and how to model and understand high dimensional phenomena in general and dynamics in particular. I also like to see things work in the real world. This leads to focusing on:

  • High dimensional statistics and learning
  • Uncertainty and risk in decision making
  • Learning and modeling dynamics from data
  • Systems that include multiple decision makers: Multi-agent/distributed/many players/adaptive systems See my publications page for more details.

 

See my Publications page for more details.

 

More specific research interests

  • Machine Learning (theory, algorithms, and applications). High-dimensional problems with uncertainty in the data and modeling and learning dynamics (e.g., networks).
  • Reinforcement Learning and Markov decision processes. Theory and application of Markov decision processes. I have worked quite a bit on adaptive control and learning algorithms for (large) stochastic systems in what is known as reinforcement learning.
  • Learning, optimization and control under uncertainty. Robust and stochastic optimization and statistical analysis of such approaches.
  • Games. Stochastic, dynamic, network, and differential games; applications in networks and resource sharing.
  • Multi-agent systems. Especially learning in such systems (e.g., online learning and learning in games). The goal here is to design economic systems (e.g., markets) where equilibrium is also a good social outcome.
  • Optimization of large scale problems. Especially combinatorial optimization using heuristic and statistical methods (e.g., the Cross Entropy method) and stochastic optimization.
  • Power Grid. Especially in reliability, pricing, and decision making in large-scale power grids (smart grids). My approach is very much data-driven: I try to understand the actual dynamics of the grid so that I can propose concrete policies for control of the grid, as well as evaluate market mechanisms and anomalies. See, for example, the EU funded GARPUR project that looks at probabilistic reliability models for large-scale grids.
  • Applications. I am interested and have worked (i.e., got to a semi-commercial prototype at least or plan to) on the following eclectic list of applications: large-scale communication network optimization, power management for laptops, adaptive compression of large data bases, a learning agent for combat planes simulator, cognitive radio networks, human activity recognition and context identification on mobiles, stochastic approaches to decoding of LDPC codes: theory, dynamics and hardware implementation. All the above applications share the following: big, hard optimization problems with uncertainty that call for statistical tools and stochastic analysis. Many of these problems have a multi-agent flavor as well that requires a game-theoretic analysis. Recently, I have become more and more involved in smart grids and reliability of large-scale power grids.

 

Open Positions (updated: September 2015)

  • I am looking for a postdoc and several graduate students to join my team. Please consider that working with me requires very strong mathematical skills and/or true hacking capabilities. Email me your resume and a brief explanation of what you want to do if you are interested.
  • I am looking for EE/CS/Math undergrads who are either mathematically strong or programming wizards for some very cool projects in mobile phone (Android) programming.