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
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.
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 2013)
I am looking for postdocs and graduate
students to join my team at the Technion. 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.