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. Our major application area is power markets.
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 especially for mobile health, 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.
Open
Positions
(updated: September 2014)
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.