#### At Google:

At Google, I work on things I'm mostly not allowed to talk about. Among other things, I work on infrastructure related to analysis of big data, for which I have a patent.
#### At Carnegie Mellon University

I am interested in applying learning to robotics applications. In particular,
I am interested in transfer learning, so that knowledge learned from one
problem can be applied to similar but new problems. This is particularly
important in robotics, because training experience is much more important (due
to imperfect models of the real world) while also being much harder to obtain
(compared to working with purely virtual entities, for example in simulators).

My work in transfer learning hinges on the concept of using feature-based state
representation. The fundamental problem of transfering knowledge is to represent
the knowledge in the right way. In order to transfer knowledge between
problems, the knowledge has to be represented in a way that's meaningfull
across problems, even if this representation is not necessarily useful for
getting high performance out of any single problem.

I have done some work of transfering knowledge in the context of dynamic
programming. In order to avoid the computational complexity of dynamic
programming, I am currently exploring different policy representations that use
discrete trajectories. This has been published at ICRA 2006. While this work
is in the Marble Maze domain, I have also expanded it to the Little Dog environment.

#### At McGill University

Before coming to CMU, I did a Bachelor's and
Master's degree at McGill
University. My Master's advisor was Doina
Precup and my master's thesis was called
"Automated Discovery of Options in Reinforcement Learning" (PDF).

Before working on my Master's degree, I also worked with
Prof. Tom Shultz and Francois Rivest in the Laboratory
for Natural and Simulated Cognition on a Java
Applet that implements and compares regular
Backpropagation neural networks with Cascade
Correlation neural networks.