I've just successfully completed my Ph.D.
in Computer Science
at the University of Texas at Austin.
This summer I will be moving to Georgetown, Texas to prepare for my new
job as an Assistant Professor of Computer Science at
which is also where I
received my undergraduate B.S. with a triple-major in Computer Science, Math, and German.
My dissertation advisor was Risto Miikkulainen of the
Neural Networks Research Group.
I'm interested in automatic discovery of complex multi-modal behavior, particularly in the domain of video games.
Agents that can behave in different manners in response to different situations are crucial for games because
they are so complex, and human players adapt so quickly. I'm particularly interested in the use of multiobjective
evolution and neuroevolution in these domains. Furthermore, I am interested in finding
domain-independent methods to solve these tasks, using tools such as fitness shaping.
The less expert knowledge, the better.
The first half of my dissertation research used the
BREVE simulation environment.
My source code is available here.
The ideas and code used in BREVE were extended in
the second half of my dissertation, which focuses on the domain of Ms. Pac-Man using the
Java implementation available here.
I've developed a software framework in Java for evolving complex, multimodal
behavior in this domain and others:
Modular Multiobjective NEAT,
or MM-NEAT, is an extension of the original
NEAT algorithm that adds
support for multiobjective evolution via NSGA-II, and modular neural
networks that have separate modules for separate output policies. The fitness-shaping
approach of Targeting Unachieved Goals (TUG),
first introduced in this paper, is also supported.