Here you can find my CV.
My research focuses on predictive modeling of (and in) complex biological systems using real-world data. I am especially interested in methods for inferring the simplified models that are necessary for making good predictions when one has limited data and limited computational power.
Useful heuristics for such a task exist under the name of "model selection" in the field of statistics, and similar algorithms are likely at the heart of adaptive cognitive systems. Interesting questions include:
What must a monkey attend to in order to efficiently strategize in future conflicts?
What heuristics guide a brain that is learning to categorize high-dimensional input into predictive concepts?
How can a computer best build intuitions about how a gene network is likely to behave under varying conditions?
Specifically, I have worked on conflict in macaques, dynamics and statistics of cellular biochemical networks, and DNA conformational dynamics. I work mainly with computational and statistical methods (often with a physics flavor), which have included sparse coding, statistical model selection, maximum entropy models, continuous time sigmoidal networks, and "sloppy" models.