About Me

I have worked on a number of topics in different areas: computational learning theory, computation theory, inductive inference, philosophy of science, belief revision, game theory: foundations, algorithms, evolutionary analysis, and machine learning for particle physics. The aim of this note is a high-level, informal introduction to my work that does not assume background in computer science. For detailed descriptions, you can look at the annotations on my publications. See also the thesis topics I've listed for graduate students.

Biography

I was born in Toronto. My family moved to Germany when I was six. After finishing high school there, I came back to the University of Toronto to do a Bachelor's Degree in Cognitive Science. The Cognitive Science program in Toronto combines computer science, philosophy and psychology. For my Ph.D. in Logic and Computation, I went to the philosophy department at Carnegie Mellon University, in Pittsburgh, Pennsylvania. Carnegie Mellon is a private university founded by Andrew Carnegie.

Research Interests

My work is mostly concerned with an issue that receives different formulations and labels in different areas: how to use observations to adapt to an external world. In computer science, we talk about machine learning and optimal design for learning systems. In philosophy of science, this leads to questions about scientific reasoning and method. Epistemology considers how to form beliefs on the basis of observations, especially inductive generalizations. In biology, the topic is adaptation. Mostly I've worked in the context of machine learning, scientific reasoning and epistemology, which aim to find optimal ways of learning and adapting, rather than to describe how humans or animals actually do learn. My guiding principle is that good reasoning methods or algorithms are those that lead us towards our cognitive aims, especially towards theories that get the observations right. Though not everybody agrees with this, it's hardly a new idea. What's new in my work is a systematic attempt to work out the details. For example, what are relevant cognitive goals? What are the most powerful reasoning methods like? How hard can it be to attain some goal? Are some learning aims more difficult to realize than others? Which empirical questions are easy, which are hard, which are impossible, and what makes them so? Together with several other people working on these questions, we have found some precise, systematic and often surprising answers.

In considering the virtues and vices of learning methods, I often resort to general principles of rational choice. The idea is to think of an inference-method as something that you can choose, and you can apply rational choice principles to help you figure out how to do so. After applying ideas from the analysis of rational choice, I've become interested in the foundations of that theory. I've also worked on methods for representing rational choice models in logic and using methods from computational logic to carry out game theoretic reasoning.

For the last five years or so, I have been working on learning for relational data. The structure of relational data is best represented in logic, so learning with such data raises the question of how to combine logic and probability. Logic and probability are the two most powerful formal theories of reasoning that our civilization has developed so far, so combining them is an important foundational topic for Artificial Intelligence and Cognitive Science. It is also a very practical problem because most enterprises actually store their data in relational databases, and because networks generate linked data.