In this lecture, I will describe a journey
through research, which started out with work on distributed systems,
continued through game theory to probability theory and decision theory,
moved to probabilistic graphical models and statistical learning, and then
gradually shifted to focus more and more on making sense of complex data in
realworld applications such as machine perception and systems biology. I
will talk about the common threads that connect these topics, and why this
seemingly random progression through research may make some sense after
all. I will also discuss some of the choices I made over time, and describe
some lessons I learned from my mentors, and lessons I learned from my
students.
