I am an engineer and researcher who has studied how complex systems communicate and process information. I have done this across wide scales, from sub-micron regions of the brain to the largest social networks.

Currently I do not have a formal position at Princeton University, but I remain a member of the community. Previously I taught and did research at Princeton for a decade.

Much of my work at Princeton has focused on decision making. Much of this has related to how the brain’s reward system (which underpins decision-making) interacts with executive function. A subtext of this work is that reinforcement learning, or at least some biological approximation of it, is central to executive function and decision making.

We have published evidence that the brain’s valuation of objects or events may require attention. This finding suggests that methods such as surveys that may engage attention in non-ethological ways may have difficulty reading people’s in natura feelings about something.

My colleagues and I have studied how people optimize so-called multi-armed bandit problems, the age-old problem where one has finite resources to invest in multiple incompletely-understood opportunities (but will learn more about their payoffs in the future). A related, although scientifically difficult, line of research has been whether meditation improves executive function by tuning the objective function underpinning decision making. Thoughts about the scientific obstacles to doing high quality work in this area recently appeared in a paper that has attracted interest from the media.

I have worked on the problem of how to merge different forms of data. Our work on simultaneously-collected functional MRI (fMRI) and electroencephalographic (EEG) data is steeped in big data, big computation, and machine learning. This is substantially more complicated that joining database tables: Not only is one modality primarily spatial and one primarily temporal, these modalities physically interfere with each other. This remains a challenging area.

I have substantial industrial experience. I was a researcher at Microsoft Research, and was a Microsoft Program Manager for MSN/Windows Messenger during its heyday. More recently I have worked at several startups focused on data and AI in politics and fashion.

I have a bachelor’s degree from UCSD, where I focused my studies on Cognitive Science (a mix of machine learning, traditional AI, and neuroscience). My doctorate is from the University of Oregon, where I wrote a computational neuroscience dissertation.