I am a postdoctoral researcher in the computer science department at Duke University working with Vincent Conitzer. I'm motivated by practical problems at the intersection of learning, optimization, computation, and economics, specifically the design of markets (or mechanisms). As the world moves towards increasing automation, there are, more and more, opportunities to combine the wealth of data with a principled, provably optimal approach to mechanism design in order to make intractable problems tractable and impossible problems possible.
I have recently accepted a faculty position at the University of Virgina with appointments in Quantitative Analysis at the Darden School of Business and Systems and Information Engineering and Computer Science at the School of Engineering and Applied Sciences (SEAS). If you are a student and interested in working with me, please reach out. I am actively looking to recruit talented and motivated students!
For my near term research agenda, I am interested in three application areas where new technologies are allowing for transformational opportunities for mechanisms. First, the development of block chain based systems for distributed computation (e.g. Ethereum) will lead to an unprecedented ability to decentralize the allocation of resources. Second, the coming introduction of self-driving, connected vehicles will lead to a once in a generation opportunity to revamp traffic infrastructure. Third, the move to a "smart" grid for the distribution of electricity and intelligent appliances that can respond to pricing information will allow for a degree of sensitivity to incentives that, up to now, has been impossible in energy markets. I have been involved in research in each of these three areas. Please look at my publications for more information.
Longer term, I am very interested in machine learning questions in settings where the agents generating the data have incentives to strategically manipulate the data. This line of research requires combining traditional algorithms in machine learning with techniques from mechanism design to provably generate accurate data.