My research is at the interface of theoretical machine learning and economics: information elicitation, crowdsourcing, and markets. I also work in dynamical systems.
Before coming to Boulder, I was a postdoc at MSR-NYC, and then at Harvard's CRCS with Yiling Chen and Yaron Singer in the EconCS group. I completed my Ph.D. in theoretical computer science at Berkeley, advised by Christos Papadimitriou and funded by the NDSEG Fellowship.
- I am currently taking on students. If you have a strong math background and are interested in tackling exciting problems in algorithmic economics and theoretical machine learning, please apply. Come join our growing theory group!
- I attended the CCC's Computing Research: Addressing National Priorities and Societal Needs 2017, and left this video evidence
- I am advising the Colorado Data Science Team, and co-organizing the Statistics, Optimization, and Machine Learning Seminar
- I organized the EC 2017 Forecasting Workshop
- I taught Algorithmic Economics and ML in Spring 2017
- I co-organized the NIPS'16 Workshop on Crowdsourcing and Machine Learning (CrowdML)
- I co-chaired the SIGAI Career Network Conference (CNC) 2016 in Boston, MA
- Bo Waggoner and I gave an EC 2016 tutorial on elicitation and machine learning