I am Associate Professor of Computer Science and Management at the Frankfurt School of Finance & Management. My research focuses on the intersection of data science and economics, with an emphasis on eliciting, aggregating, and evaluating crowd-sourced information.

Before joining Frankfurt School in August 2018, I was a Postdoctoral Fellow in the Institute for Machine Learning at ETH Zurich, working with Andreas Krause. From 2014-2015, I was a Postdoctoral Fellow in the Good Judgment Project at the University of Pennsylvania, working with Lyle Ungar, Barb Mellers, and Phil Tetlock. From 2010–2014, I was a Fellow of the School of Engineering and Applied Sciences at Harvard University, where I was working with David C. Parkes. I received my Ph.D. (2014) and Master’s (2009) degrees in Computer Science from Albert-Ludwigs-Universität Freiburg, Germany.

• I co-organize the DIMACS 2024 Workshop on Forecasting. Submit your best work (no published proceedings)!

• I got tenure!

• Our paper "An Equivalence Between Fair Division and Wagering Mechanisms" was accepted for publication in Management Science.

• Our paper "Crowd Prediction Systems: Markets, Polls, and Elite Forecasters" was accepted for publication in the International Journal of Forecasting.

• I will be an Area Chair for the 24th ACM Conference on Economics and Computation (EC'23).

• I am a member of the Virtual Institute on Data Economics hosted by Northwestern University.

• I co-organized Frankfurt School's inaugural Workshop on Artificial Intelligence and Business Analytics.

• An extended abstract of our paper "Crowd Prediction Systems: Markets, Polls, and Elite Forecasters" was accepted for publication in the 23rd ACM Conference on Economics and Computation (EC'22).

• Our paper "Incentive-Compatible Forecasting Competitions" was accepted for publication in Management Science.

• I will be an Area Chair for the "Crowdsourcing and Information Elicitation" area of the 23rd ACM Conference on Economics and Computation (EC'22).

• I gave a keynote talk at the ICML'20 Workshop on Incentives in Machine Learning! Check out the recording if you are interested.

• Our paper "Small steps to accuracy: Incremental belief updaters are better forecasters" was accepted for publication in Organizational Behavior and Human Decision Processes (OBHDP). For a popular press summary, see the Scientific American piece by my co-author Pavel Atanasov.

• An extended abstract of our paper "Small steps to accuracy: Incremental belief updaters are better forecasters" was accepted for publication in the 21st ACM Conference on Economics and Computation (EC'20).

• I have a personal website! (No news for you, I know.)