Akhmed Umyarov received his PhD from New York University’s Stern School of Business in 2010. He also holds an M.Sc. in mathematics with the highest distinction from Moscow State University, Russia.
His general research theme focuses on causal identification of human behavior phenomena in online settings. He has done specific work in the context of peer influence inside music listening freemium communities as well as in the context of anonymity inside online dating communities that was published or presented in the leading national venues such as NBER and Management Science.
His industry experience includes serving as a quantitative researcher for Moody’s Corp. on Wall Street where he worked with the student loan default data, as a research and software engineer for Samsung Electronics in South Korea, and as a research engineer for Neurocom in Moscow, Russia.
Umyarov A., Tuzhilin A. Using External Aggregate Ratings for Improving Individual Recommendations. Journal of ACM Transactions on the Web. Volume 5, Number 1, February 2011, Section 3. Pages 1 – 40.
Bapna R., Umyarov A. Do your friends make you pay? A randomized field experiment. Management Science. Forthcoming.
Bapna R., Ramaprasad J., Shmueli G., Umyarov A. One-Way Mirrors and Weak-Signaling in Online Dating: A Randomized Field Experiment. Management Science (R&R)
Bapna R., Ramaprasad J., Umyarov A. Completing the Virtuous Cycle Between Paying for Music and Social Engagement in an Online Community: Evidence from a Randomized Trial. Information System Research Journal.(Under Review)
Umyarov A., Tuzhilin A. Improving Collaborative Filtering Recommendations Using External Data. Proceedings of the 2008 IEEE International Conference on Data Mining (ICDM). Pisa, Italy [acceptance rate: 9.7% = 70 / 724]
Umyarov A., Tuzhilin A. Improving rating estimation in recommender systems using aggregation- and variance-based hierarchical models. Proceedings of 2009 ACM Conference on Recommender Systems. New York, NY, USA [acceptance rate: 17.9% = 25 / 140 ]
Umyarov A., Tuzhilin A. Leveraging Aggregate Ratings for Better Recommendations. Proceedings of the 2007 ACM conference on Recommender Systems. Minneapolis, MN