Akhmed Umyarov

Akhmed Umyarov

Assistant Professor

Information & Decision Sciences

Education

Ph.D. 2010
Information Systems New York University. Stern School of Business.

Expertise

Data Mining, Experimental Design, Statistics, Social media, Big Data

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.

Selected Works

Bapna R., Umyarov A. Do your friends make you pay? A randomized field experiment. Management Science.

Bapna R., Ramaprasad J., Shmueli G., Umyarov A. OneWay Mirrors and WeakSignaling in Online Dating: A Randomized Field Experiment. Management Science.

Benson A., Sojourner A., Umyarov A. Can reputation discipline the gig economy? Experimental evidence from an online labor market (under review)

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., 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. Information Systems Research. (Revision)

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 variancebased 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