ד"ר תומר גבע

Tomer Geva is a machine learning and data science researcher and a tenured senior lecturer at Tel-Aviv University’s Coller School of Management. His research focuses on developing data science methods for solving business problems, developing general-purpose methods to improve predictive accuracy, using large-scale online data for decision-making, and applying and developing machine learning, AI, and NLP methods to manage workers in crowdsourcing and organizational settings.

Tomer was the founder of the Business Data Science Program at Tel-Aviv University and led this program for six years. He serves as a senior editor for Decision Support Systems (DSS) journal and as an associate editor for MIS Quarterly (MISQ). Previously, he served as an associate editor for "Big Data" and "Decision Sciences" journals. Before joining Tel-Aviv University, Tomer was a visiting scholar at NYU Stern School of Business and a post-doctoral research scientist at Google.

Tomer’s research has been published in leading Data Science and Management journals, such as IEEE TKDE, Data Mining and Knowledge Discovery, Decision Support Systems, MIS Quarterly (MISQ), Information Systems Research (ISR), and Production and Operations Management (POMS). His work received generous funding from various foundations and companies, including the Israel Science Foundation (ISF), Marketing Science Institute (MSI), and Google.

Tomer industry experience includes extensive consulting, training, and “hands-on” development for high-tech companies, financial organizations, and the public sector in the fields of Machine Learning, AI, and Data Science. Before pursuing his Ph.D., Tomer held several engineering and management positions in the high-tech industry.

• Geva, Tomer, and Maytal Saar‐Tsechansky. "Who Is a Better Decision Maker? Data‐Driven Expert Ranking Under Unobserved Quality." Production and Operations Management 30, no. 1 (2021): 127-144.
   
• “Data-Driven Link Screening for Increasing Network Predictability.” Tomer Geva and Inbal Yahav”. IEEE Transactions on Knowledge and Data Engineering. 33(6): 2380 – 2391. (2021).
   
• Geva, Tomer, Maytal Saar-Tsechansky, and Harel Lustiger. "More for less: adaptive labeling payments in online labor markets." Data Mining and Knowledge Discovery 33, no. 6 (2019): 1625-1673.
   
• Geva, Tomer, Gal Oestreicher-Singer, Niv Efron, and Yair Shimshoni. "Using forum and search data for sales prediction of high-involvement products." MIS Quarterly 41 (1), 65-82 (2017).
   
• Brynjolfsson, Erik, Tomer Geva, and Shachar Reichman. "Crowd-squared: amplifying the predictive power of search trend data." MIS Quarterly 40 (4), 941-961. (2016).
   
• Dhar, Vasant, Tomer Geva, Gal Oestreicher-Singer, and Arun Sundararajan. "Prediction in economic networks." Information Systems Research 25, no. 2 (2014): 264-284.
   
• Geva, Tomer, and Jacob Zahavi. "Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news." Decision Support Systems 57 (2014): 212-223.