ד"ר תומר גבע

Tomer Geva is an Associate Professor at Tel Aviv University’s Coller School of Management and a researcher in AI, machine learning, and data science. His research focuses on developing data science methods to address business challenges, creating general-purpose AI and machine learning techniques to improve predictive accuracy, leveraging large-scale online data for decision-making, developing methods to assess human decision-making using AI, and on leveraging human judgements, data, and evaluations, to enhance AI and Generative AI.

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

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

Tomer’s industry experience includes extensive consulting, training, and hands-on development for high-tech companies, financial organizations, and the public sector in machine learning, AI, and data science. Before pursuing his Ph.D., Tomer held engineering and management positions in the high-tech industry.

• Dong, Wanxue, Maytal Saar-Tsechansky, and Tomer Geva. "A Machine Learning Framework for Assessing Experts’ Decision Quality." Management Science (2024).
   
• 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.
   
• Geva, Tomer, and Inbal Yahav. "Data-Driven Link Screening for Increasing Network Predictability." IEEE Transactions on Knowledge & Data Engineering 33.06 (2021): 2380-2391.
   
• 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.