Learning algorithms mitigate impact of fraud on product rankings
Insights on how to design methods for uncertain environments to guarantee robustness in the face of manipulation.
Faculty
Negin Golrezaei is the KDD Career Development Professor in Communications and Technology and an Assistant Professor of Operations Management at the MIT Sloan School of Management.
Her current research interests are in the area of machine learning, statistical learning theory, mechanism design, and optimization algorithms with applications to revenue management, pricing, and online markets. Before joining MIT, Negin spent a year as a postdoctoral researcher at Google Research in New York where she worked with the Market Algorithm team to develop, design, and test new mechanisms and algorithms for online marketplaces.
Negin is the recipient of a 2018 Google Faculty Research Award; 2017 George B. Dantzig Dissertation Award; 2017 INFORMS Revenue Management and Pricing Section Dissertation Prize; 2018 Elwood S. Buffa Doctoral Dissertation Award from the Decision Sciences Institute; University of Southern California (USC) Ph.D. Achievement Award (2017); USC CAMS Graduate Student Prize, for excellence in research with a substantial mathematical component (2017); Honorable mention in the 2016 MSOM Student Paper Competition; and USC Provost's Ph.D. Fellowship (2011).
Negin received her BSc (2007) and MSc (2009) degrees in electrical engineering from the Sharif University of Technology, Iran, and a PhD (2017) in operations research from USC.
Beyhaghi, Hedyeh, Negin Golrezaei, Renato Paes Leme, Martin Pál, and Balasubramanian Sivan. Operations Research. Forthcoming. Online Appendix. arXiv Preprint.
Derakhshan, Mahsa, Negin Golrezaei, and Renato Paes Leme. Management Science. Forthcoming. arXiv Preprint.
Derakhshan, Mahsa, Negin Golrezaei, Vahideh Manshadi, and Vahab Mirrokni. Management Science. Forthcoming. SSRN Preprint.
Negin Golrezaei, Max Lin, Vahab Mirrokni, and Hamid Nazerzadeh. In The 2021 SIGKDD Conference, New York, NY: August 2021. Download Paper.
Chen, Qinyi, Negin Golrezaei, and Djallel Bouneffouf, MIT Sloan Working Paper 6524-21. Cambridge, MA: MIT Sloan School of Management, August 2021.
Golrezaei, Negin, and Evan Yao, MIT Sloan Working Paper 6523-21. Cambridge, MA: MIT Sloan School of Management, August 2021.
Online shopping is even more popular now because of the pandemic. Buyer beware: Product rankings can be based on fraudulent data like fake clicks, purchases, and reviews.
Insights on how to design methods for uncertain environments to guarantee robustness in the face of manipulation.
“...a lot of people are talking about using data to make decisions...an often- overlooked aspect of data is that it can't always be trusted."
Prof. Negin Golrezaei developed a search model that learns consumer preferences to optimize product rankings.