Nika Haghtalab

She works broadly on the theoretical aspects of machine learning and algorithmic economics. Classically, the outcome of a learning algorithm is considered in isolation from the effects that it may have on the process that generates the data or the party who is interested in learning. In today's world, increasingly more people and organizations interact with learning systems, making it necessary to consider these effects. Prof. Haghtalab's work builds theoretical foundations for ensuring both the performance of learning algorithms in presence of everyday societal and economic forces and the integrity of social and economic forces that are born out of the use of machine learning systems.

Addressing machine learning in this context calls for approaches that align the incentives and interests of the learners and other parties, are robust to the evolving social and economic needs, and promote equity. Prof. Haghtalab's work in machine learning, economics, and theory of computer science addresses emerging fields such as learning in economic and societal settings, collaborative learning, robustness of ML, fairness and privacy.

Previously, Prof. Haghtalab was an assistant professor in the CS department of Cornell University, in 2019-2020. Prior to that, she was a postdoctoral researcher at Microsoft Research, New England, in 2018-2019. She received her Ph.D. from the Computer Science Department of Carnegie Mellon University under the supervision of Avrim Blum and Ariel Procaccia. Her thesis titled Foundation of Machine Learning, by the People, for the People received the CMU School of Computer Science Dissertation Award (2018) and a SIGecom Dissertation Honorable Mention Award (2019).