Scholar
Nika Haghtalab
Google Scholar ID: C6pnolkAAAAJ
University of California, Berkeley
Citations & Impact
Citations
3,234
H-index
26
i10-index
43
Publications
20
Co-authors
25
list available
Publications
Resume (English only)
Academic Achievements
- Sloan Fellowship (2024)
- Schmidt Sciences AI2050 Award
- NSF CAREER Award (2022)
- Google Research Scholar Award (2023)
- Best Paper Awards at NeurIPS and ICAPS
- Exemplary Track Paper Awards at EC
- Multiple industry awards and fellowships
- Publications in top venues including EC 2025, ICML 2025, SODA 2025
- Preprints on topics such as sample-adaptivity tradeoffs, strategic deletion, algorithmic content selection, knowledge injection via finetuning, surjectivity of neural networks, distortion in human feedback learning, emergent communication, and panprediction
Education
- Ph.D. in Computer Science from Carnegie Mellon University
- Co-advised by Avrim Blum and Ariel Procaccia
- Dissertation: 'Foundation of Machine Learning, by the People, for the People'
- Recipient of CMU School of Computer Science Dissertation Award (2018)
- SIGecom Dissertation Honorable Mention Award (2019)
Background
- Assistant Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley
- Co-director of CLIMB (Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley)
- Member of BAIR Lab and Theory Group at UC Berkeley
- Works on interdisciplinary problems at the intersection of machine learning, algorithms, economics, and society
- Develops mathematical foundations for learning and decision-making systems under economic and societal influences
- Focus areas include collaborative/federated learning, learning in markets, incentive-aware and robust learning, and foundational ML theory
