Emil is an an Assistant Professor of Marketing at Northeastern University. His research broadly focuses on AI, platforms, and labor markets, with particular emphasis on understanding how digital technologies reshape work, hiring, and economic opportunity. His current work examines the economics of digitization in labor markets, platform design for optimal matching, and the intersection of causal inference and machine learning in understanding worker behavior. He has worked on several application areas, including AI-assisted recruitment systems, online reputation mechanisms, digital labor platforms, algorithmic hiring processes, and technology interventions for career development and social mobility.
Education
- Postdoctoral Scholar, Graduate School of Business, Stanford University
- PhD Economics, Toulouse School of Economics
- M.Sc. Economics, Tilburg University
- B.Sc. Economics, Warsaw School of Economics
Selected Publications
- Vafa, K., Palikot, E., Du, T., Kanodia, A., Athey, S., & Blei, D. M. (2024). CAREER: A foundation model for labor sequence data. Transactions on Machine Learning Research. [OpenReview preprint]. https://openreview.net/forum?id=4i1MXH8Sle
- Palikot, E., & Pietola, M. (2023). Pay‐for‐delay with settlement externalities. The RAND Journal of Economics, 54(3), 387-415.
- Ivaldi, M., & Palikot, E. (2023). Sharing when stranger equals danger: Ridesharing during Covid-19 pandemic. Transport policy, 141, 221-231.
Selected Presentations
- NBER Summer Institute – Digital Economics, 2025
- Advances with Field Experiments, University of Chicago, 2025
- AI in Social Science Conference, University of Chicago, 2024 & 2025
- NBER Summer Institute – Labor Studies, 2024
- NBER Gender in the Economy Meeting, 2025
- CODE@MIT Conference, 2023
- Harvard Business School Seminar, 2023
- University of Michigan – Ross School of Business Seminar, 2024
- Platform Strategy Research Symposium, Boston University, 2023 & 2025
- Economics of Platforms (ECOP) Workshop, Barcelona, 2025