I am an assistant professor in the Statistics & Data Science department at Yale University. I study the manifestation of social and economic biases in our online lives via the algorithms that encode and perpetuate them. My research leverages both experimental and theoretical approaches, and my work spans multiple disciplines including data science, machine learning, fairness in socio-technical systems and algorithm design.
At Yale I co-founded the Computation and Society Initiative.
- FAT* 2020 PC Co-Chair.
- LatinX in AI @ ICML 2019 Executive Chair
- Women in Big Data 2019 General Co-Chair
- Best Paper at FAT* 2019
- Teaching Data Science Ethics in Spring 2019 at Yale, follow along on our course Blog!
SELECTED PRESS & OUTREACH:
- For my papers, demos, code, etc. related to my work on fairness, visit: Controlling bias in AI
- TEDx talk on “Should you trust what AI says?”
- Implementing Fair Elections in Switzerland [radio]
- Video shorts on algorithmic bias: [video1], [video2], [video3]
- Controlling Polarization
- An Algorithmic Framework to Control Bias in Bandit-based Personalization
Elisa Celis, Sayash Kapoor, Farnood Salehi and Nisheeth K. Vishnoi
BEST PAPER AWARD
Fairness Accountability and Transparency Conference (FAT*), 2019
- Fair and Diverse DPP-based Data Summarization
Elisa Celis, Vijay Keswani, Damian Straszak, Amit Deshpande, Tarun Kathuria and Nisheeth K. Vishnoi
International Conference on Machine Learning (ICML), 2018
- Coordinate Descent with Bandit Sampling
Farnood Salehi, Patrick Thiran and Elisa Celis
Advances in Neural Information Processing Systems (NeurIPS), 2018.
- Buy-it-now or Take-a-chance: Price Discrimination through Randomized Auctions
Elisa Celis, Greg Lewis, Markus Mobius and Hamid Nazerzadeh
Management Science, 2014
Full list of publications.