Elisa Celis

Assistant Professor of Statistics and Data Science

Elisa Celis’ research focuses on problems that arise at the interface of computation and machine learning and its societal and economic ramifications. She approaches these problems by using both experimental and theoretical techniques. Her work spans multiple areas including social computing and crowdsourcing, data science, and algorithm design with a current emphasis on fairness and diversity in artificial intelligence and machine learning. Her recent works include methodologies for debiasing data, approaches to produce diverse ranking and recommendation systems, mechanisms to avoid discrimination in access to opportunity in online advertisements, and the design of voting techniques that allow for the election of diverse committees.

Celis received her B.Sc. from Harvey Mudd College in 2006 in computer science and mathematics, a M.Sc. in mathematics from the University of Washington in 2008, and a Ph.D. in computer science and engineering from the University of Washington in 2012. She was a research scientist at Xerox Research India where she managed the crowdsourcing research thrust worldwide, and then was a senior research scientist at École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland in computer and information sciences, where she was also a program advisor for the Digital Humanities doctoral program and a co-founder of the Computation Nature and Society Think Tank.

Celis has published articles in the leading conferences in computer science including ICML, NeurIPS, WWW, FOCS, FAT*, CSCW, in addition to journals such as Management Science, SIAM Journal on Computing. She is a leader in the #DiversifyAI and #FairML space, and was/is a co-organizer of the Fairness, Accountability and Transparency in Machine Learning workshop (FAT/ML) in 2018, the AI, Ethics and Society at Yale workshop (AIES @ Yale) in 2019, the LatinX in AI workshop (LXAI) at ICML 2019, the Women in Big Data workshop in 2019, and the Fairness, Accountability and Transparency conference (FAT*) in 2020.

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