Sayash Kapoor, Professor & Doctoral Candidate, Princeton University
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Sayash Kapoor, Professor & Doctoral Candidate, Princeton University

Kapoor is a Ph.D. candidate at Princeton University’s Center for Information Technology Policy. His research critically investigates machine learning methods and their use in science and has been featured in WIRED, the Los Angeles Times, and Nature, among other media outlets. At Princeton, Kapoor organized The Reproducibility Crisis in ML-based Science, a workshop that saw more than 1,700 registrations. He has worked on machine learning in several institutions and academia, including Facebook, Columbia University, and EPFL Switzerland.
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Kapoor is a doctoral candidate at the Center for Information Technology Policy at Princeton University. Among other media venues, his study has been covered in WIRED, the Los Angeles Times, and Nature. It critically examines machine learning techniques and their use in science. More than 1,700 people registered for Kapoor’s workshop, The Reproducibility Crisis in ML-based Science, at Princeton. He has experience in academia and several organizations, such as Facebook, Columbia University, and EPFL Switzerland, where he has worked on machine learning. He received an Impact Recognition award from ACM CSCW and a Best Paper Award from ACM FAccT.


2023

Princeton starts a new program centered on large language models amid the AI boom. A recently launched Princeton effort seeks to attract expertise and allocate funds for the advancement of artificial intelligence (AI) in the fields of academia and research.

The Princeton Language and Intelligence Initiative (PLI) aims to investigate the societal and ethical implications of artificial intelligence (AI) and devise strategies to prevent any negative effects. The website states that PLI “seeks to develop fundamental understanding of large AI models; enable their application to research and education across academic disciplines.” Apart from serving as a platform for interdisciplinary collaboration between students, researchers, and teachers, PLI will also endeavor to establish a resilient local computer infrastructure, complete with its own staff and equipment.


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