Events

The Story of CILVR

Speaker: Prof. Kyunghyun Cho

Location: 60 Fifth Avenue, Room 7th Floor CDS Open Space
Videoconference link: https://nyu.zoom.us/s/99140419191

Date: Wednesday, September 3, 2025

Abstract: This talk will open the 2025-2026 CILVR Seminar Series with a review of the lab’s history, current research directions, and future vision. Beginning with CILVR’s foundational work in computer vision, NLP, and generative models, the presentation will cover our recent expansion and the consequent broadening of our research scope. We will survey the lab’s current intellectual landscape, which now spans from core AI foundations and major applications like robotics to interdisciplinary frontiers such as computational neuroscience and AI for climate science. The talk will conclude by outlining the lab’s forward-looking vision, which is focused on developing trustworthy AI, applying machine learning to scientific and societal challenges, and exploring the future of human-AI collaboration.

Bio: Kyunghyun Cho is a professor of computer science and data science at New York University and an executive director of frontier research at the Prescient Design team within Genentech Research & Early Development (gRED). He became the Glen de Vries Professor of Health Statistics in 2025. He is also a CIFAR Fellow of Learning in Machines & Brains and an Associate Member of the National Academy of Engineering of Korea. He served as a (co-)Program Chair of ICLR 2020, NeurIPS 2022 and ICML 2022. He was one of the three founding Editors-in-Chief of the Transactions on Machine Learning Research (TMLR) until 2024. He was a research scientist at Facebook AI Research from June 2017 to May 2020 and a postdoctoral fellow at University of Montreal until Summer 2015 under the supervision of Prof. Yoshua Bengio, after receiving MSc and PhD degrees from Aalto University April 2011 and April 2014, respectively, under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He received the Samsung Ho-Am Prize in Engineering in 2021. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so. (edite