Biography

Kibok Lee is currently an assistant professor in the Department of Applied Statistics / Statistics and Data Science at Yonsei University. Previously he worked as an Applied Scientist at Amazon Web Services, Rekognition Team (AWS AI). He received his Ph.D. from the Department of Computer Science and Engineering at the University of Michigan in 2020, advised by Prof. Honglak Lee. His research focuses on machine learning and computer vision, which spans over deep representation learning, out-of-distribution detection, continual lifelong learning, and few-shot learning.

Employment

• 2022-Present: Assistant Professor, Applied Statistics / Statistics and Data Science at Yonsei University, Seoul, Korea

• 2020-2022: Applied Scientist, Amazon Web Services, WA, US

• 2019-2019: Research Intern, Uber Advanced Technologies Group, CA, US

• 2012-2015: Research Engineer, Samsung Electronics, Suwon, Korea

Education

• Ph.D. in Computer Science and Engineering, University of Michigan, 2020 (Advisor: Honglak Lee)

• M.S. in Electrical Engineering, KAIST, 2012 (Advisor: Junmo Kim)

• B.S. in Electrical Engineering, KAIST, 2011

Invited Talks

• Nov 2024: Korean Artificial Intelligence Association

• Oct 2024: Krafton AI

• Aug 2024: Korean Artificial Intelligence Association

• Jul 2024: The Korean Statistical Society

• Nov 2023: Resilient Autonomous Grid Engineering Research Center

• Jul 2023: Young Statistician's Meeting @ The Korean Statistical Society

• Jun 2023: Korea Data Mining Society

• May 2023: Department of Artificial Intelligence, Yonsei University

• Mar 2023: Department of Statistics, Korea University

• Mar 2023: Department of Statistics, Seoul National University

• Jan 2023: Korean Artificial Intelligence Association

• Dec 2022: Department of Statistics and Data Science, Yonsei University

• Aug 2022: Department of Statistics, Ewha Womans University

Professional Service

Journal Reviewer

• IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

• IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

• Journal of Machine Learning Research (JMLR)

• Transactions on Machine Learning Research (TMLR)

• International Journal of Computer Vision (IJCV)

• Pattern Recognition

Conference Reviewer

• ML: NeurIPS, ICML, ICLR, AAAI

• CV: CVPR, ICCV, ECCV, WACV,  ACCV

Workshop Program Committee (Reviewer)

• CVPR 2023-2024 Workshop on Generative Models for Computer Vision (GCV)

• NeurIPS 2022 ML Safety Workshop

• ICCV/ECCV 2022-2024 Workshop on a Challenge for Out-of-Distribution Generalization in Computer Vision (OOD-CV)

• NeurIPS 2021-2023 Workshop on Distribution Shifts (DistShift)

• ICCV/ECCV 2020-2023 Workshop on Adversarial Robustness in the Real World (AROW)

• ICML 2020-2021 Workshop on Uncertainty and Robustness in Deep Learning (UDL)

• NeurIPS 2018 Workshop on Continual Learning