Kangwook Lee

 

Assistant Professor
Department of Electrical and Computer Engineering
Department of Computer Sciences (by courtesy)
University of Wisconsin-Madison

Affiliations:
MLOPT Research Group
Institute for Foundations of Data Science (IFDS)

Email : first_name.last_name at wisc dot edu
Phone : +1-608-265-4841
Webpage : http://kangwooklee.com
Google Scholar : http://scholar.google.com/citations?user=sCEl8r-n5VEC&hl=en

Lab News

New work

  • (Sep. 2021) NeurIPS 2021 Two papers are accepted to NeurIPS 2021. Congratulations!

    • Y. Roh, K. Lee, S. Whang, and C. Suh
      Sample Selection for Fair and Robust Training

    • J. Kim, J. Jeon, K. Lee, S. Oh, and J. Ok
      Gradient Inversion with Generative Image Prior

  • (June 2021) ICML 2021 Workshops Two papers are accepted to ICML 2021 Workshops.

    • J. Jeon, J. Kim, K. Lee, S. Oh, and J. Ok
      Gradient Inversion with Generative Image Prior

    • D. Seo, H. Wang, D. Papailiopoulos, and K. Lee
      Empirical Study on the Effective VC Dimension of Low-rank Neural Networks

  • (May 2021) ICML 2021 Two papers are accepted to ICML 2021. Congratulations, Tuan and Changhun!

    • T. Dinh and K. Lee
      Coded-InvNet for Resilient Prediction Serving Systems (long oral)

    • C. Jo and K. Lee
      Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information

  • (Feb. 2021) Preprint Our new work on permutation-based SGD is available on Arxiv. ‘‘Permutation-Based SGD: Is Random Optimal?’’.

  • (Jan. 2021) MLSys 2021 Our work on adaptive gradient communication for distributed ML is accepted.

    • S. Agarwal, H. Wang, K. Lee, S. Venkataraman, and D. Papailiopoulos.
      Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification.

  • (Jan. 2021) ICLR 2021 Our work on adaptive sampling for group fairness is accepted to ICLR 2021.

    • Y. Roh, K. Lee, S. Whang, and C. Suh
      FairBatch: Batch Selection for Model Fairness

  • (Sep. 2020) NeurIPS 2020 Our work on robust federated learning is accepted to NeurIPS 2020.

    • H. Wang, K. Sreenivasan, S. Rajput, H. Vishwakarma, S. Agarwal, J. Sohn, K. Lee, and D. Papailiopoulos
      Attack of the Tails: Yes, You Really Can Backdoor Federated Learning

  • (June 2020) ECML PKDD 2020 Our new work on GAN reprogramming is accepted to ECML PKDD 2020.

    • K. Lee, C. Suh, and K. Ramchandran
      Reprogramming GANs via Input Noise Design

  • (May 2020) ICML 2020 Our work on trustworthy AI is accepted to ICML 2020.

    • Y. Roh, K. Lee, S. Whang, and C. Suh
      FR-Train: A mutual information-based approach to fair and robust training

Award, Grant & Gift

Members

  • (Sep. 2021) Joseph Shenouda joined the lab! Welcome!

  • (Aug. 2021) Ruisu Zhang and Andrew Geng joined the lab! Welcome!

  • (Aug. 2021) Dr. Daewon Seo joined DGIST as an Assistant Professor! Congrats!

  • (June 2021) Yuchen Zeng and Ziqian Lin joined the lab! Welcome!

  • (Feb. 2021) Jy-yong Sohn (co-hosted with Prof. Dimitris Papailiopoulos) came back to Madison as a postdoc! Welcome!

  • (Feb. 2021) Liang Shang, Ethan Grover, and Michael Gira joined the lab. Welcome!

  • (July/Aug. 2020) Tuan Dinh, Nayoung Lee, and Liu Yang joined the lab. Nayoung will be co-advised with Prof. Dimitris Papailiopoulos, and Liu will be co-advised with Prof. Rob Nowak and Prof. Dimitris Papailiopoulos. Welcome!

  • (Jan./Mar. 2020) Dr. Daewon Seo and Zhixu Du joined the lab! Welcome!

  • (Nov. 2019) Changhun Jo and Hyecheol Jang joined the lab! Welcome!

  • (Oct. 2019) Jy-yong Sohn (co-hosted with Prof. Dimitris Papailiopoulos) will visit the lab for five months! Welcome!

Invited talk

  • (June 2021) Gave an invited talk @ AI institute of POSTECH. ‘‘Information Theory and Coding for Trustworthy and Scalable Machine Learning’’

  • (June 2021) Gave an invited talk at the Shannon meets Turing Colloquium @ Seoul National University. ‘‘Information Theory and Coding for Trustworthy and Scalable Machine Learning’’

  • (June 2021) Gave invited talks at KRAFTON, Inc. and Furiosa.ai. ‘‘Recent Trends of AI Research’’

  • (Apr. 2021) Gave an invited talk on ‘‘Fairness in AI’’ at IFDS Ethics & Algorithms SIG

  • (Feb. 2021) Gave an invited lecture on ‘‘Fairness in AI’’ as a part of Lectures on Machine Learning based ICT, organized by Korea Information and Communications Society

  • (Nov./Dec. 2020) Gave invited talks at the BLISS seminar @ UC Berkeley, SILO seminar @ UW Madison, ML Ideas @ Microsoft Research New England. ‘‘Make-or-break issues in fair classification’’

  • (May 2020) Gave a talk at Air Force Research Laboratory’s Virtual Workshop on Adversarial Robustness of Machine Learning, ‘‘FR-Train: A mutual information-based approach to fair and robust training’’

  • (Feb. 2020) Gave an invited talk at the Chaos and Complex Systems Seminar @ UW-Madison.

  • (Oct. 2019) Gave an invited talk at SILO seminar. ‘‘Learning with Scarce Data: The Role of Side Information, Simulators, and GANs’’