Daniel J. Hsu - Department of Computer Science and Data Science Institute, Columbia University

I work on algorithmic statistics and machine learning. My research is part of broader efforts in Foundations of Data Science, Machine Learning, and Theory of Computation at Columbia.

If you are a (current or prospective) student interested in coming to Columbia and/or working with me on research, or if you are generally interested in getting started in machine learning and/or research, please check this page of frequent answers to questions.

My papers can be found by following this link. (See also arXiv, dblp, Google Scholar.)

People

I’m lucky to be able to work with several outstanding students and postdocs.

Students
- Alejandro Buendia (B.S., 2019; → Microsoft/MIT/Columbia → Stanford)
- Arushi Gupta (B.S., 2016; M.S., 2018; → Princeton)
- Berkan Ottlik (B.S., 2024; → Hawaii Farming → Univ. of Pennsylvania)
- Clayton Sanford (Ph.D., 2024; → Google Research)
- Eden Shaveet (Bridge-to-Ph.D. program, 2024; → Cornell Tech)
- Edward Ri (B.S., 2024; → Princeton)
- Geelon So (M.S., 2019; → UC San Diego)
- Giannis Karamanolakis (Ph.D., 2022; → Amazon Alexa AI)
- Jagdeep Bhatia (High school diploma, 2020; → MIT)
- Ji Xu (Ph.D., 2020; → Two Sigma)
- Jingwen Liu (Ph.D. in progress)
- Kevin Shi (Ph.D., 2020; → Facebook)
- Kiran Vodrahalli (Ph.D., 2022; → Google Brain)
- Mingyue Xu (M.S., 2022; → Purdue)
- Navid Ardeshir (Ph.D. in progress)
- Noah Bergam (Ph.D. in progress)
- Rishabh Dudeja (Ph.D., 2021; → Harvard → Univ. of Wisconsin)
- Samuel Deng (M.S., 2021; Ph.D. in progress)
Postdocs
- Christopher Tosh (→ Memorial Sloan-Kettering Cancer Center)
- Daniel G. Alabi (→ Univ. of Illinois Urbana-Champaign)
- Debmalya Mandal (→ MPI-SWS → Univ. of Warwick)

I’ve also worked with other fantastic students on their thesis research, including: Avner May, Cun Mu, Gan Yuan, José Manuel Zorrilla Matilla, Karl Stratos, Mathias Lécuyer, Tom Effland.

Service

Associate editor / action editor
- ACM Transactions on Algorithms (2017-)
- Journal of Machine Learning Research (2022-)
- SIAM Journal on Mathematics of Data Science (2021-)
Conference program chair
- International Conference on Algorithmic Learning Theory (2024; co-chair with Claire Vernade)
- Conference on Learning Theory (2019; co-chair with Alina Beygelzimer)
- International Conference on Machine Learning (2025; co-chair with Maryam Fazel, Simon Lacoste-Julien, and Virginia Smith)
Conference program committee member
- AAAI Conference on Artificial Intelligence (2014)
- International Conference on Artificial Intelligence and Statistics (2010, 2011, 2016 AC, 2017 AC, 2019 AC)
- International Conference on Algorithmic Learning Theory (2017, 2018, 2019, 2021, 2022 AC, 2023 AC)
- Conference on Learning Theory (2011, 2013, 2015, 2016, 2017, 2018, 2020 AC, 2021 AC, 2022 AC, 2023 AC, 2024 AC)
- IEEE Symposium on Foundations of Computer Science (2021)
- International Conference on Machine Learning (2010, 2011, 2012 AC, 2013 AC, 2015 AC, 2016 AC, 2017 AC)
- Conference on Neural Information Processing Systems (2008, 2011, 2012 AC, 2013 AC, 2014, 2015, 2017 AC, 2019 SAC, 2020, 2021 SAC, 2023 SAC)
- Conference on Uncertainty in Artificial Intelligence (2010)
Program / workshop / seminar organization
- Columbia Foundations of Data Science Center Tutorials (May 2, 2025)
- Columbia Foundations of Data Science Center Workshop (April 29, 2025)
- Program on Modern Paradigms in Generalization @ Simons Institute (Fall 2024)
- Columbia Foundations of Data Science Center Workshop (April 26, 2024)
- Columbia Statistical Machine Learning Symposium (April 7-8, 2023)
- FOCS 2021 Workshop on Machine Learning (February 7-8, 2022)
- Columbia Year of Statistical Machine Learning (Fall 2019-Spring 2020)
- Columbia DSI/TRIPODS Deep Learning Workshop (March 15, 2019)
- Columbia Foundations of Data Science Seminar (Fall 2015)
- ICML 2014 Method of Moments and Spectral Learning (June 25, 2014)
- DIMACS/CCICADA Systems and Analytics of Big Data (March 17-18, 2014)
- NeurIPS 2013 Spectral Learning (December 10, 2013)
- ICML 2013 Spectral Learning (June 21, 2013)

Teaching

Courses at Columbia
- Computational Linear Algebra
- Machine Learning
- Machine Learning Theory
- Theoretical Foundations of LLMs
- Topics in Learning Theory
- Unsupervised Learning (previously “Advanced Machine Learning”)
Tutorials

Thanks

I am grateful for support provided by the National Science Foundation, the Office of Naval Research, the National Aeronautics and Space Administration, the Alfred P. Sloan Foundation, the Columbia Data Science Institute, Bloomberg, Google, JP Morgan, NVIDIA, Two Sigma, and Yahoo.