Dr. Chen Dan 丹晨
I am a postdoctral researcher at TTIC working with Prof. Avrim Blum. In 2022, I graduated with Ph.D. degree from Computer Science Department, Carnegie Mellon University, advised by Pradeep Ravikumar. Prior to this, I received B.Sc from School of EECS, Peking University, where I worked with Liwei Wang.
I am looking for research positions in the industry.
CV (last updated: Jan 2023)
My research interest is in the broad area of robust statistical learning, with an emphasis on the theoretical understanding and practical algorithms for learning under various types of adversarial distribution shift. This encompasses many new challenges in ML, including:
- Adversarial Examples: See e.g. my papers in ICML'20a and ICLR'20;
- Class/Group Imbalance: ICML'21 and ICML'20b;
- Outliers in Data: ICML'21.
Email: chendan at ttic dot edu ; You may also find me on Google Scolar, DBLP, and Twitter.
- [January 2023] Our paper "Understanding Why Generalized Reweighting Does Not Improve Over ERM" has been accepted to ICLR 2023.
- [December 2022] I am joining TTIC as a postdoctral researcher, working with working with Prof. Avrim Blum.
- [November 2022] Our paper "Fundamental Limits and Tradeoffs in Invariant Representation Learning" has been accepted to JMLR 2022. arXiv
- [August 2022] Defended my PhD thesis.
- [September 2021] Our Paper Boosted CVaR Classification has been accepted to NeurIPS 2021. arXiv
- [May 2021] Our Paper DORO: Distributional and Outlier Robust Optimization has been accepted to ICML 2021. arXiv