About Me
I am a Ph.D. student in the Machine Learning Department at Carnegie Mellon University, advised by Prof. Kun Zhang (CMU-CLeaR Group). Prior to CMU, I was a Senior Machine Learning Engineer at Meta, Ads Core ML team. In 2017, I obtained my B.S. in Computer Science and B.S. in Mathematics with highest distinction at the University of Illinois at Urbana-Champaign, where I was fortunate to be advised by Prof. Dan Roth and Prof. AJ Hildebrand. I worked at ZhenFund in 2023 as an Investment Intern and Metabit Trading in 2022 as a Quantitative Research Intern. I worked at Jump Trading in 2024 as a Quantitative Research Intern.
Research Interests
My research goal is to build better machine learning methods using mathematical insights. My current work mainly focuses on two areas:
Working at Meta gave me extensive experience with real problems in the world of machine learning, specifically in optimizing large-scale advertising recommendation systems. I focus on identifying these problems and coming up with principled ways to fix them. Examples: maximization bias problem, model evaluation problem.
How to use machine learning to solve problems in quantitative finance.
Preprints and Publications
(* denotes equal contribution)
Preprints
Calibration-then-Calculation: A Variance Reduced Metric Framework in Deep Click-Through Rate Prediction Models
Yewen Fan, Nian Si, Xiangchen Song, Kun Zhang
ArxivOn the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors
Xinshuai Dong, Haoyue Dai, Yewen Fan, Songyao Jin, Sathyamoorthy Rajendran, Kun Zhang
Arxiv
Conference Publications
Causal Temporal Representation Learning with Nonstationary Sparse Transition
Xiangchen Song, Zijian Li, Guangyi Chen, Yujia Zheng, Yewen Fan, Xinshuai Dong, Kun Zhang
Neural Information Processing Systems (NeurIPS), 2024
ArxivAgentKit: Structured LLM Reasoning with Dynamic Graphs
Yue Wu, Yewen Fan, So Yeon Min, Shrimai Prabhumoye, Stephen McAleer, Yonatan Bisk, Ruslan Salakhutdinov, Yuanzhi Li, Tom Mitchell
Conference on Language Modeling (COLM), 2024
Code, ArxivRead and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals
Yue Wu, Yewen Fan, Paul Pu Liang, Amos Azaria, Yuanzhi Li, Tom M. Mitchell
Neural Information Processing Systems (NeurIPS), 2023
Workshop on Reincarnating Reinforcement Learning, ICLR, 2023 (Oral)
Arxiv, Blog
Media Coverage: New Scientist, Singularity Hub, National PostTemporally Disentangled Representation Learning under Unknown Nonstationarity
Xiangchen Song, Weiran Yao, Yewen Fan, Xinshuai Dong, Guangyi Chen, Juan Carlos Niebles, Eric Xing, Kun Zhang
Neural Information Processing Systems (NeurIPS), 2023
Arxiv, OpenReviewCalibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems
Yewen Fan*, Nian Si*, Kun Zhang
International Conference on Learning Representations (ICLR), 2023
Code, Arxiv, OpenReview, Video, Slide, PosterGeneralized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks
Yujia Zheng, Ignavier Ng, Yewen Fan, Kun Zhang
International Conference on Learning Representations (ICLR), 2023
Arxiv, OpenReview
Education
- 2021.9 - Present, Carnegie Mellon University
Ph.D. in Machine Learning - 2021.9 - 2024.5, Carnegie Mellon University
M.S. in Machine Learning - 2015.1 - 2017.5, University of Illinois at Urbana-Champaign
B.S. in Computer Science - 2015.1 - 2017.5, University of Illinois at Urbana-Champaign
B.S. in Mathematics - 2013.9 - 2015.1, Beijing Jiaotong University
Transferred to University of Illinois at Urbana-Champaign - 2007.9 - 2013.5, The High School Affiliated to Renmin University of China
Industry Experiences
- 2024.6 - 2024.8, Quantitative Research Intern, Jump Trading
- 2023.5 - 2023.8, Investment Intern, ZhenFund
- 2022.6 - 2022.8, Quantitative Research Intern, Metabit Trading
- 2017.7 - 2021.5, Senior Machine Learning Engineer, Meta
- 2016.5 - 2016.8, Software Engineer Intern, Meta
Honors and Awards
Competitive Programming Contests
- Finalist, 2017 ACM-ICPC World Final (56th / 128)
- Gold Medal, 2016 ACM-ICPC Mid-Central USA Regionals Chicago site (3rd / 152)
Math Contests
- 2017, 2016 University of Illinois Undergraduate Math Contest, Rank 1st
- 2015 William Lowell Putnam Mathematical Competition, Top 10%
- 2014 Beijing Undergraduate Mathematics Competition, First Prize
Machine Learning Contests
- 2023, ADIA Lab Market Prediction Competition (7th / 375, $5000 award)
Contract Bridge
- 2016 American Contract Bridge League Collegiate Bridge Bowl, Champion, $20000 award
- 2014 Chinese National Youth Bridge Championship, U20 Group Champion
- 2012 Chinese National Middle School Bridge Championship, Champion (Senior High School Division)
- 2009 Chinese National Middle School Bridge Championship, Champion (Junior High School Division)
Others
- 2017 Summa Cum Laude
- 2014 China National Scholarship
Teaching
- TA, 10-715 Advanced Introduction to Machine Learning, Fall 2024
- TA, 10-715 Advanced Introduction to Machine Learning, Fall 2022
Services
- Online Chair, The Conference on Uncertainty in Artificial Intelligence (UAI) 2022
- Reviewer: NeurIPS, ICLR, ICML, UAI, KDD
Packages
- causal-learn: Causal Discovery for Python
causal-learn is an advanced Python adaptation and expansion of the Tetrad Java framework, providing cutting-edge causal discovery techniques coupled with user-friendly and intuitive APIs. The causal-learn project is a collaborative effort involving multiple teams, with my role being the leader of the quality control team. We are continuously working to improve the project, and we greatly appreciate any feedback or recommendations from the community.
Documentation, Github
Miscellaneous
I am enthusiastic about all kinds of card games (e.g. contract bridge, 双升, Texas Hold’em, 拱猪). I write about 双升 [1, 2] and Texas Hold’em [3] in Zhihu(知乎).