Yuxiao Wen
Yuxiao Wen (top)
I am a Ph.D. candidate in Computer Science at the Courant Institute of Mathematical Sciences, New York University, advised by Prof. Yanjun Han and Prof. Zhengyuan Zhou. My research focuses on reinforcement learning, online decision-making, and causal inference, with applications to digital advertising, recommendation systems, and adaptive experimentation.
My work develops statistically principled and sample-efficient methods for sequential decision-making, connecting theoretical guarantees with practical challenges in digital advertising, personalized recommendations, and AI agentic systems. I have also worked as an Applied Scientist Intern at Amazon Web Services and a Machine Learning Engineer Intern at Spotify, developing learning algorithms for large-scale advertising and recommendation systems.
More recently, I have been exploring reliable LLM post-training and evaluation under operational constraints.
I'm graduating in 2027 and seeking full-time roles!
CV · Resume · Google Scholar · LinkedIn
Email: yuxiaowen@nyu.edu
Research interests
Online Learning & Decision-Making
Contextual bandits, reinforcement learning, adaptive policy evaluation
Causal Inference & Experimentation
Treatment effects, counterfactual estimation, adaptive A/B testing
Advertising & Marketplace Optimization
Auction bidding, dynamic pricing, budget-constrained optimization
Reliable AI & LLM Post-training
Policy improvement, constrained self-improvement, evaluation
Work experience
- Applied Scientist Intern, Amazon Web Services Summer 2025Reinforcement learning · Dynamic floor pricing · Online advertising
- Machine Learning Engineer Intern, Spotify Summer 2024Contextual bandits · Personalized recommendation · Adaptive experimentation [Publication] [Blog]
Selected publications
- International Conference on Machine Learning (ICML), 2026
- 2026POMS-HK 2026 Best Student Paper Finalist
POMS-China 2026 Best Student Paper Third Prize
- In The Fourteenth International Conference on Learning Representations (ICLR) , 2026
- International Conference on Machine Learning (ICML), 2025
Full publication list → (ICML, NeurIPS, ICLR, AISTATS, and more)
Education & teaching
Education
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Ph.D. in CS, New York University, 2021-present
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B.A. in CS, New York University, 2017-2021
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B.A. in Honors Math, New York University, 2017-2021
Teaching assistant
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DS-GA 3001: Applied Statistics, Fall 2024
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MATH-UA 133: Math for Econ III, Spring 2024
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DS-GA 3001: Applied Statistics, Fall 2023
About me
In 2021-23, I have been working on Scientific Machine Learning with Prof. Benjamin Peherstorfer. Prior to that, I received my B.A.s in Math and in CS at New York University and graduated with latin honor and department award.
More about me: I (fortunately) have the most lovely cat in the world. I love anything with matcha. My favorite anime/manga are (alphabetical order) Ajin, Alice in Borderland, Fire Punch, Jojo 2-4, The Disastrous Life of Saiki K., and tons of others.