Yue Yu
Room 301, Swain East
729 E 3rd Street
Bloomington, IN, US, 47405
I’m Yue Yu, a PhD candidate in Statistical Science at Indiana University. I am fortunate to be jointly advised by Prof. Chunfeng Huang and Prof. David Crandall. I also work closely with Prof. Dongruo Zhou and Prof. Guanyu Hu.
My research broadly spans Machine Learning, Spatial Statistics, and Sports Analytics. My recent work in Machine Learning focuses on computationally efficient post-training and test-time methods for generative AI models.
Some of my past and current projects include:
- Machine Learning: Continuous-Time and Multi-Task Reinforcement Learning, Test-Time Inference for LLMs, RL Post-Training for LLMs
- Spatial Statistics: Deep Kriging on the Sphere
- Sports Analytics: Causal Inference for basketball player injury and load management, Bayesian copula clustering of player types
Please feel free to reach out by email or LinkedIn message if you are interested in research collaborations or would like to connect for discussion! ![]()
News
| Oct 01, 2026 | One pre-print Exploring More, Reasoning Better: Stepwise Risk-Sensitive GRPO for Diffusion Language Models has been posted on arXiv |
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| Sep 24, 2026 | Our paper Test-Time Scaling for Diffusion Language Models with Frequency-Aware Remasking has been accepted to the Conference on Neural Information Processing Systems (NeurIPS) 2026 |
| Aug 21, 2026 | I have wrapped up my summer internship with the Supply Chain Optimization Technologies (SCOT) team at Amazon. It has been an unforgettable experience working alongside such an amazing team, and I am deeply grateful to the colleagues who supported me throughout this wonderful journey! Our project paper has been accepted to the GenAIECommerce Workshop at RecSys 2026 |
| May 14, 2026 | I have obtained the M.S. in Computer Science degree at Indiana University! |
| Apr 30, 2026 | Our papers On the Limits of Test-Time Compute: Sequential Reward Filtering for Better Inference and Instance-Dependent Continuous-Time Reinforcement Learning via Maximum Likelihood Estimation have been accepted to the International Conference on Machine Learning (ICML) 2026 |