About Me

Howdy! My name is Yufeng Yang (杨钰峰). I’m a third year PhD student at CSE department, Texas A&M University, advised by Prof. Yi Zhou. I leverage foundational ML principles to address the computational and algorithmic challenges arising from large-scale machine learning applications.

(Open for Collaboration) My past research focuses on bridging connections between data and training algorithm/pipeline design via a framework so-called Distributionally Robust Optimization. Recently, my interests span:

• Providing modelling/algorithmic solutions for DRO in areas like data curation, multi-objective alignment, and agentic systems.
• Mechanistic understanding of how SGD and preconditioned variants interact with model structure and noisy/heavy-tailed training data.
• Black-box optimization and RL algorithms with potential applications to AI for hardware design/AI4Science.

If you believe our research interests align and want to collaborate with me. Feel free to drop me an email at ynyang94@tamu.edu or add me on WeChat: ynyang94 (please indicate your purpose when connecting).

I’m actively looking for AI/ML research/engineer internship starting at 27 Spring/Summer/Fall; And I’m also Open to Full-time Roles starting May, 2028. Here is my brief CV.

📄First-author Papers

• Distributionally-robust multi-objective optimization [arXiv]
  Y. Yang, F. Zhuo, Z. Chen, H. Huang and Y. Zhou, submitted.
• Nested SGD for Sinkhorn distance-regularized Distributionally Robust Optimization [arXiv] [code] [short version] [poster]
  Y. Yang, Y. Zhou and Z. Lu, submitted to JMLR; short version accepted at OPT Workshop, NeurIPS 2024.
• Adaptive Gradient Normalization and Independent Sampling for (Stochastic) Generalized-Smooth Optimization [arXiv] [code] [slides]
  Y. Yang, E. Tripp, Y. Sun, S. Zou and Y. Zhou, TMLR.