I am a fourth-year Ph.D. student in Machine Learning Department, School of Computer Science, Carnegie Mellon University, co-advised by Prof. Yiming Yang and Prof. Ameet Talwalkar. I received my Bachelor’s degree in computer science (summa cum laude) from Turing Class, CFCS, Peking university, with a minor in mathematics.

I have interned as a Student Researcher at ByteDance Seed (2025, mentored by Tianle Cai) and Google (2023 & 2024, mentored by Srinadh Bhojanapalli and Nikunj Saunshi).

My research goal is to develop machine intelligence methods that better augment human intelligence. My PhD study has been focused on AI for science and scientists. Recently, I’m fascinated with building and evaluating LLM agents that facilitates scientific research and discovery. To empower these agents with deeper reasoning and robust understanding of complex problems, I am also interested in scaling laws and long-context capabilities of large language models.

My detailed CV can be found here.

Selected Publications (one per year)

2025 CodePDE: An Inference Framework for LLM-driven PDE Solver Generation (TMLR) [PDF]
Shanda Li, Tanya Marwah, Junhong Shen, Weiwei Sun, Andrej Risteski, Yiming Yang, Ameet Talwalkar

2024: Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models (ICLR 2025) [PDF] [talk (in Chinese)]
Yangzhen Wu, Zhiqing Sun, Shanda Li, Sean Welleck, Yiming Yang

2023: Functional Interpolation for Relative Positions Improves Long Context Transformers (ICLR 2024) [PDF]
Shanda Li, Chong You, Guru Guruganesh, Joshua Ainslie, Santiago Ontanon, Manzil Zaheer, Sumit Sanghai, Yiming Yang, Sanjiv Kumar, Srinadh Bhojanapalli

2022: Is $L^2$ Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network? (NeurIPS 2022) [PDF]
Chuwei Wang*, Shanda Li*, Di He, Liwei Wang

Blogs

July 29, 2024 CMU-MATH Team’s Innovative Approach Secures 2nd Place at the AIMO Prize (Published on CMU ML Blog)