Engineering

LIN F. YANG

Dr. Lin Yang (杨林) is currently an Associate Professor (tenured) in the Electrical and Computer Engineering Department and Computer Science Department at the University of California, Los Angeles.

His research focuses on the foundations of modern machine learning and data science. He develops fast algorithms with provable guarantees, especially in reinforcement learning, large language model acceleration, compression, and quantization, non-convex optimization, and streaming algorithms. He is particularly interested in safe and efficient decision-making under uncertainty, bridging theoretical insights with real-world impact in areas such as healthcare, robotics, and scientific discovery.

Dr. Yang is a recipient of an Amazon Faculty Award, the Simons Research Fellowship, Dean Robert H. Roy Fellowship, and the JHU MINDS Best Dissertation Award. Prior to joining UCLA, he was a postdoctoral researcher at Princeton University working with Prof. Mengdi Wang. He received dual Ph.D. degrees in Computer Science and in Physics & Astronomy from Johns Hopkins University, advised by Alex Szalay and Vova Braverman.

[Last updated Jun. 2026]

Research

I study the foundations and systems principles behind reliable learning and decision-making. My recent interests connect reinforcement learning, agentic AI frameworks, LLM fine-tuning, and LLM compression and quantization: how agents should gather feedback, adapt language models to specialized tasks, run efficiently under resource constraints, and make sequential decisions with safety guarantees.

Research map connecting reinforcement learning, agentic frameworks, LLM fine-tuning, and LLM compression A diagram showing four research areas feeding into efficient and reliable AI: reinforcement learning, agentic frameworks, LLM fine-tuning, and LLM compression and quantization. Reinforcement Learning exploration, safety, efficiency Agentic AI Frameworks planning, memory, feedback LLM Fine-Tuning adaptation, alignment, evaluation LLM Compression & Quantization small models, fast inference Efficient, Reliable AI Systems

Featured work: Winning Gold at IMO 2025 with a Model-Agnostic Verification-and-Refinement Pipeline, with Yichen Huang. We study how verification and iterative refinement can turn strong general-purpose LLMs into reliable mathematical reasoning agents.

Application Domains

Healthcare
Robotics
Science Discovery
Finance
  • Reinforcement learning: algorithms with provable sample efficiency, safe exploration, and robust decision-making under uncertainty.
  • Agentic frameworks: principled ways to design AI agents that plan, use tools, remember context, and learn from feedback.
  • LLM fine-tuning: methods for adapting large language models to specialized tasks while preserving reliability, efficiency, and evaluation rigor.
  • LLM compression and quantization: techniques for reducing memory and compute costs so language models can run faster and more broadly without losing essential capabilities.

For publications and citation records, visit my Google Scholar profile.

Publications

Service

  • Senior Area Chair: NeurIPS 2026
  • Area Chair: ICLR, AISTATS, ICML, NeurIPS
  • Senior Program Committee: AAAI
  • Conference Reviewing: AAAI, ICML, NeurIPS, STOC, FOCS, COLT, PODS, ITCS, ACML, AISTATS, ICALP, RANDOM, ESA, LATIN, KAIS, FSTTCS
  • Journal Reviewing: Theoretical Computer Science, QIC, JCSS, OPRE, TMLR

People

Students
  • Yunfan Li
  • Chang Liu
  • Lawrence Liu
Alumni
  • Osama Hanna (co-advising with Prof. Christina Fragouli. First Job: Meta)
  • Jialin Dong (First Job: SAP Labs)
  • Sanae Amani (First Job: PayPal)
  • Masoud Monajatipoor (co-advising with Kai-Wei Chang, First Job: Optum)
  • Fei Feng (co-advising with Wotao Yin, First Job: Pinterest)
  • Qiwen Cui (former undergraduate advisee, Ph.D. degree from UW)
  • Kunhe Yang (former undergraduate advisee, now Ph.D. student at Berkeley)
  • Junhong Shen (former undergraduate advisee, now Ph.D. student at CMU)
  • Jinghan Wang (former undergraduate advisee, now Ph.D. student at UMD)
  • Dingwen Kong (former undergraduate advisee, now Ph.D. student at MIT)

Education

Johns Hopkins University, Baltimore, MD
  • Ph.D. in Computer Science, Advisors: Vladimir Braverman, Alex Szalay
  • Ph.D. in Physics & Astronomy, Advisor: Alex Szalay
  • MS.E. in Computer Science (GPA 4.0/4.0)
Tsinghua University, Beijing, China
  • B.S. in Math & Physics with High Honors

Contact Me

Email: linyang[at]ee[dot]ucla[dot]edu

My middle name Forrest is a translation of my first name (林). My publication name is Lin F. Yang -- to distinguish me from many Lin Yang's. Note that my official name (Lin Yang OR 杨林) does not contain a middle name.