Hi! Welcome to Hanxian Huang’s webpage

I am a fifth-year Ph.D. candidate at the Computer Science and Engineering Department of UC San Diego, where I am fortunate to be advised by Prof. Jishen Zhao. Before joining UCSD, I received my B.S. degree from the School of Electronics Engineering & Computer Science at Peking University, advised by Prof. Guojie Luo. My current research broadly spans and stretches the intersection of machine learning (ML) with programming languages, compilers, and computer systems. This includes exploring advanced ML techniques to improve system designs and PL tasks, as well as efficient ML algorithms and system co-design.

News

2024/10: Our summary of “AI for Chip Design” tutorial at Hot Chips 2024 has been published on the ACM SIGARCH technical blog. Thanks for all the collaborators for putting them together!

2024/09: Our ISSTA’24 paper “Multi-modal Learning for WebAssembly Reverse Engineering” was selected as one of 11 (out of 143) papers to receive an ACM SIGSOFT Distinguished Paper Award!

2024/08: I will give a talk on “Domain Adaptive LLM Models for Chip Design” at the Hot Chips 2024 tutorial, see you at Hot Chips!

2024/08: I was invited as a speaker at IEEE MIPR Innovation Forum Industry Challenges of Efficient AI.

2024/07: I was invited to the ML and System Rising Stars workshop [poster]! Thanks ML Commons for this great organization and support!

2024/06: Our workload forecasting project was highlighted in the Microsoft Research Blog!

2024/05: I was selected as one of the Machine Learning and Systems Rising Stars 2024! See you at the workshop in the NVIDIA Headquarters on July 15-16!

2024/05: Our paper, “Learning to Maximize Mutual Information for Chain-of-Thought Distillation”, is accepted to the ACL Findings 2024!

2024/04: Our paper, “Fasor: A Fast Tensor Program Optimization Framework for Efficient DNN Deployment”, is accepted by the International Conference on Supercomputing (ICS) 2024!

2024/03: I’ll be working as a research scientist intern in the Pytorch team at Meta this summer and looking forward to meeting you in Menlo Park!

2024/03: Our paper, “Multi-modal Learning for WebAssembly Reverse Engineering”, is accepted by The ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) 2024!

2023/11: Our paper, “Sibyl: Forecasting Time-Evolving Query Workloads”, is accepted by the ACM SIGMOD Conference 2024!