About Me
- Greetings! I’m a fourth-year Ph.D. student in the Department of Electrical and Computer Engineering, at Stevens Institute of Technology, advised by Prof. Hao Wang.
- I received my Master’s Degree in Computer Science and Systems at University of Washington Tacoma, advised by Prof. Wes J. Lloyd and Dr. Athirai A. Irissappane. I received my Bachelor’s Degree in Electronic Engineering at Shanghai Jiao Tong University.
- My research interests lie in Large-Scale AI/ML Systems, LLM Serving Systems, Reinforcement Learning Systems, Serverless Computing, and Cloud Computing. Specifically, I focus on improving the resource efficiency of serverless computing systems with AI/ML-driven techniques and building efficient serverless systems for AI/ML training and inference:
- [VLDB’25, SC’24 (Best Student Paper Finalist), AAAI’24] Accelerated and cost-effective large-scale distributed DRL systems on serverless computing
- [SoCC’24 (Best Paper Award)] Accelerated serverless inference of large-scale Deep Learning systems
- [ASPLOS’24, TPDS’24, HPDC’23, WWW’22, ACSOS’21] Efficient resource management and function scheduling for serverless computing systems
- [In-progress] Memory-efficient LLM inference of Mixture-of-Experts (MoE) architectures
- I’m the recipient of various academic awards, including SoCC’24 Best Paper Award, SC’24 Best Student Paper Finalist, and ECAI’23 Call to Arms Award.
News
- Nov 2024 [Service] Serve on the Artifact Evaluation Program Committee for FAST 2025
- Sep 2024 [Paper] Pre-Warming is Not Enough: Accelerating Serverless Inference With Opportunistic Pre-Loading accepted by SoCC 2024 and got the Best Paper Award!
- Sep 2023 [Service] Serve as a Reviewer for ICLR 2025
- Aug 2024 [Paper] Nitro: Boosting Distributed Reinforcement Learning with Serverless Computing accepted by VLDB 2025
- Aug 2024 [Paper] Freyr+: Harvesting Idle Resources in Serverless Computing via Deep Reinforcement Learning accepted by TPDS 2024
- July 2024 [Service] Serve on the Technical Program Committee for ICPADS 2024
- June 2024 [Paper] Stellaris: Staleness-Aware Distributed Reinforcement Learning with Serverless Computing accepted by SC 2024 and got into the Best Student Paper Finalist!