CV

Education

• Ph.D in Computer Science, Hong Kong University of Science and Technology, 2028 (expected)
• B.S. in Computer Science, ShanghaiTech University, 2021

Work experience

• November 2023 - Present: Research Intern at Alibaba Group, Hangzhou, China
  • Designed and implemented a straggler detection and mitigation framework for hybrid-parallel large model training.
  • Analyzed production traces from the Alibaba HPAI cluster.
  • Mentor: Yinghao Yu
• May 2022 - July 2023: Research Intern at Microsoft Research Asia, Beijing, China
  • Developed Catur, a NUMA-aware scheduler for VM allocation on Microsoft Azure utilizing reinforcement learning.
  • Implemented system optimizations for reinforcement learning frameworks.
  • Mentors: Qi Chen, Hui Xue
• August 2021 - April 2022: Teaching Assistant at NYU Shanghai, Shanghai, China
  • Served as a teaching assistant for CSCISHU 11 - Introduction to Computer Programming in Fall 2021.
  • Served as a teaching assistant for CSCISHU 2314 - Discrete Mathematics in Spring 2022.

Skills

• Programming Languages (in order of familiarity)
  • Python
  • C/C++
  • CUDA
  • Rust
  • Shell
  • JavaScript
• HPC/OS-related Software (in order of familiarity)
  • Linux (Ubuntu, Debian, CentOS, Arch Linux, Manjaro)
  • Docker
  • Kubernetes
  • Slurm
  • MPI, OpenMP
• DL-related Frameworks (in order of familiarity)
  • PyTorch
  • Megatron-LM
  • vLLM
  • DeepSpeed
  • Ray RLlib
• Misc
  • LaTeX
  • Markdown
  • HTML

Publications

Crab: A Semantics-Aware Checkpoint/Restore Runtime for Agent Sandboxes

Weave: Efficient Co-Scheduling for Disaggregated RL Post-Training

RollArt: Scaling Agentic RL Training via Disaggregated Infrastructure

AReaL-Hex: Accommodating Asynchronous RL Training over Heterogeneous GPUs

RollPacker: Mitigating Long-Tail Rollouts for Fast, Synchronous RL Post-Training

Virtual Machine NUMA Placement at Scale: Learning the Norm, Shielding the Tail

Attack of the Bubbles: Straggler-Resilient Pipeline Parallelism for Large Model Training

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Toppings: CPU-Assisted, Rank-Aware Adapter Serving for LLM Inference

Greyhound: Hunting Fail-Slows in Hybrid-Parallel Training at Scale

A Data Optimizer for Region-Aware Self-describing Files in Scientific Computing

Portus: Efficient DNN Checkpointing to Persistent Memory with Zero-Copy

Reproducibility: Performance Evaluation of MemXCT on Azure CycleCloud Platform

Teaching

Teaching assistant for CSCISHU 2314 - Discrete Mathematics (2022 Spring)

Head teaching assistant for CS130 - Operating Systems (2021 Fall)

Teaching assistant for CSCISHU 11 - Introduction to Computer Programming (2021 Fall)

Head teaching assistant for CS100 - Introduction to Computer Programming (2019 Fall, 2020 Spring, 2022 Spring)