Bio
I am currently a Ph.D. student in Computer Science at the University of Illinois Chicago (UIC) guided by Prof. Yan Yan.
My research focuses on advancing Efficient AI, with interests in dataset distillation and large-scale model training. My work explores both algorithmic and systems-level approaches to improving training efficiency. I have published on dataset distillation in CVPR, ICCV, ECCV, and NeurIPS, covering topics on long-tailed dataset distillation, generative dataset distillation, and dataset quantization.
I interned at Argonne National Laboratory in 2023 and 2025. In 2023, I studied the performance of distributed training frameworks such as PyTorch DDP, Horovod, and DeepSpeed. In 2025, I returned to Argonne to work on LLM training on high-performance computing (HPC) platforms, where I conducted a comparative study of DeepSpeed, TorchTitan, and FSDP for scalable optimization.
Before Ph.D., I received my M.S. in Computer Science from the Illinois Institute of Technology (IIT) and a B.S. in Computer Science and Engineering from Nanjing University of Post and Telecommunications (NJUPT).
I am actively exploring 2026 Summer Internship opportunities. Feel free to review my resume here.
