Yuzhang Shang
[I'm on the 24/25 academic job market. Feel free to contact me.]
About Me
I am a final-year Ph.D. candidate in Computer Science at the Illinois Institute of Technology advised by Prof. Yan Yan.
During my Ph.D. journey, I was a visiting student at the University of Wisconsin-Madison under the supervision of Prof. Yong Jae Lee. I was a Research Scientist Intern at Google DeepMind working with Daniele Moro and Andrew Howard, on training inference-efficient large language models. Additionally, I interned as a Research Scientist at Cisco Research working with Gaowen Liu and Ramana Kompella, where I developed efficient CV models and created the model slimming toolbox.
Research Goal: Efficient/Scalable AI Systems
- Visual Generative Model Acceleration:
PTQ4DM (CVPR’2023), PTQ4DiT (NeurIPS’2024), QuEST, DKDM, . - Efficient (Visual, Language, and Multimodal) Large Models:
Bi-MTDP (CVPR’2024), FBPT (ICRA’2024), PB-LLM (ICLR’2024), LLM-Viewer (Efficient LLM Survey), RPTQ, ASVD, LLaVA-PruMerge, INTP-Video-LLMs. - Neural Network Compression:
LONDON (ICCV’2021), CMIM (ECCV’2022), LCR-BNN (ECCV’2022), Causal-DFQ (ICCV’2023), CL-Calib (CVPR’2024). - Efficient Training for Neural Networks:
MIM4DD (NeurIPS’2023), DQAS (ECCV’2024)), HEPrune (NeurIPS’2024), LTDD.
Before Ph.D., I worked as research assistants at Shandong University and Hong Kong University of Science and Technology (HKUST) under the supervision of Prof. Liqiang Nie and Prof. Dan Xu, respectively. I received my bachelor’s degrees in Applied Mathematics and Economics (dual degrees) at Wuhan University.
Besides research, I am a contract photographer for Shutterstock Images and Getty Images (my portfolio).