Welcome to my homepage!
I’m a first-year PhD student in Computer Science and Engineering at University of Michigan, Ann Arbor, advised by Prof. Mosharaf Chowdhury. Before UMich, I completed my MSc in Computer Science at University of British Columbia, working with Prof. Ivan Beschastnikh and Prof. Mathias Lécuyer. I obtained my Bachelor’s in Computing at Hong Kong Polytechnic University.
My current research interests involve distributed system, cloud computing and machine learning. In my MSc work, I proposed GlueFL, a federated learning framework designed to optimize downstream bandwidth. Prior to this, I finished my undergraduate thesis under the supervision of Prof. Song Guo. My thesis was about developing new compression techniques in distributed machine learning. Even before, I did an internship at Microsoft Research Asia (MSRA), working with Dr. Yang Chen. We worked on Forerunner, a novel computing framework that leverages speculative execution to accelerate transaction processing on Ethereum.
July 2022 - Join our competition at NeurIPS 2022!
NL4Opt: Formulating Optimization Problems Based on Their Natural Language Descriptions
Publications & Pre-prints
Anchor Sampling for Federated Learning with Partial Client Participation
Feijie Wu, Song Guo, Zhihao Qu, Shiqi He, Ziming Liu, Jing Gao
International Conference on Machine Learning (ICML 2023) [.pdf]
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning
Shiqi He, Qifan Yan, Feijie Wu, Lanjun Wang, Mathias Lécuyer, Ivan Beschastnikh
Conference on Machine Learning and Systems (MLSys 2023) [.pdf]
Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions
Rindranirina Ramamonjison, Haley Li, Timothy T. Yu, Shiqi He, Vishnu Rengan, Amin Banitalebi-Dehkordi, Zirui Zhou, Yong Zhang
Empirical Methods in Natural Language Processing (EMNLP 2022) [.pdf]
Sign Bit is Enough: A Learning Synchronization Framework for Multi-hop All-reduce with Ultimate Compression
Feijie Wu*, Shiqi He*, Song Guo, Zhihao Qu, Haozhao Wang, Weihua Zhuang, Jie Zhang
Design Automation Conference (DAC 2022) [.pdf]
On the Convergence of Quantized Parallel Restarted SGD for Serverless Learning
Feijie Wu, Shiqi He, Yutong Yang, Haozhao Wang, Zhihao Qu, Song Guo
arXiv, preprint arXiv:2004.09125. [.pdf]