Ph.D. Candidate in Computer Science
University of Southern California, Los Angeles, USA
Founder of FedML.ai
R&D Manager and Staff Engineer at Tencent (2014-2018)
Senior Software Engineer at Baidu (2012-2014)
A lifelong learner with a strong passion and interest in scientific research, engineering, production, R&D team management, and entrepreneurship.
I am a Ph.D. Candidate in Computer Science at University of Southern California, Los Angeles, USA. My academic advisors are Professor Salman Avestimehr (USC), Professor Mahdi Soltanolkotabi (USC), Professor Murali Annavaram (USC), and Professor Tong Zhang (HKUST). Previously, I was an R&D Team Manager and Staff Software Engineer at Tencent (2014-2018), a Team Leader and Senior Software Engineer at Baidu (2012-2014), and a Software Engineer at Huawei (2011-2012). During my Ph.D. study, I also worked closely with researchers/engineers at Google, Facebook, Amazon, and Tencent. I am a lifelong learner with a strong passion and interest in scientific research, engineering, production, R&D team management, and entrepreneurship (Biography).
In engineering and production, I have more than three years of experience in Internet R&D management, leading a team with 10-20 software engineers and researchers to build commercial Internet products. The Internet Products that I worked for include Tencent Cloud, Tencent WeChat Automotive / AI in Car, Tencent Games, Tencent Maps, Baidu Maps, and Huawei Smartphone (see Production for details).
In pure scientific research, I am equally interested in machine learning algorithms and systems. From the perspective of algorithmic ML, my research interest is machine learning algorithms, modeling, and their applications on computer vision, natural language processing, and data mining. In the system domain, I am particularly interested in distributed systems, cloud computing, mobile computing, embedded operating systems.
The goal of my Ph.D. career is to develop distributed/federated, automated, and trustworthy machine learning algorithms and systems. Recently, I am focusing on:
1. Large-scale distributed training algorithms and systems for huge DNN models (Transformers, ViT, BERT, etc.)
2. Federated learning algorithms, models, systems, and their applications in CV, NLP, Data Mining, and IoT.
3. Automated machine learning (AutoML) with Neural Architecture Search.
Funding and Research Platform in Our Lab:
(For researchers in Machine Learning/CV/NLP/Data Mining/Large-scale Distributed Training System, if you would like to apply PhD/Post Doc position in the United States, please consider applying to our lab. We have many related findings as follows)
USC-Amazon Center on Trusted AI! (large-scale distributed learning, privacy, security, etc)
Frontiers of Distributed Machine Learning
Open, Programmable, and Secure 5G
Intel/NSF Project on Machine Learning at the Wireless Edge!