I am a Ph.D. Candidate in Computer Science at University of Southern California, Los Angeles, USA. My research focuses on large-scale machine learning algorithms, models, and systems. 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, and Tencent.
I have a strong background in Internet industrial level Research and Development, including cloud computing architecture, machine learning, AI computing platform, distributed systems, mobile computing, and embedded operating system. 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.
I am interested in both ML and System. From the perspective of ML, my research interest is machine learning algorithms, modeling, systems, 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. Recently, I am focusing on:
1. Machine learning in distributed, federated, multi-agent, multitask settings
2. Large-scale distributed training algorithm and system for extremely large DNN models (Transformers, BERT, DLRM, Video Recognition, Vision+Language, etc)
2. Federated learning algorithms, models, systems, and analytics
3. Automated machine learning (AutoML), neural architecture search (NAS), efficient model architecture design
5. Cutting-edge models and their applications, such as Transformers, Graph Neural Networks, GAN, etc.
6. Large-scale computer system architecture design and optimization; open-source software
7. Machine learning optimization, information theory
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!