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.
Chaoyang He is a Ph.D. Candidate in the CS department at the University of Southern California, Los Angeles, USA. He is advised by Salman Avestimehr (USC), Professor Mahdi Soltanolkotabi (USC), Professor Murali Annavaram (USC), and Professor Tong Zhang (HKUST). He also works closely with researchers/engineers at Google, Facebook, Amazon, and Tencent. Previously, He 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). His research focuses on distributed/federated machine learning algorithms, systems, and applications. [Biography]
Chaoyang He has received a number of awards in academia and industry, including Amazon ML Fellowship (2021-2022), Qualcomm Innovation Fellowship (2021-2022), Tencent Outstanding Staff Award (2015-2016), WeChat Special Award for Innovation (2016), Baidu LBS Group Star Awards (2013), and Huawei Golden Network Award (2012). During his Ph.D. study, he has published papers at ICML, NeurIPS, CVPR, ICLR, MLSys, among others. Besides pure research, he also has R&D experience for Internet products and businesses such as Tencent Cloud, Tencent WeChat Automotive / AI in Car, Tencent Games, Tencent Maps, Baidu Maps, and Huawei Smartphone. He obtained three years of experience in R&D team management at Tencent between 2016-2018. With his advisors, he also co-founds FedML.ai, built based on a paper that won Best Paper Award at NeurIPS 2020 FL workshop. [Production, Awards]
In pure scientific research, He is equally interested in machine learning algorithms and systems. From the perspective of algorithmic ML, his research interest is machine learning algorithms, modeling, and their applications on computer vision, natural language processing, and data mining. In the system domain, he is particularly interested in distributed systems, cloud computing, mobile computing, embedded operating systems. The goal of his Ph.D. career is to develop distributed/federated, automated, and trustworthy machine learning algorithms and systems. Recently, He is focusing on:
1. Large-scale distributed training algorithms and systems for massive-scale DNN models (Transformers, ViT, BERT, MoE, 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)
FedCV! Funded by Konica Minolta.inc, to develop FedML for diverse computer vision applications.
Frontiers of Distributed Machine Learning: distributed pre-training of large-scale models (e.g., Transformers) for Computer Vision and Natural Language Processing
Open, Programmable, and Secure 5G
Intel/NSF Project on Machine Learning at the Wireless Edge!