Chaoyang He (何朝阳)
Ph.D. Student in Computer Science
University of Southern California, Los Angeles, USA
Team Manager and Staff Engineer at Tencent (2014-2018)
Senior Software Engineer at Baidu (2012-2014)
chaoyang.he [at] usc [dot] edu
me [at] chaoyanghe [dot] com
Chaoyang He is a Computer Science Ph.D. student focusing on Machine Learning at University of Southern California, Los Angeles, USA. Previously, he was an R&D Team Manager and Principal Engineer at Tencent (2014-2018), a Team Leader and Senior Software Engineer at Baidu (2012-2014), and a Software Engineer at Huawei (2011-2012).
Currently, his academic advisors are Professor Salman Avestimehr (USC, machine learning theory, distributed learning, federated learning), and Tong Zhang (HKUST, machine learning optimization, AutoML, CV/NLP).
He has a strong background in Internet industrial level computer science, including cloud computing architecture, machine learning, and data computing platform, distributed systems, mobile computing, and embedded operating system.
He has 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 he worked for include
Tencent Cloud ,
Tencent WeChat Automative / AI in Car (news report, a talk by CEO Pony Ma ), Tencent Games,
and Huawei Smartphone.
My research focuses on the field of machine learning and AI.
I care about the next generation of AI: efficient and automated learning from small data that is decentralized, multimodal, and weakly supervised.
To achieve this long-term goal, I am particularly interested in automated machine learning (AutoML), federated learning, multimodal machine learning, distributed systems, mobile computing,
and their applications in computer vision, NLP, speech, robotics, transportation, health care, finance, and online education.
Working in Progress
- FedML: A Research Library and Benchmark for Federated Machine Learning
Chaoyang He*, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu,
Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram*, Salman Avestimehr*
* means corresponding authors.
Highlights: We are building an open source project for federated learning!
- Group Knowledge Transfer: Collaborative Training of Large CNNs on the Edge
Chaoyang He, Murali Annavaram, Salman Avestimehr
Keywords: Alternating Minimization (AM), Non-convex Optimization, Federated Learning, Computer Vision
- Central Server Free Federated Learning over Single-sided Trust Social Networks
Chaoyang He, Conghui Tan, Hanlin Tang, Shuang Qiu, Ji Liu
Keywords: Distributed Machine Learning, Convex Optimization, Federated Learning, Online Learning
- MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation
[bib] [arXiv] [video]
Chaoyang He*, Haishan Ye*, Li Shen, Tong Zhang
To appear in CVPR 2020 (IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020)
Keywords: Neural Architecture Search, AutoML, Machine Learning Optimization, Computer Vision.
- FedNAS: Federated Deep Learning via Neural Architecture Search
[bib] [arXiv] [code] [video]
Chaoyang He, Murali Annavaram, Salman Avestimehr
Accepted to CVPR 2020 Workshop on Neural Architecture Search and Beyond for Representation Learning
Keywords: AutoML, Neural Architecture Search, Federated Learning, Computer Vision
- Bipartite Graph Neural Networks for Efficient Node Representation Learning
Chaoyang He*, Tian Xie*, Yu Rong, Wenbing Huang, Yanfang Li, Junzhou Huang, Xiang Ren, Cyrus Shahabi
Keywords: Graph Neural Networks, Self-supervised Learning, Adversarial Learning, Efficient Neural Architecture
- Advances and Open Problems in Federated Learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael GL d'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adria Gascón, Badih Ghazi, Phillip B Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrede Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X Yu, Han Yu, Sen Zhao
To appear in Foundations and Trends in Machine Learning (FnTML), 2019
with Google AI, Seattle, WA, USA
- Collecting Indicators of Compromise from Unstructured Text of Cybersecurity Articles using Neural-Based Sequence Labelling
Zi Long, Lianzhi Tan, Shengping Zhou, Chaoyang He, Xin Liu
International Joint Conference on Neural Networks (IJCNN), 2019
Keywords: Natural Language Processing, Attention-based Bi-LSTM
(If you are interested in the following topics, please email me to discuss the potential collaboration.)
- 2018 - Data-Driven Cloud Computing Platform and Machine Learning System for the Internet of Vehicles
- 2016 - The Large-Scale Distributed System and Real-Time Location Data Mining
- 2017 - TAI: An Intelligent Operating System and Open Platform
- 2016 - Speech Recognition System for Connected and Autonomous Driving Car
- 2016 - WeGameMap: Real World Map Rendering Engine for Location-Based Mobile Game
- 2015 - WeLink: a High-Performance Vehicle-Mobile Networking Library
- 2014 - TMAP: A System Framework for Mobile Maps and Navigation
- 2016 - Efficient Spatial Anti-Aliasing Rendering for Line Joins on Vector Maps
- 2014 - WeCross: a Mobile and Vehicle C/C++ Cross-Platform Library
- 2017 - A High Precision Private Car Trajectory Dataset and an Open Source Location Data Computing Platform
- 2017 - A Open Source High Reliable Location SDK for the Automotive Industry
- 2013 - The First Generation Mobile Navigation SDK Design in China
- 2012 - Map State Switching under Multi-trigger Condition Based on Finite State Machine Design Pattern
- 2012 - A General Downloader Engine for iOS/Android Platform
- 2012 - The Best Practice for Java Native Interface (JNI) Development on Android Operating Platform
- 2014 - Taxi-Calling Platform and the Open Source Code
- 2015 - A UX design of Bluetooth Steering Wheel Wireless Controller and the communication system design
- 2016 - Sliding Window Algorithm Template to Solve All the Leetcode Substring Search Problem.
Honors and awards
- Tencent Outstanding Staff Award (selection ratio < 1%, led to a promotion as Team Manager), 2015-2016
- WeChat MyCar, Special Award for Innovation, Mercedes-Benz, Daimler Supplier Award 2016 (teamwork; I worked as the technical leader)
- Baidu LBS Group Star Awards (6 holders among 1000+ employees), 2013
- Huawei Golden Network Award in 2012
- Province-level Outstanding Undergraduate Thesis Awards, 2008
- The First Prize Scholarship for undergraduate students, 2007
- CSCI 567-Machine Learning, with Professor Victor Adamchik, Summer 2019
- CSCI 567-Machine Learning, with Professor Victor Adamchik, Fall 2019
- INF 552-Machine Learning for Data Science, with Professor Satish Kumar Thittamaranahalli, Spring 2020
I documented an open and editable Teaching Statement at https://www.overleaf.com/6136771432bggvjvhshrkz, including my experience introduction, teaching philosophy, future plan, and some useful notes in the machine learning course. I will update it once I have more insights on teaching.
- Team manager at Tencent, led a SDE team including 15+ software engineers, 2015-2018
- Team leader at Baidu, led 11 software engineers, 2013-2014
Courses in the United States
Last updated: 2020/01.
- CSCI699-Advanced Topics in Deep Learning, Spring 2019
- CSCI699-Advanced Computer Vision, Fall 2019
- CSCI699-Machine Learning for Knowledge Extraction and Reasoning (a PhD-level NLP course), Spring 2019
- CSCI599-Deep Learning and its Applications, Spring 2019
- CSCI698 Practicum in Teaching Computer Science, Spring 2019
- CSCI698 Practicum in Teaching Computer Science, Spring 2020
- CSCI697-Seminar in Computer Science Research, Fall 2018
- CSCI697-Seminar in Computer Science Research, Fall 2019
- ENGR502x-Writing Skills for Engineering Ph.D. Students, Fall 2018
- ALI246-Intermediate Oral Communication for ITAs, Fall 2018