Biography
I am a Senior Research Scientist at ByteDance Seed, where I work on large-scale reinforcement learning training infrastructure for foundation models (LLMs and VLMs). My work focuses on machine learning systems and distributed systems for efficient and stable post-training.
I received my Ph.D. in Computer Science from the University of Illinois Urbana-Champaign and my B.Eng. in Computer Engineering from the University of Hong Kong.
Previously, I worked at Anyscale, Jane Street, Meta, and Google.
Interests
- Reinforcement Learning Systems
- Distributed Systems
- Large-Scale Model Training
- Machine Learning Infrastructure
Education
Ph.D. in Computer Science, 2024
University of Illinois, Urbana Champaign
B.Eng. in Computer Engineering, 2018
University of Hong Kong
Experience
Senior Research Scientist
Software Engineer
Quantitative Trading Intern
Software Engineer Intern, Machine Learning
Software Engineer Intern
Featured Publications
(2023).
ScaleFlow: Efficient Deep Vision Pipeline with Closed-Loop Scale-Adaptive Inference.
In ACM MM 2023.
(2022).
BoFL: Bayesian Optimized Local Training Pace Control for Energy Efficient Federated Learning.
In Middleware 2022.
(2022).
Multi-View Scheduling of Onboard Live Video Analytics to Minimize Frame Processing Latency.
In ICDCS 2022.
(2021).
CrossRoI: Cross-camera Region of Interest Optimization for Efficient Real Time Video Analytics at Scale.
In MMsys 2021.
(2021).
DeepRT: A Soft Real Time Scheduler for Computer Vision Applications on the Edge.
In SEC 2021.
(2020).
Secure Broadcast Protocol for Unmanned Aerial Vehicle Swarms.
In ICCCN 2020.
(2019).
Thanos: Incentive Mechanism with Quality Awareness for Mobile Crowd Sensing.
In TMC 2019.
(2019).
Dynamic Task Pricing in Multi-Requester Mobile Crowd Sensing with Markov Correlated Equilibrium.
In INFOCOM 2019.
Contact
- hpguosherlock@gmail.com
- 620 N 6th St, San Jose, CA