Zhongzhu Zhou / Charlie Zhou
/ZHONG-JOO JOH/
Senior Research Scientist
Turbo Team, Together AI
Ph.D., School of Computer Science, Faculty of Engineering, The University of Sydney
B.Eng. (Hons), School of Computer Science and Engineering, Sun Yat-sen University
🏢 Office: Room 408, J12/1 Cleveland St, Darlington NSW 2008
📮 Email: zhongzhu.zhou [at] sydney.edu.au, zhouzhzh8 [at] mail2.sysu.edu.cn
"Let everything happen to you. Beauty and terror. Just keep going. No feeling is final." Rainer Maria Rilke
Who am I?
I am a Senior Research Scientist at the Turbo Team, Together AI, supervised by Ben Athiwaratkun.
I am a Ph.D. at the School of Computer Science, Faculty of Engineering, The University of Sydney, supervised by Prof. Shuaiwen Song. I have been fortunate to intern at Dolby, DeepSpeed Microsoft, Weixin Group Tencent, and Microsoft (China), contributing to machine learning systems and large-scale training and inference projects. I was also a research associate at the School of Computer Science and Engineering, Sun Yat-sen University from 2019 to 2022, under the supervision of Prof. Dan Huang and Yutong Lu. I received my B.E. degree from the School of Computer Science and Engineering, Sun Yat-sen University in 2019.
Research Highlights
My research spans efficient machine learning and systems, from model pretraining quality to efficient algorithms and system co-design that bridge emerging ML and LLM methods with real-world applications. I focus on improving both productivity and performance, with particular emphasis on LLM efficiency and scalable training infrastructure across academia and industry. My research is mainly supported by Together AI.
Feel free to drop me an email if you have aligned interests.
Currently, I am working on the following projects:
(🔥 indicates the projects I am leading)
Efficient ML Algorithm
- Imitate Optimal Policy: Prevail and Induce Action Collapse in Policy Gradient (Efficient RL training) 🔥
- CARE: Covariance-Aware and Rank-Enhanced Decomposition for Enabling Multi-Head Latent Attention (Efficient inference) 🔥
- SQUEEZE THINK: Multi-Model Orchestration for Efficient Recursive Self-Aggregation (Efficient inference)
- Bio-Inspired LLM-Based Multiagent Systems (Efficient agent inference-time training)
Efficient ML System
- XoRL (RL training system) 🔥
- Aurora: When RL Meets Adaptive Speculative Training: A Unified Training-Serving System (Speculator training system design)
- Fair Scheduler (Efficient agent fairness system)
Model Related
- CoderForge 🔥