Zhongzhu Zhou / Charlie Zhou $\approx$ /ZHONG-JOO JOH/
Senior Research Scientist [Incoming] / Research Consultant
Turbo Team, Together AI
Ph.D. Candidate, School of Computer Science, Faculty of Engineering, The University of Sydney
🏢 Office: Room 408, J12/1 Cleveland St, Darlington NSW 2008
📮 Email: zhongzhu.zhou [at] sydney.edu.au
$\mathcal{“Let\ everything\ happen\ to\ you.\ Beauty\ and\ terror.\ Just\ keep\ going.\ No\ feeling\ is\ final.”\ -\ Rainer\ Maria\ Rilke}$
Who am I?
I am a Senior Research Scientist [Incoming] / Research Consultant at the Turbo Team, Together AI, supervised by Ben Athiwaratkun.
I am a Ph.D. candidate at the School of Computer Science, Faculty of Engineering, The University of Sydney, supervised by Prof. Shuaiwen Song.
Prior to my current position, I have been fortunate to intern at Dolby, DeepSpeed Microsoft, Weixin Group Tencent, and Microsoft (China), contributing to projects in building machine learning systems.
I was also a research assistant at the School of Computer Science and Engineering, Sun Yat-sen University from 2019 to 2022, under the supervision of Prof. Dan Huang, Yunfei Du, and Yutong Lu.
I received my B.E. degree from the School of Computer Science and Engineering, Sun Yat-sen University in 2019. My research is mainly supported by Together Computer.
Research Highlights
My research focuses on multiple aspects of the efficient machine learning system stack, including the neural network algorithms & architecture design, and training & inference system optimization. Specifically, I aim to enhance and bridge the gap between emerging machine learning algorithms & applications and efficient heterogeneous hardware (CPU/GPU) training & inference systems in terms of productivity and performance.
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 LLM Track
- LLM’s Reasoning Steering
- Coding Agent Training
- Multi-Latent Attention Application on General Transformer Model 🔥
- RL Training Service Analyze and Build 🔥
🔴 Efficient Machine Learning System Track
- Efficient Mobile Video Compress & Streaming System Design 🔥
- Pre-Expedite: Use Hierarchical Structure Space for Improving the Performance of Accessing Small Files in Parallel File System 🔥
- Hybrid-Share: Universal Resource Scheduling for Hybrid Jobs on Supercomputers 🔥
- EmReal: A Digital Twin Framework of Emulated and Real Components for Robots with Reinforcement Learning 🔥