📰 __ Researcher

Ph.D. @ HKU | Specializing in Efficient AI, System Co-Design, and LLM Training

I am a research scientist at Huawei, working on LLM post-training.

Previously, I completed my PhD at HKU. Before that, I received my B.Eng./B.S. in IC Design from HQU/HKUST. I’ve interned/worked at UCSB and Samsung.

📫 zhoutomas177@gmail.com(Personal)

✉️ ryjjc@connect.hku.hk

📍 Hong Kong SAR (Now)

AI Pyramid (as below): the layered ecosystem of artificial intelligence, progressing from chips at the base to applications and agents at the top. It highlights how each upper layer depends on the robustness of the lower level, forming a complete stack from silicon to intelligent systems.

💼 Experience

Researcher @ Huawei Hong Kong Research Center

May. 2025 – Now

LLM Training & RL framework

Research Intern @ Huawei Hong Kong Research Center

Sep. 2025 – Feb. 2026

LLM Infra

Research Intern @ Samsung Research America, Mountain View

May. 2025 – Aug. 2025

On-device LLM framework

Research Associate @ UCSB, Santa Barbara

Sept. 2023 – Apr. 2025 | Advisor: Prof. Zheng Zhang

Developed efficient training techniques for LLMs
Collaborated with the Amazon AGI team on scalable training paradigms

📝 Full publication list on Google Scholar

🚀 Research Highlights

Machine learning and systems, with a focus on efficient training and inference:

Efficient LLM Fine-Tuning: Developed low-bit frameworks to push the limit of parameter-efficient and quantized tuning strategies.
Hardware-Aware ML: Designed acceleration methods on FPGAs and NPU chips for DNN inference and edge AI.
Algorithm/Hardware Co-Design: Collaborated on the hardware compiler optimization across algorithms and model simulators.

📷 Fun Fact

I enjoy exploring the intersection of AI algorithms and hardware—whether it’s crafting efficient LLM models, squeezing memory on an edge chip, or analyzing training efficiency.

🤝 Academic

I’m passionate about bridging academia and decentralized technology—whether it’s co-authoring papers on efficient LLM training, collaborating with global research labs, or exploring LLM infrastructure projects that bring AI infrastructure and intelligent agents on-chain.

🔧 Technical Skills

Languages: Python, C/C++, MATLAB, Verilog
Frameworks & Platforms: PyTorch, TensorFlow (incl. Lite & Keras), CUDA
Tools: Cadence, Xilinx Vivado & ISE, HSpice, Modelsim, VS Code