Siyi Hu - Lecturer at Curtin University

Siyi Hu

Lecturer at Curtin University

Australia

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Summary

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Rockstar

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Top School

Siyi Hu is a lecturer and researcher with seven years' experience building intelligent, cooperative agents at the intersection of AI, multi-agent decision-making, and large language models, with applied work from satellite constellations to human-AI teaming. Holding PhD studies across leading institutions and a background in electrical and communications engineering, Siyi combines rigorous theory with hands-on system development. Her contributions to open-source MARL tooling (notably environment setup work in the Replicable-MARL/MARLlib repo) reflect a practical emphasis on reproducible multi-agent reinforcement learning environments using PyTorch. At Adelaide and now Curtin University she focuses on adaptive, decentralized agents that reason and coordinate under uncertainty, bridging research and real-world deployment. Colleagues describe her strength as turning complex coordination problems into tractable, engineering-ready solutions.

7 years of coding experience

Master's degree, Electrical, Electronics and Communications Engineering, Master's degree, Electrical, Electronics and Communications Engineering at Fudan University

Doctor of Philosophy - PhD, Artificial Intelligence, Doctor of Philosophy - PhD, Artificial Intelligence at University of Technology Sydney

Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Monash University

Github Skills (2)

python10

pytorch10

Programming languages (5)

C++CAstroSCSSPython

Github contributions (5)

Replicable-MARL/MARLlib

Dec 2021 - Jan 2023

One repository is all that is necessary for Multi-agent Reinforcement Learning (MARL)

Role in this project:

Back-end Developer

Contributions:10 releases, 375 commits, 87 PRs in 1 year 1 month

Contributions summary:Siyi's commits primarily focused on creating and modifying environment setup files, including changes to the `starcraft2_copy.py` file. The commit messages show a clear focus on setting up the environment and include substantial code modifications for the environment initialization process. The main goal of the commits appears to be establishing a functional MARL environment with tools like PyTorch.

pytorchraystable-baselinesreinforcement-learningmachine-learning

Theohhhu/Wireless_RL

Jun 2019 - Jun 2019

Contributions:30 commits, 26 pushes, 1 branch in 5 days

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