Ziyi Xia - Quantitative Researcher

Ziyi Xia

Quantitative Researcher

New York, New York, United States

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Summary

🤩

Rockstar

🎓

Top School

Ziyi Xia is a quantitative researcher and first-year CS master’s student at Columbia University with four years of experience applying deep reinforcement learning to finance and AI research. She contributes to leading open-source FinRL and ElegantRL projects, focusing on documentation clarity and integrating DRL models for market simulation and backtesting. At AI4Finance and as a Columbia/IDEA research assistant she blends hands-on ML engineering—adding ElegantRL and RLlib model implementations—with technical writing that improves usability for practitioners. Brief stints at Point72 and industry roles in algorithm engineering underscore her ability to move prototypes toward production in quantitative trading environments. Her strong academic record (MS CS at Columbia, BA in Math & Engineering from Oberlin) complements a rare combination of rigorous research, code-level model integration, and user-focused documentation.

4 years of coding experience

1 year of employment as a software developer

Master of Science - MS, Computer Science, 3.92, Master of Science - MS, Computer Science, 3.92 at Columbia University

Bachelor of Arts - BA, Math, Engineering, GPA: 3.89, Bachelor of Arts - BA, Math, Engineering, GPA: 3.89 at Oberlin College

Github Skills (14)

deep-reinforcement-learning10

pytorch10

rs10

restructuredtext10

python10

sphinx10

documentation10

machine-learning9

api-documentation9

rllib8

tensorflow8

finance7

fintech7

reinforcement-learning6

Programming languages (2)

Jupyter NotebookPython

Github contributions (5)

AI4Finance-Foundation/FinRL

Dec 2021 - Jan 2023

FinRL®: Financial Reinforcement Learning. 🔥

Role in this project:

Technical Writer

Contributions:1 review, 223 commits, 6 PRs in 1 year 1 month

Contributions summary:Ziyi's commits primarily focus on updating and modifying documentation files within the repository. The changes involve restructuring documentation, adding and correcting links, and incorporating new content related to tutorials and the FinRL project's architecture. The user's work enhances the accessibility and clarity of the documentation for users and developers. These updates appear to be focused on providing clear explanations and navigation within the project's documentation.

stable-baselinesdrl-frameworkdeep-reinforcement-learningsecfinance

AI4Finance-Foundation/FinRL-Meta

Jun 2022 - Jan 2023

FinRL-Meta: Dynamic datasets and market environments for FinRL.

Role in this project:

ML Engineer

Contributions:43 commits, 4 PRs, 47 pushes in 7 months

Contributions summary:Ziyi contributed to the development of machine learning models within the FinRL-Meta repository. Their work included updating training scripts (`train.py`), modifying core modules such as `main.py` and `plot.py`, and adding new model implementations from ElegantRL and RLlib (through `agents/elegantrl_models.py` and `agents/rllib_models.py`), suggesting a focus on integrating and experimenting with different DRL algorithms. The changes also suggest involvement in model training, prediction, and backtesting.

data-drivenfinancialdrl-trading-agentsmetaversereinforcement-learning

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