Research & Building
My work spans the full stack - from training AI models to governing their risks.
From designing transformer architectures for financial time series to graph neural networks that model financial market dynamics, my work bridges cutting-edge research with practical applications.
During my PhD studies at Singapore Management University (2020–2023), my research focused on dynamic multimodal networks - combining structured data, text, images, and network relationships for financial applications.
I received the SMU Presidential Doctoral Fellowship in 2022. I still review papers for conferences such as ACL, UIST, and AIES (under AAAI).
More recently, I was a co-author of a paper on scalable runtime governance for agentic AI in financial services, bridging my technical research background with my work in AI risk management.
Lukasz Szpruch, Agus Sudjianto, Tanveer Bhatti, Gary Ang · SSRN
Gary Ang, Ee-Peng Lim · ACM TWEB
Gary Ang, Ee-Peng Lim · ACM TIST 2024
Gary Ang, Ee-Peng Lim · ACM TMIS 2023
Gary Ang, Ee-Peng Lim · IEEE Big Data 2022
Gary Ang, Ee-Peng Lim · ACM TiiS 2022
Gary Ang, Ee-Peng Lim · ACM TWEB
Gary Ang, Ee-Peng Lim · ACM TiiS 2022
Gary Ang, Ee-Peng Lim · ACL 2022
Gary Ang, Ee-Peng Lim · ACM IUI 2022 Honorable Mention
Gary Ang, Ee-Peng Lim · ACM ICAIF 2021
Gary Ang, Ee-Peng Lim · ACM IUI 2021
GOT - The AI Governance Tool
Generates tailored AI governance reports with applicable standards, controls, and implementation guidance based on your specific AI use case.
Agentic MRM
A demo showing how the concepts in the agentic AI model risk management paper by Lukasz, Agus, Tanveer, and me work in practice.
AI Agents for Investing
A practical ebook on how agentic AI systems work and how they apply to investing - from workflow design to risk considerations. Includes an accompanying app to explore the concepts hands-on.
Check out sample projects and apps on GitHub.