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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.
GOT - The AI Governance Tool
Generates tailored AI governance reports with applicable standards, controls, and implementation guidance based on your specific AI use case.
Job AQ
An assessment that measures how ready individuals and teams are to work with AI.
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.
Check out sample projects and apps on GitHub.