Building AI systems that make sense of markets, language, and risk. Researching LLMs, multimodal models, and RAG at the frontier of finance.
I'm a data scientist and AI researcher with 10+ years of experience applying machine learning to high-stakes problems. Currently Head of Data Science at S&P Global, directing AI strategy across Investor Sentiment, Cybersecurity, ESG, and Generative AI — with a $10M+ yearly revenue impact through pricing optimization.
Before S&P, I worked at Praedicat detecting emerging risks in scientific literature using deep learning & NLP. I hold an M.Sc. in Data Science from NYU (GPA 3.81), where I was a Teaching Assistant for Prof. Yann LeCun's legendary Deep Learning class and did NLP research under Prof. Kyunghyun Cho.
I've filed 2 US Patents, published at IEEE and ACM conferences, and been recognized with multiple awards including the S&P Global Hall of Fame. My current research explores Knowledge Graphs, cost-efficient LVLMs, and RAG for long-context financial documents.
IEEE and ACM publications spanning multimodal LLMs, RAG systems, cybersecurity AI, and financial ML. Two US Patents filed in AI & GenAI.
Pushing the frontier where language meets vision meets financial reasoning.
A selection of research systems and AI tools spanning finance, cybersecurity, and multimodal intelligence.
Sharing what I learn at the frontier of AI, finance, and research. Deep dives, tutorials, and opinions.
Working on deep-dive articles about LLMs, RAG, multimodal AI, and practical ML in finance. Be the first to read.
Whether you want to collaborate on research, discuss AI in finance, explore speaking opportunities, or just geek out about LLMs — I'd love to hear from you.