About Me

I am a Quant Researcher at an asset management firm, developing deep learning models that leverage core statistical backbones to extract insights from complex market data.

Most recently, I was a member of the Machine Learning Research team at Morgan Stanley, where I designed and deployed deep learning models for forecasting and decision-support systems across equity and fixed income markets. I also contributed to academic research in the intersection of deep learning and statistics, with several co-authored papers published in leading ML and statistics venues. Earlier, I was a Quantitative Strategist at Goldman Sachs, building statistical and econometric models.

My academic background is in statistics, and my graduate research focused on high-dimensional time series and graphical models, as well as optimization algorithms.

Broadly, I am interested in:

• Deep learning models for complex dynamics with interpretable structure (e.g., diffusion, VAEs, contextual models with covariates)
• Modern LLM system techniques (e.g., RAG, LLM fine-tuning) and their adaptation to time-series forecasting
• Causal discovery and inference

Education

• Ph.D. in Statistics, University of Michigan
• B.S. in Mathematics & Statistics, University of Illinois at Urbana Champaign