About Me

I am a machine learning scientist with expertise in time series and graph neural networks, currently working at Charles Schwab.

Before, at Pendulum Systems (2023-2025), I worked in research, development and deployment of statistical and machine learning algorithms for solving complex problems in logistics and supply chain, including time series forecasting for demand prediction, recommendation systems for improved client networks, sensor-based navigation in GPS-denied environments, and LLM-driven systems for customer interaction that provide tailored supply-chain planning solutions.

Before that, I worked at Morgan Stanley (2022-2023) in research and development of statistical and machine learning tools for pricing securities (fixed income and equities), leveraging machine learning algorithms for multivariate time series imputation and forecasting of both regular and irregular time series, using tools such as graph neural networks, sequential-based methods, and attention mechanisms.

I received my Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania (2020), and an M.A. in Statistics from the Wharton School (2017). I had postdoctoral stints at the University of California, Berkeley (2020/21) and at Rice University (2021/22).