About
As a Data Scientist at Capital One’s Artificial Intelligence Foundations team, I specialize in Natural Language Processing (NLP), Generative AI, and Large Language Models (LLMs). My work spans across various facets of distributed machine learning, including training and inference optimization, generative NLP, and tackling large-scale data science and engineering challenges.
In addition to my work at Capital One, I have a strong academic background. I earned my Ph.D. from Vanderbilt University and hold two M.S. degrees in Computational Engineering and Applied Mathematics from Delft University and the University of Erlangen-Nuremberg, respectively.
My research interests include the development of hybrid machine learning methods that integrate physics knowledge seamlessly. I have a particular focus on modeling uncertainty and developing multi-objective optimizations in response to uncertainty. This work involves Physics-Informed Machine Learning (PIML) for Uncertainty Quantification (UQ) and optimization. Currently, I take great pleasure in building GenAI models for Capital One using cutting-edge architectures and techniques.
In summary, my work and research are at the intersection of machine learning, computational engineering, and optimization under uncertainty, with a strong emphasis on practical applications and theoretical foundations.