About
I am passionate about and have a firm foundation in Machine Learning, and Multimodal Perception. Much of my experience is in applying these technologies to enhance autonomous or defense capabilities.
Research papers that excite me:
Background
I received my Bachelor of Science in Electrical Engineering from UIUC ECE. After my undergraduate education, I worked in the automation, and semiconductor industries for a couple years. In order to pivot towards Machine Learning, I chose to attend UW ECE where I received my Master of Science in Electrical Engineering. In this program, built a strong background in machine learning, deep learning, and computer vision by working as a ML/AI Research Assistant in several labs and through courses. For one of my assistantships I built synthetic data generation and evaluation pipelines for foundation model pose estimation, achieving sub-mm error, enabling robots to successfully grasp unseen objects.
At Sandia National Laboratories, as a R&D ML intern, I trained and evaluated a CNN that uses uncertainty estimation to detect unseen anomalies in national security devices, achieving a recall of 100%. As a Post-Masters R&D Machine Learning Intern at Los Alamos National Laboratories, I tuned the loss weights of a Shock Time-Series CVAE to achieve a performance 80% better than traditional analysis methods. I am also developing score-based and latent diffusion models to generate shock time-series signals.
I also have a strong background in Data Structures & Algorithms and make YouTube videos that visually explain important algorithms: study2simple
