Overview
I work with domain experts across different fields to solve challenging real-world problems through the application or development of advanced signal processing, computer vision, machine learning (especially deep learning) methods to real-world problems. Recently, my work has spanned topics such as remote sensing, energy systems, and materials science. My work has recently been featured in premiere machine learning conferences (e.g., NeurIps, ICLR) and computer vision conferences (e.g., WACV).
Current Appointments & Affiliations
Adjunct Assistant Professor in the Department of Electrical and Computer Engineering
·
2022 - Present
Electrical and Computer Engineering,
Pratt School of Engineering
Recent Publications
Are deep learning models robust to partial object occlusion in visual recognition tasks?
Journal Article Pattern Recognition · March 1, 2026 Image classification models, including convolutional neural networks (CNNs), perform well on a variety of classification tasks but struggle under conditions of partial occlusion of relevant objects. Methods to improve performance under occlusion, including ... Full text CitePhysics-informed learning in artificial electromagnetic materials
Journal Article Applied Physics Reviews · March 1, 2025 The advent of artificial intelligence—deep neural networks (DNNs) in particular—has transformed traditional research methods across many disciplines. DNNs are data driven systems that use large quantities of data to learn patterns that are fundamental to a ... Full text CiteLearning Electromagnetic Metamaterial Physics With ChatGPT
Journal Article IEEE Access · January 1, 2025 Large language models (LLMs) such as ChatGPT, Gemini, LlaMa, and Claude are trained on massive quantities of text parsed from the internet and have shown a remarkable ability to respond to complex prompts in a manner often indistinguishable from humans. Fo ... Full text CiteEducation, Training & Certifications
Duke University ·
2015
Ph.D.