I am a fourth year PhD student at the Computer Science Department of Northwestern University. My research is focused on hand-related activity recognition using wearable devices and machine-learning. Recently, I interned at Apple where I worked on multiple machine-learning projects using different sensing technologies. You can find my resume here.
My research is situated at the intersection of human activity sensing and machine learning, with a specific focus on hand-related activities in health and human-computer interaction (HCI) contexts. I have developed techniques to enhance privacy and efficiency in detecting hand-related activities. More recently, I have investigated data-efficient methods for recognizing hand gestures.
A wearable necklace with a deep-learning data analysis pipeline that automatically detects smoking and extracts smoking topography. SmokeMon has received worldwide attention from news media outlets including BBC, WebMD, Fox News, DigitalTrends, NPR. Northwestern University has filed a patent for this work. https://dl.acm.org/doi/abs/10.1145/3569460
Work done during internship at Apple and accepted in UIST'24. We employ transformers and meta-learning techniques to address few-shot learning challenges. Unlike prior work, our method supports any combination of one-handed, two-handed, static, and dynamic gestures, including different viewpoints, and the ability to handle irrelevant hand movements. https://arxiv.org/abs/2402.08420