Eric is a biomedical engineer with a multidisciplinary background spanning electrical engineering, wearable sensor systems, and machine learning–driven physiological monitoring. His work focuses on developing next-generation wearable and multimodal sensing technologies for non-invasive assessment of cardiovascular, respiratory, and metabolic health. Eric received both his B.S. and M.S. degrees in Electrical Engineering, where he built a strong foundation in analog/digital electronics, signal processing, and embedded system design. He later spent five years at Maxim Integrated as an Application Engineer, working closely with customers to support the development and validation of integrated circuit solutions for sensing and embedded applications. During his time in industry, he gained extensive experience in hardware validation, system integration, and translating complex engineering concepts into real-world product implementations—experience that continues to shape his research approach today. He went on to earn his Ph.D. in Biomedical Engineering from Texas A&M University, where his research focused on wearable physiological sensing and multimodal data analysis. His doctoral work involved the design and validation of wearable devices that integrate photoplethysmography (PPG), bioimpedance, and other sensing modalities with machine learning algorithms to enable non-invasive monitoring of key hemodynamic and metabolic parameters, including blood pressure, oxygen saturation (SpO₂), cardiac output, and oxygen consumption (VO₂). His research has resulted in multiple peer-reviewed publications and conference presentations, and he has contributed to both human subject and preclinical studies aimed at advancing continuous health monitoring technologies. Currently, Eric’s work centers on advancing wearable and point-of-care sensing platforms that bridge engineering innovation with clinical relevance. He is particularly interested in combining optical, electrical, and data-driven approaches to create robust, scalable systems for continuous health monitoring in both clinical and real-world environments.
Looking ahead, Eric aims to further integrate engineering, physiology, and data science to develop practical and impactful health technologies, with the long-term goal of translating advanced sensing and machine learning solutions into accessible tools that improve early detection, monitoring, and management of cardiopulmonary and metabolic diseases.