Keynote Lecture - Maruf Hossain Shuvo

KEYNOTE LECTURE

Maruf Hossain Shuvo

University of Texas at El Paso, TX, USA

The rapid proliferation of wearable sensors is creating new opportunities for continuous, personalized, and data-driven healthcare. However, translating sensor data into reliable decision support remains challenging because biomedical signals are often noisy, heterogeneous, patient-specific, and constrained by limited computation, memory, and power resources. This talk will present efficient deep learning and edge-AI approaches for wearable sensor-based biomedical decision support, with emphasis on compact model design, model compression, on-device inference, and resource-aware implementation. Several representative applications will be discussed, including personalized blood glucose prediction, diabetes self-care, sleep apnea detection from wearable and physiological signals, brain-computer interfaces, and activity recognition for rehabilitation. The talk will highlight how efficient AI techniques can support real-time inference, personalized prediction, and clinical decision support while reducing dependence on cloud-based computation. Key technical challenges will also be addressed, including robustness to sensor variability, personalization, edge deployment, privacy-preserving analytics, and system-level validation for wearable sensor-based biomedical decision support.

Md. Maruf Hossain Shuvo received the B.Sc. degree in Electronics and Communication Engineering from Khulna University of Engineering and Technology (KUET), Bangladesh, in 2014, and the M.S. degree in Electrical Engineering and the Ph.D. degree in Electrical and Computer Engineering from the University of Missouri, Columbia, MO, USA, in 2021 and 2024, respectively. He is currently an Assistant Professor in the Department of Electrical and Computer Engineering at The University of Texas at El Paso, TX, USA. Previously, he served as a Lecturer and Assistant Professor in the Department of Electronics and Communication Engineering at KUET from 2015 to 2019. His research interests include efficient deep learning, edge AI, circuits and systems for AI, biomedical electronics, and biomedical signal and image analysis. He is a recipient of the Outstanding Ph.D. Student Award and the EECS Travel Fellowship Award from the University of Missouri, the Prime Minister Gold Medal from the University Grants Commission of Bangladesh, and the University Gold Medal from KUET. He is an Editorial Board Member of Discover Computing, a Springer Nature journal.