Advances in machine learning (ML) algorithms have propelled AI across diverse sectors, including healthcare, agriculture, microelectronics, and cyber-physical systems, enabling task automation, predictive capabilities, and learned decision-making. However, computation-intensive AI techniques pose accessibility challenges, hindering widespread deployment. The research at EBMI laboratory seeks solutions, building compact deep learning (DL) model, algorithm-hardware co-design, and efficient AI acceleration on edge devices while ensuring explainability, privacy, and security.

Dr. Shuvo has been awarded a $230,000 grant from the University of Texas (UT) System STARS program to establish a new research laboratory within the ECE department at UTEP.

The EBMI Lab will be a hub for cutting-edge research in applied AI for healthcare and beyond. As we ramp up, expect access to a rapidly growing suite of state-of-the-art facilities. These include powerful GPU workstations for deep learning, wearable sensor devices for data acquisition, edge computing platforms that accelerate AI development, and measurement facilities. We will also provide access to the latest software packages (e.g., Cadence, Vivado, Sentaurus TCAD, Matlab, etc.).