Qusai Almustafa

Qusai Almustafa is a master’s student in Electrical and Computer Engineering at the University of Arizona and a Research Technologist at the university’s Digital Health Technology & Sensor (DHTS) Core. His academic and professional interests lie at the intersection of engineering, artificial intelligence, and healthcare, with a focus on applying emerging technologies to improve human health and advance medical research.

His research interests include machine learning, physiological signal processing, wearable sensor technologies, and AI-driven healthcare solutions. At the DHTS Core, Qusai supports interdisciplinary research by developing advanced data processing and machine learning pipelines, analyzing large-scale physiological datasets, and integrating wearable sensors and emerging technologies into research applications. His work also involves developing computational tools, managing research computing infrastructure, and collaborating with researchers across disciplines to transform complex sensor data into meaningful insights.

With a background in both electrical and computer engineering, Qusai enjoys combining software development, data science, and hardware technologies to address real-world challenges. He is particularly passionate about bridging the gap between engineering and medicine, developing intelligent systems that support healthcare research, improve patient outcomes, and make advanced technologies more accessible. He also enjoys mentoring students, sharing technical knowledge, and contributing to collaborative research environments.

Looking ahead, Qusai hopes to continue advancing research in artificial intelligence and digital health, with a long-term goal of developing innovative technologies that make a meaningful impact on healthcare and society.

Outside of his academic and professional pursuits, Qusai enjoys traveling, hiking, camping, and exploring new places and cultures. He values opportunities to connect with people from different backgrounds, experience new environments, and continually learn both inside and outside the laboratory.