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Five faculty jumpstart AI-health care projects

Aug. 5, 2026
Three doctors work at an operating table

The University of Arizona is funding 12 research teams to improve diagnosis, treatment, patient care and medical training with AI. ECSE faculty are collaborating on half of these projects.

The University of Arizona has awarded nearly $1 million to a dozen faculty research teams developing artificial intelligence tools to improve health care. Faculty from the School of Electrical, Computing, and Software Engineering are working on half of these teams, helping to benefit diagnosis, treatment, and patient care. 

“We encouraged teams to think boldly, pursue innovative ideas and build collaborations that extend beyond traditional boundaries,” said David Ebert, Computer Science Engineering Endowed Innovation Chair and the university’s inaugural chief AI officer. “By fostering this kind of convergent research, we can accelerate discoveries, improve health outcomes and create new opportunities for impactful research that benefits communities in Arizona and beyond.”

ECSE faculty are involved in six funded research teams:

  • Leveraging Multimodal AI to Standardize Deceased Donor Kidney Evaluation: Bekir Tanriover, College of Medicine – Tucson; with Ali Bilgin, College of Engineering; Marek Rychlik, College of Science; and Maryam Emami, College of Medicine – Tucson.
  • AI-Driven Surgical Excellence: Multimodal Digital Twins in Surgical Training: Ehsan Azimi, College of Engineering; with Eduardo Blanco, College of Science; Geoffrey Gurtner, College of Medicine – Tucson; and Iman Ghaderi, College of Medicine – Tucson.
  • AI-Driven Image Classification for Deep Vein Thrombosis Diagnosis: Eung Joo Lee, College of Engineering; with Srikar Adhikari, College of Medicine – Tucson.
  • AI-Based Prediction of Drug Response in Colorectal Cancer: Marwan Krunz, College of Engineering; with Curtis Thorne, College of Medicine – Tucson.
  • Remote Ecosystem for Physics-Aware Intelligent Rehabilitation (REPAIR): Christopher Arellano, College of Medicine – Tucson; with Nicholas Adam Bonazza, College of Medicine – Tucson; and Janet Roveda, College of Engineering.
  • Precision Equanimity: AI-Enabled Disentanglement of Metabolic and Psychophysiological Arousal for Real-Time Stress Regulation: Chang-Chun Chen, College of Engineering; with Richard Lane, College of Medicine – Tucson; John JB Allen, College of Science; Janet Roveda, College of Engineering; and Shu-Fen Wung, College of Engineering.