Hojjat Salehinejad

"I design AI systems that sense, understand, and anticipate risk in complex environments, enabling safer and more reliable decisions."

My research focuses on making artificial intelligence reliable outside the lab: learning from limited data, operating continuously, and supporting human decision-making under uncertainty. Healthcare serves as the primary deployment setting, where robustness, trust, and real-world impact matter most.

Short Bio I’m an Associate Professor of Health Care Systems Engineering at Mayo Clinic in Rochester, MN, USA. My work focuses on building practical and trustworthy AI systems for healthcare — tools that help clinicians anticipate risks, support decisions, and improve patient care. I received my PhD in Electrical and Computer Engineering from the University of Toronto in Canada and later completed a postdoctoral fellowship there in machine learning. Along the way I’ve been fortunate to receive support from programs such as the NSERC Postdoctoral Fellowship, Ontario Trillium Scholarship, and Mitacs Accelerate. I’m actively involved in the research community particularly through IEEE, where I found and lead the AI in Healthcare Task Force within the Government and Industrial Activities Committee of the IEEE Computational Intelligence Society and serve as an associate editor for IEEE Transactions on Cybernetics and IEEE Transactions on Neural Networks and Learning Systems. I enjoy collaborating across academia, healthcare, and industry to translate AI ideas into systems that actually work in practice.