Pooyan Kazemian

Pooyan Kazemian’s research lies at the intersection of artificial intelligence (AI), machine learning, and data-driven optimization, focusing primarily on healthcare management applications. His work addresses pressing problems in the areas of healthcare operations, medical decision making, and health policy, aiming to improve quality of care, access to care, and health outcomes while reducing costs. Kazemian is particularly interested in novel applications of deep learning and AI to enhance the operational aspects of healthcare delivery and support complex medical and policy decisions. He also employs machine learning and robust optimization techniques to develop personalized disease monitoring and treatment strategies and to inform evidence-based public health policy.

Another area of Kazemian’s research involves developing novel methods for trustless and verifiable AI by leveraging advanced cryptographic techniques, such as zero-knowledge proofs. These methods aim to improve the security, trustworthiness, and reliability of medical AI tools, especially those deployed as Software as a Medical Device (SaMD).

Kazemian’s research has been published in leading journals such as Management Science, Production and Operations Management, JAMA Internal Medicine, and Annals of Internal Medicine. His work has been featured in major media outlets, including NPR, The New York Times, Science Codex, Yahoo Finance, and Business Standard.

Prior to joining UC San Diego in 2025, Kazemian was on the faculty at Case Western Reserve University (2020–2025) and Harvard Medical School (2017–2020). He received his Ph.D. in Industrial and Operations Engineering from the University of Michigan in 2016.