Neema Jamshidi

Dr. Neema Jamshidi is an Associate Professor-in-residence in the Department of Radiological Sciences at the University of California Los Angeles (UCLA) School of Medicine. With a research career spanning over two decades, Dr. Jamshidi has established herself as a leading expert at the intersection of systems biology, radiogenomics, and medical imaging. Her work bridges computational approaches with clinical applications, particularly in cancer research and metabolic disorders.

Dr. Jamshidi's research interests focus on systems biology approaches to metabolic networks, radiogenomics (the correlation between imaging features and genomic data), and the application of computational methods to medical imaging. Her work has significantly advanced our understanding of pancreatic cancer through radiogenomic mapping, developed innovative computer vision algorithms for surgical planning, and explored the metabolic underpinnings of various diseases including diabetes and fatty liver disease. Her research integrates computational modeling with clinical imaging to develop precision medicine approaches.

Analysis of Dr. Jamshidi's publication record reveals a strong trajectory in systems biology and radiogenomics, with increasing focus on pancreatic cancer, metabolic network modeling, and artificial intelligence applications in medical imaging. Her most recent work demonstrates a sophisticated integration of computational biology with clinical radiology, particularly in developing predictive models for cancer treatment outcomes and advancing our understanding of metabolic pathways through novel imaging techniques.

Dr. Jamshidi leads the SIML (Systems Imaging and Metabolic Laboratory) at UCLA, where her team develops and applies computational approaches to medical imaging problems. Her laboratory serves as a hub for interdisciplinary research, bringing together experts in radiology, systems biology, computer science, and clinical medicine.

Supported by multiple NIH grants including an NCI R37 MERIT award, Dr. Jamshidi's current research focuses on systems metabolic approaches for multi-scale pancreatic cancer phenotyping, investigating PFKFB3 polymorphism and lactate signaling in diabetes-related pancreatic tumor promotion, and developing AI platforms to predict surgical resectability of pancreatic cancer based on preoperative CT scans.