School of Medicine
Showing 1-7 of 7 Results
Saisindhu Narala, MD, MAS
Clinical Assistant Professor, Dermatology
Clinical Assistant Professor, Pathology
BioDr. Narala completed her dermatology residency at the University of Texas at Houston and MD Anderson Cancer Center. She then completed a fellowship in dermatopathology at Stanford. Her clinical interests include general medical dermatology, dermatology in skin of color, and pigmentary disorders. She also has an interest in medical education.
MD Student, expected graduation Winter 2027
Stanford Student Employee, Dermatology
BioVanessa Nava is a first year medical student interested in Mental Health, Culinary Medicine, and Social Justice. She has co-authored several publications with Dr. Eleni Linos in the Department of Dermatology. https://pcrt.stanford.edu/linoslab She enjoys mentoring low-income, first-generation students.
Kristin M. Nord, MD
Clinical Professor, Dermatology
BioKristin M. Nord, M.D., is a Clinical Professor of Dermatology and served as Residency Program Director from 2012-2022. Dr. Nord received her doctor of medicine from the Columbia University College of Physicians and Surgeons, and completed her residency in Dermatology at New York Presbyterian-Columbia University Medical Center. Dr. Nord is Attending Physician at the VA Palo Alto Health Care System, where her clinical interests are general dermatology, complex medical dermatology and procedural dermatology, and she serves as Assistant Co-Chief of Dermatologic Surgery. Her research focus is on skin cancer education and prevention, and she is co-faculty lead for SUNSPORT (Stanford University Sun Protection Outreach Research and Teamwork).
Roberto Novoa, MD
Clinical Associate Professor, Pathology
Clinical Associate Professor, Dermatology
Current Research and Scholarly InterestsMy research interests include the medical applications of artificial intelligence, cutaneous lymphoma, and the side effects of targeted therapies. I have served as the lead dermatologist in our ongoing effort to develop AI-augmented classification of skin lesions. We are in the process of establishing one of the first prospective studies examining the performance of a deep learning algorithm in real-world patients.