DoM Faculty Promotions!

Clinical Associate Professor

Gopala Yadavalli, MD, Infectious Diseases, is a clinician-educator focused on the care of inpatients. He splits his clinical time at Boston Medical Center (BMC) between attending on inpatient infectious diseases ward and consult services. Shortly after joining the faculty in 2011, Dr. Yadavalli co-created BMC’s inpatient infectious diseases ward team (MP ID) and the current version of the inpatient “Advanced Acting Internship” teams. The department’s current vice chair for education and former director of the Internal Medicine Residency Program, he has recruited and mentored faculty to co-create and implement numerous educational innovations, including eight individualized career pathways to augment traditional internal medicine training. He served as founding program director of the Ravin Davidoff Executive Fellowship in Health Equity, has served on the Council of the Association of Program Directors in Internal Medicine and chaired the Wellness Committee for the Alliance for Academic Internal Medicine.

Associate Professor

Finn Hawkins MBBCh, Pulmonary, Allergy, Sleep & Critical Care Medicine, is a Principal Investigator (P.I.) in the Center for Regenerative Medicine and The Pulmonary Center. His research focus is studying human lung development and disease using human induced pluripotent stem cells (iPSCs). Dr. Hawkins is currently using iPSC technology to study airway biology with a focus on Cystic Fibrosis.


Vipul C. Chitalia M.D., Ph.D., Nephrology, is a faculty member with a focuses on the role of post-translational modifications of proteins, especially polyubiquitnation of the key mediators of vascular pathologies in diseases such as cancer and renal failure. While these diseases are discrete, several fundamental biological processes remain similar. Through a highly collaborative network, our laboratory harnesses the power of various cellular and molecular biological tools, relevant animal models (zebrafish and mice), computational methods and machine-learning techniques and strives to validate these findings and hypotheses in humanized models or human samples from large data bases, which highlights the translational nature of our approach.