The Department of Anesthesia and Perioperative Care is co-sponsoring a new medical student research fellowship along with the School of Medicine. The UCSF Department of Anesthesia and Perioperative Care Yearlong Fellowship supports the department’s longstanding mission to train and foster the development of the next generation of physician-scientists in anesthesiology. One fellowship will be awarded per year to a UCSF medical student interested in conducting research with an Anesthesia faculty mentor. During the fellowship, the medical student will take a year off from their clinical studies for full-time research, participate in department research activities such as the Anesthesia Research Day symposium, and will work towards a written product (e.g., a first-author manuscript). Students who complete this fellowship program will graduate with an MD with Distinction to recognize their research training.
2022-2023 UCSF Department of Anesthesia and Perioperative Care Yearlong Fellow
The inaugural fellowship was awarded to UCSF medical student Ishan Kanungo, left, who is conducting his yearlong research under the mentorship of Dr. Prasad Shirvalkar. Ishan received a BA double majoring in Molecular and Cell Biology with a concentration in Neurobiology and Psychology from the University of California, Berkeley in 2017. Before starting medical school, he co-founded a digital health startup creating a natural language processing platform for clinical research articles and went on to join the business process operations team in early clinical development at Genentech. He has also been involved in clinical research at the Aghi lab in the UCSF Department of Neurosurgery.
Fellowship Project: Dissecting human brain circuit mechanisms of chronic pain using ambulatory intracranial neural recordings
This project will investigate neural network dynamics and plasticity in patients with chronic pain using local field and evoked potentials in ambulatory and experimental settings. Using data from a deep brain stimulation clinical trial and tools from machine learning and graph theory, the study has the unique potential to identify network-level processes that underly the experience of chronic pain. The long-term goal of the study is to guide the development of targeted therapies for chronic pain.