T32 Presentations-Day 1: Jae Ho Sohn, MD, MS and Kirti Magudia, MD, PhD
Date
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Type
Time Duration
Location
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Speakers
![](https://radiology.ucsf.edu/sites/radiology.ucsf.edu/files/styles/150w/public/fields/field_image/speakers/Image%20for%202019%20T32%20lectures_4x4_0.jpg?itok=HGGnI3Cf)
The Department of Radiology and Biomedical Imaging has a long record of excellence in clinical and academic radiology, and has one of the largest research enterprises funded through intra- and extramural funding and private donors. With numerous outstanding basic scientists and clinicians engaged in innovative imaging research across five principal campus units, the department provides a fertile ground for interdisciplinary collaboration. The T32 program exists to jumpstart the academic careers of junior radiologists and nuclear medicine physicians and to provide the essential foundation for developing a research program as an independent investigator.
![](https://radiology.ucsf.edu/sites/radiology.ucsf.edu/files/styles/150w/public/fields/field_image/speakers/Jae%20Ho.Sohn2_.jpeg.png?itok=3Jxlwdvk)
'Big Data in Radiology: Natural Language Processing & Chest Imaging Application'
Dr. Jae Ho Sohn is a resident in the UCSF Department of Radiology and Biomedical Imaging. As a 2019-2020 NIH T-32 scholar, Dr. Sohn's research investigations over the past year was on big data in radiology, particularly focusing on natural language processing and computer vision work in cardiothoracic imaging. Under the faculty guidance of Drs. Thienkhai Vu, MD, PhD and Youngho Seo, PhD and Bonnie Joe, MD, PhD, Dr. Sohn also ran a trainee-led medical machine learning team called Big Data in Radiology (BDRAD) and mentored 13 students this academic year.
![](https://radiology.ucsf.edu/sites/radiology.ucsf.edu/files/styles/150w/public/fields/field_image/speakers/Kirti%20Magudia.jpg?itok=mscKqgf3)
'Improving the Screening and Detection of Prostate Cancer with Deep Learning'
Kirti Magudia, MD, Ph.D. is finishing her radiology training at the University of California, San Francisco as a T32 research fellow in the Biomedical Imaging for Clinical Scientists Program followed by a clinical fellowship in abdominal imaging and ultrasound. Her research centers on high-level applications of machine learning in radiology, including CT-based body composition analysis and prostate MR, which was facilitated by 7 dedicated months at the MGH/BWH Center for Clinical Data Science. Dr. Magudia was the founding resident chair of the Brigham and Women’s Hospital Women in the Radiology Program and has extensively advocated for family-friendly trainee policies. She is a graduate of the Tri-institutional MD/PhD program of Weill Cornell, Sloan-Kettering, and Rockefeller University, where she completed her Ph.D. in cell and cancer biology in the laboratory of Alan Hall developing a novel 3D cell culture model of colon tumorigenesis.