Hi, it’s nice to meet you! I’m Feng, a sixth-year MD-PhD student in Biostatistics. I completed my PhD in the PennSIVE group at the Perelman School of Medicine with Dr. Russell (Taki) Shinohara. My research interests include developing novel statistical and machine-learning-based methodology for neuroimaging and other big data settings. In neuroimaging, I’ve developed tools for multi-modal image analysis, unsupervised MRI segmentation problems, and image harmonization as well as authored a comprehensive review of current image harmonization methods. Statistically, I’ve proposed a privacy-preserving algorithm for distributed fitting of population-based reference charts. As I continue to grow as a biostatistician, I’m particularly interested in leveraging powerful and flexible methods, such as deep learning, in statistically-informed ways and building new methods alongside the scientists who will use them.

Outside of my methodological work, I’m interested in collaborating with biomedical researchers and clinicians to enable them to get the most out of their data in a statistically-sound way, as well as how to interpret studies and clinical trials from a statistical lens. To do so, I’ve served as a statistical collaborator for a number of biomedical, clinical, and health informatics projects. To formalize the process, organize real-life consulting experience for fellow Biostatistics students, and provide an accessible statistical service to fellow biomedical trainees, I founded the Biostatistical Student Analysis Core (BSAC) within the Biostatistics graduate program. If you’re a Penn biomedical trainee of any kind and are interested in working together, please reach out to us through our BSAC Intake Application! We’d love to hear from you!

Clinically, I’m interested in diagnostic radiology. I love the mix of diagnosis, medical thinking, and opportunity for procedure within this specialty. In this context, I look forward to the opportunity for developing new methods in the context of spatially-dependent imaging data, supported by clinical knowledge and a dedication for advancing patient care. Over the last few years of medical school and beyond, I’m excited to learn more!

View my CV here or at the “My CV” tab above!