About me. I’m currently a sixth year Ph.D. student in Electrical Engineering at Stanford University. I work at the intersection of medicine and machine learning, and I’m excited about the potential of new technology to improve the quality and accessibility of healthcare. Most of my graduate research has focused on developing and evaluating deep learning methods for medical image analysis. Please click here for more info on my research and here for my CV.
Brief graduate bio. At Stanford, I’m lucky to be advised by Drs. Christopher Ré and Curtis Langlotz, affiliated with the Stanford AI Lab and the Center for Artificial Intelligence in Medicine and Imaging. I’m also grateful to be supported by a Hertz Fellowship, an NSF Graduate Research Fellowship, and a Stanford Graduate Fellowship in Science and Engineering as the Texas Instruments Fellow. During my graduate career, I interned with the Bill and Melinda Gates Foundation and have enjoyed ongoing collaboration with the NIH, specifically the NHLBI’s Imaging AI Program.
Brief undergraduate bio. I graduated summa cum laude from Rice University with a B.S. in Electrical Engineering and a minor in Global Health Technologies in 2017. At Rice, I worked with Dr. Behnaam Aazhang in the Rice Neuroengineering Initiative and with Drs. Rebecca Richards-Kortum and Maria Oden in the Rice360 Institute for Global Health Technologies. During undergrad, I spent time at the Polytechnic University of Malawi through an internship with Rice360 learning about healthcare technology and accessibility.