First PhD Students Selected for Pre-Doctoral Training Program in Computational Medicine

September 21, 2017

Clockwise from top left are Julie Shade, Sieu Tran, Ran Liu and Cynthia Steinhardt.

In July 2017, The Institute for Computational Medicine announced the inaugural year of its Pre-Doctoral Training Program in Computational Medicine, funded by the National Institute of General Medical Sciences. The program supports selected first-year PhD students from the departments of Biomedical Engineering and Applied Mathematics & Statistics.

Students chosen for this Ruth L. Kirschstein National Research Service Award institutional training grant will learn through a combination of focused coursework and dissertation research alongside computational medicine training program faculty mentors from across the Johns Hopkins Whiting School of Engineering and the School of Medicine.

Ran Liu, Julie Shade, and Cynthia Steinhardt, PhD students in the department of Biomedical Engineering, and Sieu Tran, a PhD student in the department of Applied Mathematics and Statistics, are the first candidates selected to participate in the program.

Ran Liu received a Bachelor of Science in Bioengineering and Biomedical Engineering from the Johns Hopkins University in 2016. After graduating, Liu worked as a research assistant in the lab of ICM Director, Raimond Winslow, where he developed an interest in computational methods and mathematical modeling. Liu continues to work with Dr. Winslow and his current research focuses on developing models of disease based on clinical data, and applying computational methods to aid clinicians with the diagnosis and treatment of Sepsis.

Also a Hopkins alumnus, Julie Shade completed her undergraduate degree in Biomedical Engineering in 2016 where she became interested in computer programming, computational modeling, and imaging. As an undergraduate, Shade did nanomedicine and drug delivery research, enjoyed the translational aspect of it, and wanted to continue that type of research in graduate school. An interest in the use of computers and simulations to advance patient care attracted her to Computational Medicine and led her to the ICM lab of Natalia Trayanova where she researches the use of patient-specific computer models of electrical activity in the heart to improve treatment of ventricular tachycardia.

Cynthia Steinhardt received a Bachelor of Arts in Neuroscience and a certificate in Cognitive Science from Princeton University in 2016. Steinhardt has always been fascinated by the questions of neurological science, but few widely-accepted quantitative and theoretical models of biological systems currently exist. She was drawn to computational medicine because it builds on the quantitative and computational foundations of a variety of fields to attempt to answer some of these questions in ways that can help us understand fundamental principles of the human body and improve techniques for diagnosis and treatment of disease. Steinhardt’s research in the lab of ICM Associate Director, Sridevi Sarma, focuses on understanding how current injection, a common technique in research and brain-computer interface-based treatment of disease, affects brain function at the local and network level. By exploring these concepts, Steinhardt hopes to further prosthetic and neural feedback based treatments and continue to use this network-based theory to characterize and treat neurological diseases.

Sieu Tran, who completed a BS in both Mathematics and Microbiology at Virginia Polytechnic Institute and State University, has always been interested in the intersection of math and biological sciences. In 2014, Professor Nanda Nanthakumar, a well-respected mucosal immunologist at Virginia Tech, introduced Tran to microbiology. As he delved into the microbiological world, Tran noticed similar patterns that resembled fundamental concepts of mathematics. Inspired by curiosity, Tran wishes to further concrete knowledge in pure mathematics and relevant discussion and practice of experimental biology. He is interested in building computational models to guide experimental programs that develop methods for measuring and computing the closeness of the geometric representation of normal and abnormal anatomical and biological structures.

Upon completion of the program, participants will receive PhD degrees from their home department as well as research experience and training designed to prepare graduates to fill the growing need for researchers trained in computational medicine in both industry and academia.

JHU - Institute for Computational Medicine