Time-series Analysis of Physiological Measurement Data
Jan Christoph, Professor
UC San Francisco
Applications for Spring 2026 are closed for this project.
Our group studies heart rhythm disorders and cardiac pathophysiology. In our lab, we produce high-resolution imaging data of intact isolated hearts ex vivo. This data shows action potential waves propagating across the ventricular surface of isolated beating hearts. A common pathophysiology, which we simulate in this ex vivo setting, is the so-called "Long QT syndrome", which refers to an abnormal prolongation of the action potential during the heartbeat. The action potential prolongation affects the mechanical pumping of the heart and is associated with an increased arrhythmia risk in patients.
Role: The goal of this project is to develop algorithms and analyze measurement data of action potentials in beating hearts. The work will be based on data that we generate with an imaging system that is described in our preprint: https://arxiv.org/abs/2307.07943
In this project, the student will improve existing analysis scripts and analyze data of the Long QT syndrome. It will be required to conduct literature research on the Long QT syndrome, perform systematic time-series analysis, improve our algorithms, compare the literature values with our data, and present the findings in our lab meeting.
Qualifications: Applicants should have a solid foundation in coding and an interest in physiology, medicine, cardiology, mathematical biology, and data and time-series analysis. Experience with Python programming is absolutely required. At least 1 day per week is needed to make significant progress, and students who can devote 8 hours will be given preference.
Day-to-day supervisor for this project: Jan Christoph, Staff Researcher
Hours: 6-8 hrs
Off-Campus Research Site: The work can be performed remotely or, if desired, at our institute, the Cardiovascular Research Institute on UCSF's Mission Bay Campus. We will have regular 1-on-1 meetings every 1-2 weeks to discuss the progress, either on Zoom or in person, if desired.
Related website: http://cardiacvision.ucsf.edu
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