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Faculty

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Jeffrey Tithof

  • Assistant Professor (Research), Department of Mechanical Engineering

PhD, Georgia Institute of Technology, 2016

214  Hopeman
j.tithof@rochester.edu

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Professor Jeff Tithof was appointed Assistant Professor (Research) in the Department of Mechanical Engineering at University of Rochester in July 2019. Prior to this appointment, Professor Tithof was a postdoctoral associate for three years working in the lab of Professor Doug Kelley. He received a PhD in Physics from Georgia Institute of Technology in June 2016 and a B.S. in Physics and Mathematics from University of Tennessee in 2010. He was a recipient of a Burroughs Wellcome Fund Career Award at the Scientific Interface in 2019.

 

Research Overview

Professor Tithof’s research focuses on developing numerical and experimental models of cerebrospinal fluid (CSF) flow through perivascular spaces in the brain. Such CSF flow is part of the glymphatic system, discovered at University of Rochester in 2012, and compelling evidence suggests that disruption of this system may directly contribute to development of neurodegenerative diseases, such as Alzheimer’s. Professor Tithof’s research aims to help elucidate the fundamental fluid mechanics of CSF transport through the brain, which may in turn lead to novel treatment and prevention strategies for neurodegenerative diseases, as well as new methods for drug delivery to the brain. Dr. Tithof currently collaborates closely with Professors Kelley, Thomas, and Shang in Mechanical Engineering, as well as Professor Nedergaard in the Center for Translational Neuromedicine located in the University of Rochester Medical Center.

 

Dr. Tithof is also interested in exploring novel approaches to characterize and forecast turbulence using techniques from nonlinear dynamics. In his doctoral work, he demonstrated the feasibility of forecasting a weakly turbulent flow using “exact coherent structures”, which are special unstable solutions of the nonlinear governing equation. In future work, he is interested in leveraging exact coherent structures and/or machine learning to improve predictability of aperiodic fluid flows.