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URMC Response to CrowdStrike Software Issue

Saturday, July 20: All UR Medicine facilities are open as scheduled and providing safe patient care, with a goal to return all clinical services to full efficiency by early next week.

Patients: click here for more information. Faculty/Staff: click here for information.

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Zhengwu Zhang, Ph.D.

Contact

Call Center (585) 276-3000

About Me

I am an assistant professor in the Department of Biostatistics and Computational Biology at the University of Rochester. Before joining Rochester, I was a Postdoctoral Fellow at Duke University and Statistical and Applied Mathematical Science Institute (SAMSI). I was affiliated with SAMSI's Challen...
I am an assistant professor in the Department of Biostatistics and Computational Biology at the University of Rochester. Before joining Rochester, I was a Postdoctoral Fellow at Duke University and Statistical and Applied Mathematical Science Institute (SAMSI). I was affiliated with SAMSI's Challenges of Computational Neuroscience (CCNS) program in 2015-2016, where I got the change to work with Prof. David Dunson (Duke) and Prof. Hongtu Zhu (MD Anderson Cancer Center) on various problems in the computational neuroscience, including construction of structural connectivity, brain network analysis, image and shape analysis.

I earned my Ph.D. in Statistics from the Florida State University (FSU), under the supervision of Prof. Anuj Srivastava. I was working on developing statistical methods for functional and shape data, e.g. 2D contours, surfaces, signals, densities, and trajectories on non-linear manifolds.

Faculty Appointments

Adjunct Assistant Professor - Department of Biostatistics and Computational Biology (SMD)

Research

My main interests lie in developing statistical and machine learning methods for high-dimensional "objects" with low-dimensional underlying structures, e.g. images, contours, surfaces, networks and time-indexed paths on non-linear manifolds. These datatypes are abundant in various fields: neuroscien...
My main interests lie in developing statistical and machine learning methods for high-dimensional "objects" with low-dimensional underlying structures, e.g. images, contours, surfaces, networks and time-indexed paths on non-linear manifolds. These datatypes are abundant in various fields: neuroscience, epidemiology, genomics, meteorology and computer vision. Interdisciplinary skills across different fields, e.g. Statistics, Computer Science, Mathematics, are keys to solve challenges raised from efficiently analyzing such "objects".