Bilkent University
Mechanical Engineering Department
GÖKBERK KABACAOÐLU
Assistant Professor
Address
Department of Mechanical Engineering
Bilkent University
06800 Bilkent, Ankara
Office
EA109
Phone
+90 (312) 290-1286
BIOGRAPHY
I joined BilMech as an assistant professor in January, 2022. From 2019 to 2021, I was a postdoctoral researcher at the Flatiron Institute, an internal research division of the Simons Foundation in New York. There, I was a member of the Centers for Computational Biology and Computational Mathematics led by Mike Shelley and Leslie Greengard, respectively. I received my bachelor’s degree in Mechanical Engineering from Bilkent University (2014) and my doctoral degree in Mechanical Engineering from the University of Texas at Austin (2019) under the supervision of George Biros.
EDUCATION
Ph.D., Mechanical Engineering, University of Texas at Austin (2019)
B.S., Mechanical Engineering, Bilkent University (2014)
RESEARCH
The key areas of my research are:
• computational biological fluid dynamics (e.g., viscous flows of deformable particles),
• mathematical modeling,
• fast numerical methods for fluid-structure interactions,
• optimization,
• scientific machine learning.
Real-world complex phenomena are typically characterized by interacting physical processes, uncertain parameters, dynamic boundaries, and intimate coupling over a wide span of spatial and temporal scales. Predictive computational models of such phenomena inherit these characteristics and ought to be multi-scale and multi-physics, quantify the uncertainties in their predictions, and provide a framework for data synthesis. My primary research aim is to understand and predict the behavior of multi-scale complex systems in biology. This objective establishes an interdisciplinary research program in computational biophysics, which sits at the intersection of engineering, biology, computer science and applied mathematics. In particular, I develop numerical methods and computational tools to overcome the sheer scale of problems and to seamlessly integrate data into in silico experiments.
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