I am a postdoctoral researcher at Max Planck Institute for Intelligent Systems, Haptic Intelligence Department directed by Dr. Katherine J. Kuchenbecker. I received my Ph.D. degree in Mechanical Engineering from Koc University (Istanbul, Turkey) in 2018, under the supervision of Dr. Ipek Basdogan. I received my M.Sc. degree in Computational Mechanics from Technical University of Munich in 2013, where I completed my thesis at BMW Group under the supervision of Prof. Dr. Fabian Duddeck. I earned my B.Sc. degree in Mechanical Engineering from Middle East Technical University (Ankara, Turkey) in 2011.
My research focuses on computational modeling of contact mechanics between the finger and surfaces providing haptic feedback via electrovibration and/or mechanical vibrations. I aim to develop simulation tools that will enable deeper understanding of contact phenomenon in surface haptics applications and circumventing a portion of costly experimental procedures. Such tools can be used for optimal design of intelligent systems delivering various realistic haptic experiences on today’s electronic devices such as smartphones or tablet computers.
Work-in-progress paper (2 pages) presented at the IEEE World Haptics Conference (WHC), Tokyo, Japan, July 2019 (misc)
In this study, we develop a high-fidelity finite element (FE) analysis framework that enables multiphysics simulation of the human finger in contact with a surface that is providing tactile feedback. We aim to elucidate a variety of physical interactions that can occur at finger-surface interfaces, including contact, friction, vibration, and electrovibration. We also develop novel FE-based methods that will allow prediction of nonconventional features such as real finger-surface contact area and finger stickiness. We envision using the developed computational tools for efficient design and optimization of haptic devices by replacing expensive and lengthy experimental procedures with high-fidelity simulation.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems