About

I’m currently a researcher at the Flatiron Institute, co-supervised by Alex Williams and Cristina Savin, working on metrics for comparing dynamic representations. I have an MSc degree in Computational Neuroscience from the University of Tübingen, where I was funded by the German Academic Exchange Service (DAAD), and I did my bachelor’s in Electronics & Communication Engineering at Istanbul Technical University.

My research focus is learning, computation through dynamics, and latent variable models. In general, I approach neuroscience questions using modern machine learning tools and dynamical systems theory.

I completed my master’s thesis in Savin Lab at NYU’s Center for Neural Science, where I combined control and learning theory to develop a novel algorithm for recurrent neural networks (currently in preparation). Before that, I worked in Macke Lab, on inferring dynamical systems underlying data. My first lab rotation was in Dayan Lab, where I explored three-factor Hebbian learning. During my first year of the master’s program, I also worked on learning rules in Levina Lab. For my bachelor’s thesis, I worked with Neslihan Serap Şengör, on spiking neural networks for perceptual decision-making. I am deeply grateful for her mentorship since my undergraduate years, as she not only guided my research but also helped me appreciate the beauty of math and neuroscience.