Greetings! I am currently a postdoctoral research scientist in Electrical Engineering at Columbia University, focusing on deep learning applications. Previously, as a member of the Bionet group, I studied the efficient initialization and execution of very-large-scale, real connectome and synaptome driven, single-synapse-level simulations of neural circuits on GPUs for the purpose of understanding realistic neural networks. Afterwards, I worked on the design of computational circuits for understanding the mushroom body and the lateral horn, two brain regions in insects linked with associative and evolutionary memories. I am also one of the main developers of an open-source interactive neuroscientific computing platform called FlyBrainLab (FBL) that enables access to, manipulation of and simulations for fruit fly brain data.
- October 25, 2023: I will be in Boston to present our work on transfer learning for low-resource surgical phase segmentation at the ACS Clinical Congress 2023.
- September 5, 2023: In Fall 2023, I will be teaching the course "ECBM E4040: Neural Networks and Deep Learning" at Columbia University! Link: Columbia Course Directory! Course description is now available here.
- June 15, 2023: In Summer 2023, I will be managing the research projects taking place in Kostic lab at Columbia University! Our 9-student team will focus on AI research for reimagining New York as a smart city.
- January 17, 2023: In Spring 2023, I will be teaching the course "EECS E6691: Advanced Deep Learning" at Columbia University! Link: Columbia Course Directory! Course description is now available here.
- November 9, 2022: My poster, "Design and Simulation of Spiking Programmable Neural Computers" has been accepted to SNUFA 2022! The poster is now available here.
- January 28, 2022: Our abstract "A circuit library for exploring the functional logic of massive feedback loops in Drosophila brain" has been accepted to Cosyne 2022!
- January 11, 2022: We have updated the preprint of our newest paper "A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain"! This latest version is now available at bioRxiv.
- December 30, 2021: The preprint for our newest paper, "A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain", is now available at bioRxiv.
- December 1-2, 2021: I will be presenting our poster, "Visualization and Graph Exploration of the Fruit Fly Brain Datasets with NeuroNLP++", in Neuromatch 4.0.
- November 10, 2021: I am presenting two posters in this year's Society for Neuroscience (SFN) conference: "Interrogating the functional logic of Drosophila brain circuits at single-synapse scale" and "Untangling the Graph Structure of Drosophila Brain Datasets with Open Source FlyBrainLab Utility Libraries".
- November 8, 2021: A poster I will be presenting, "Visualization and Graph Exploration of the Fruit Fly Brain Datasets with NeuroNLP++", was accepted to Neuromatch 4.0!
- October 5-8, 2021: I will present our poster, "NeuroNLP Gene Match: An open source genetic data visualizer and explorer", in the CSHL Neurobiology of Drosophila conference!
- March 1, 2021: My team, Zeroknowledge47, has won a $1000 prize in the Terminal East Coast Regional 2021 AI competition, entering the Top 12!
- February 22, 2021: Our paper, "Accelerating with FlyBrainLab the discovery of the functional logic of the Drosophila brain in the connectomic and synaptomic era", is now published in eLife!
- January 5, 2021: My talk for PyData 2020 (November 15), "FlyBrainLab: An Interactive Open Computing Platform for Exploring the Drosophila Brain", is now online for everyone!
Some of my recent talks and conference posters are available for public view:
Recent Past and Interests
Previously, I have obtained my master's degree in Computer Science at Columbia University and my bachelor's degree in Electronics and Communication Engineering at Istanbul Technical University. In addition to my current focus, I am interested in computational neuroscience, machine learning and signal processing in general. I enjoy painting, video game and video game asset development, cooking and graphic design.
In the last few years, I have worked on the applications of machine learning to a variety of problems. I have designed and implemented methods to utilize thermal images to augment vulnerability prediction models for ConEd New York infrastructure. I have worked on a project to build a successor to Google's discontinued Google Flu Trends service. I have prototyped methods for automatic agricultural data extraction from images. I have taken part in research projects seeking to build novel approaches for cross-modal face recognition. You can find my CV here.