Mehmet (Matt) Kerem Turkcan

Postdoctoral Research Scientist and Lecturer at Columbia University | mkt2126 (at)


Greetings! I am currently a postdoctoral research scientist in Electrical Engineering at Columbia University. 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 using ablational studies and more. My current focus is 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.

Due to the availability of data in single cell scale and below as well as the similarity of these circuits to those in humans, insect brains are good models for understanding the principles of biological learning. It is my belief that such an understanding can help us design better few-shot learning algorithms, especially for natural sensory data and transfer learning applications.


  • 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 at 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 at 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.