Ilker Yildirim

I am a research scientist at MIT. I mainly work with Josh Tenenbaum (MIT) and Winrich Freiwald (The Rockefeller University). Before, I did my Ph.D. studies at University of Rochester advised by Robbie Jacobs.
E: ilkery at
P: 585 2670718

You may find my CV here.


My research takes a computational approach to perception and its neural basis. My scientific goal is to understand how individual sensory modalities (e.g., vision) and the interactions between more than one sensory modality (e.g., interactions between vision and touch) give rise to our seamless and richly structured everyday perceptual experiences. To this end, I utilize probabilistic programming and deep learning alongside psychophysics and computational neuroscience.

Recent and upcoming talks
  • New: I am organizing a workshop titled "Deep Learning in Computational Cognitive Science" at the upcoming CogSci conference in July in London, UK.
  • New: Invited talk at the Hierarchical Multisensory Integration Workshop in June in Barcelona, Spain.
  • Action and Perception Seminar Series, Brown University, December, 2016.
  • Center for Brains, Minds, and Machines Research Meeting, MIT, November, 2016.
  • Physical and Social Scene Understanding Workshop, CogSci'16, August, 2016.
  • Special Seminar, RIKEN Brain Science Institute, Tokyo, November 2015.
  • International Symposium on Object Vision in Humans, Monkeys, and Machines, The University of Electro-Communications, Tokyo, November 2015.
Recent Abstracts and Workshop papers

  • Efficient inverse graphics in biological face processing systems. Yildirim, I., Freiwald, W. A., Tenenbaum, J.B. (2017). Computational and systems neuroscience (Cosyne) 2017.
  • Interpreting Dynamic Scenes by a Physics Engine and Bottom-Up Visual Cues. Yildirim, I.*, Wu, J.*, Du, Y., & Tenenbaum, J.B. (2016). 1st Workshop on Action and Anticipation for Visual Learning, European Conference on Computer Vision (ECCV).

Data Set

Tutorials and Code

Below are short notes on Bayesian inference and Bayesian nonparametrics. I provided sample code alongside each note hoping that it will be helpful. Feel free to drop me a line about what you think, and if you see any bugs. See the Computational Cognition Cheat sheets for an extensive list of tutorials, including the ones below.