Monday, May 18, 2026, 12:30 – 2:00 pm, Talk Room 2
The Vision Sciences Society is honored to present Ilker Yildirim with the 2026 Elsevier/VSS Young Investigator Award.
The Elsevier/VSS Young Investigator Award, sponsored by Vision Research, is given to an early-career vision scientist who has made outstanding contributions to the field. The nature of this work can be fundamental, clinical, or applied. The award selection committee gives highest weight to the significance, originality and potential long-range impact of the work. The selection committee may also take into account the nominee’s previous participation in VSS conferences or activities, and substantial obstacles that the nominee may have overcome in their careers. The awardee is asked to give a brief presentation of their work and is required to write an article to be published in Vision Research.
Ilker Yildirim
Assistant Professor, Department of Psychology, Yale University
The 2026 Elsevier/VSS Young Investigator Award goes to Professor Ilker Yildirim for his important contributions to the scientific study of visual perception and attention. Dr. Yildirim is an Assistant Professor in the Department of Psychology at Yale University. After completing his undergraduate and master’s degrees in Computer Science at Boğaziçi University in Turkey, Dr. Yildirim earned his PhD with Robert Jacobs in the Departments of Brain and Cognitive Sciences and Computer Science at the University of Rochester. He then conducted postdoctoral research with Josh Tenenbaum at MIT and Winrich Freiwald at The Rockefeller University.
Dr. Yildirim builds computational models of visual processing across multiple levels of analysis that he then tests in both rigorous psychophysical experiments and human and non-human primate neural data. Dr. Yildirim’s research stands out not only because of this careful integration of experimental and modeling approaches, but also because of the diverse computational approaches employed, including probabilistic programming, causal generative models, dynamical systems, and deep neural networks. Examples of recent work include the use of an inverse graphics model to explain the visual processing of faces and bodies, a novel adaptive computational account of attention, and intuitive physics models of the perception of liquids and soft objects such as cloth. Dr. Yildirim has received awards and funding from the NIH, NSF, and the Air Force Office of Scientific Research (AFOSR), and is renowned at Yale for his integrative Algorithms of the Mind course. Dr. Yildirim’s innovative and technically demanding multilevel research expands vision science into new and exciting directions.
How to model the mind simultaneously across the computational, algorithmic, and neural levels
In the history of neuroscience and psychology, we can, for the first time, discover complex algorithms of intelligence in concrete, computational terms. I will present my lab’s work, which advances these efforts by focusing on visual cognition, uncovering the computational logic and intermediate representations that transform images into rich representations of objects and scenes that we can think about and plan with. The overall theoretical core of this work is that visual cognition is fundamentally about building and manipulating ‘structure-preserving representations’ (SPRs) of the physical world, going beyond the task-optimized statistical representations featured in standard deep neural networks. I’ll present multilevel formulations of this core theory of SPRs, making contact with empirical measurements across levels of analysis, from dense psychophysics to single-cell electrophysiology. I’ll highlight our work in the domains of object perception, intuitive physics, and goal-driven attention. These studies blur the boundaries between between currently divergent modeling approaches of cognitive science (probabilistic/connectionist/dynamical systems), provide accounts of neural mechanisms that are simultaneously more interpretable and predictive than alternatives, and offer a way to synthesize task optimization and structure preservation.