Ilker Yildirim, Ph.D.

How does perception transform raw sensory signals arising from our physical environments, into things like objects and people, into things that we can think about? This is the key question that drives the research in the lab. We approach this goal primarily with computational modeling that brings together a diverse range of approaches including probabilistic models, simulation engines (including graphics and physics engines), and efficient approximate Bayesian inference (using deep neural networks, sequential importance samplers, approximate Bayesian computation, and their hybrids). We test these models empirically in behavioral and neural experiments to build a unified account of neural function, cognitive processes, and behavior, in precise engineering terms.