Human cognition is remarkably flexible, enabling us to perform a wide variety of tasks, often with little to no prior experience. This is essential to our survival, specially in a world that never repeats itself; every situation we face is unique in some way. The throughline of my research program is to study cognitive flexibility by examining how symmetries of the external world are reflected in the architecture and function of our brains. Such symmetries give us vital inductive biases to learn faster and more efficiently, and flexibly adapt to the changes in the stimuli and contexts. Inspired by the neurophysiological data collected at our lab, we have been building models of flexible cognitive control to study these phenomena.
Prior to Princeton, I was at Zuckerman Mind Brain Behavior Institute, where I studied how neural networks represent the concepts they have learned. Specifically at the Qian Lab, we were studying such representations and their various topological and geometrical properties in recurrent neural networks of firing rate cells, specifically in the context of visual perception and its relationship with (working) memory.
During my PhD, I worked on large-scale human-in-the-loop data analytics (HILDA). My research interests included a wide range of data management and analysis topics, from building frameworks and tools for large-scale HILDA to applications of data management techniques on Big Data. I worked as a part of AnHai Doan's group. We built frameworks for the next generation of entity matching, data integration and data cleaning tools.
Here is my CV (as of Jun 2025).
Ph.D. in Computer Sciences, University of Wisconsin-Madison (2018).
M.Sc. in Computer Sciences, University of Wisconsin-Madison (2015).
M.Sc. in Information Technology Engineering, University of Tehran, Iran (2008).
B.Sc. in Software Engineering, University of Tehran, Iran (2005).
My NeuroTree
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