ICoN Center Researchers

Center Director

Ila Fiete collaborates with experimentalists to create computational models that inform how cognitive computations give rise to evolutionarily conserved neural biophysics and circuitry.
Center Faculty

James DiCarlo studies how biological mechanisms are converted into learning networks that underlie rapid image recognition.
Collaborating Labs: Fiete and Harnett

Ev Fedorenko uses artificial intelligence to better understand human language acquisition and cognition.
Collaborating Labs: Fiete and Levy

Michale Fee studies how memories in auditory circuits are transformed into stereotyped motor memories in songbirds.
Collaborating Lab: Fiete

Guoping Feng creates comparative models of autism-relevant behaviors in marmosets and humans.
Collaborating Lab: Seethapathi

Michael Frank
Michael Frank uses large datasets to create models that inform our understanding of language acquisition and human cognition.
Collaborating Labs: Saxe and Tenenbaum

John Gabrieli creates predictive models from longitudinal behavioral data related to psychiatric symptoms.
Collaborating Lab: Ghosh

Satrajit Ghosh uses large experimental data sets to create analytic platforms that predict human health outcomes.

Ann Graybiel studies the computational mechanisms of cost-benefit decision making.
Collaborating Lab: Yang

Mark Harnett studies how the biophysics of synaptic activity controls neural networks and computations that underlie complex behavior.
Collaborating Labs: Fiete and DiCarlo

Mehrdad Jazayeri models complex causal inference based on data from animal models.
Collaborating Labs: Yang and Halassa

Roger Levy develops algorithms and models of language processing based on psycholinguistic data sets.
Collaborating Lab: Fedorenko

Josh McDermott studies how the features of different sounds are related to efficient memory encoding in humans.
Collaborating Lab: Fiete

Rebecca Saxe models attention and cognition in human infants.
Collaborating Labs: Tenenbaum and Frank

Nidhi Seethapathi collaborates with experimentalists to build predictive models of how humans learn, execute and select complex movements.

Josh Tenenbaum
Josh Tenenbaum acquires and uses behavioral experimental data in children and adults to create computational models of human inference.
Collaborating Labs: Saxe and Frank

Fan Wang models homeostatic mechanisms that regulate pain, addiction and sleep.
Collaborating Lab: Fiete

Robert Yang collaborates with experimentalists to integrate multi-level data sets for the creation of multi-system models of cognition.
ICoN Fellows

Vin Agarwal
Graduate Student – McDermott Lab
Vin Agarwal’s research aims at understanding and computationally modelling how humans infer physical causes from the sounds they hear.

Antoine De Comite
Postdoctoral Fellow – Feng & Seethapathi Labs
Antoine De Comite aims at developing comparative models of naturalistic motor control across species and populations.

Lakshmi Govindarajan
Postdoctoral Fellow – McDermott and Fiete Lab
Lakshmi Govindarajan’s research draws from modern statistical tools to provide computational accounts of the interface between perceptual and cognitive processes in biological systems.

Carina Kauf
Graduate Fellow – Federenko Lab
Carina Kauf’s research investigates different aspects of linguistic meaning representations, including which features of a linguistic stimulus receive representation in the biological brain and how representations learned by artificial neural networks compare to those produced by the human brain.

Maedbh King
Postdoctoral Fellow – Ghosh and Gabrieli Lab
Maedbh King focuses on building computational models that integrate biological and behavioral information to develop risk predictors of neuropsychiatric disorders.

Graduate Fellow – Fiete Lab
Mikail Khona studies developmental processes and how they shape neural circuits and representations in the hope of building more robust, embodied agents.

Leo Kozachkov
Postdoctoral Fellow – Fiete and Wang Lab
Leo Kozachkov is studying the brain from the perspective of dynamical systems theory and machine learning.

Michelangelo Naim
Postdoctoral Fellow – Yang and Graybiel Lab
Michelangelo Naim uses machine learning tools to model memory, behavior and learning principles of the brain.

Aran Nayebi
Postdoctoral Fellow – Jazayeri, Halassa, and Yang Labs
Aran Nayebi’s interests lie at the intersection of systems neuroscience and artificial intelligence, where he uses tools from deep learning and large-scale data analysis to reverse engineer neural circuits.

Mahdi Ramadan
Graduate Fellow – Jazayeri Lab
Mahdi Ramadan’s research focuses on the cognitive and neural modeling of flexible hierarchical decision making.

Gal Raz
Graduate Student – Saxe, Frank, and Tenenbaum Labs
Gal Raz is interested in how humans and infants choose to focus their visual attention and how to make computational models to explain this.

Noga Zaslavsky
Postdoctoral Fellow – Levy, Fedorenko, and Yang Labs
Noga Zaslavsky’s research aims to understand language, learning, and reasoning from first principles, building on ideas and methods from machine learning and information theory.

Chengxu Zhuang
Postdoctoral Fellow – Saxe & Fedorenko labs
Chengxu Zhuang is building ecologically plausible AI models and using these models to better understand brain functions.