ICoN Center Researchers

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

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.

Fabio Catania

Postdoctoral Fellow – Ghosh, Gabrieli, & Seethapathi Labs

Fabio Catania focuses on understanding the behaviors and communication patterns of autistic people through the lens of human-robot interaction.

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.

Abhiram Iyer

Postdoctoral Fellow – DiCarlo, Fiete, & Harnett Lab

Abhiram Iyer works on computational models of neocortical microcircuits and dendrites to construct more biologically plausible learning rules as an alternative to traditional deep learning.

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.

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.

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.