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 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 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.
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.
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.
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.
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.
Postdoctoral Fellow – Fiete and Wang Lab
Leo Kozachkov is studying the brain from the perspective of dynamical systems theory and machine learning.
Postdoctoral Fellow – Yang and Graybiel Lab
Michelangelo Naim uses machine learning tools to model memory, behavior and learning principles of the brain.
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.
Graduate Fellow – Jazayeri Lab
Mahdi Ramadan’s research focuses on the cognitive and neural modeling of flexible hierarchical decision making.
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.
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.
Postdoctoral Fellow – Saxe & Fedorenko labs
Chengxu Zhuang is building ecologically plausible AI models and using these models to better understand brain functions.