Members


Principal Investigator

Matt Nassar

Matt is interested in how the brain processes information to facilitate intelligent behavior in complex and dynamic environments. He did his PhD with Josh Gold and post-docs with Joe Kable and Michael Frank before joining the Department of Neuroscience at Brown in January 2019.

Matt is currently teaching NEUR1660 Neural Computation in Learning and Decision-Making

Email: matthew_nassar@brown.edu


Post-Doctoral Researcher

Linda Yu

Linda studies how the brain creates, stores, and retrieves latent state representations to improve the efficiency of learning in the face of changing contexts. She did her PhD in neuroeconomics at the University of Pennsylvania with Joe Kable. She is a true believer when it comes to TIRG.

Email: linda_yu@brown.edu


Lab Manager

Tiantian Li

Tiantian is a recent graduate of Boston University where she studied Neuroscience and Economics. Her undergraduate research in the Cognition and Decision Lab attempted to build computational models of learning to human persistence behaviors. Or, learning to not TIRG?

Email: tiantian_li1@brown.edu


Graduate Students

Harrison Ritz (honorary lab member)

Harrison is a graduate student in CLPS with Amitai Shenhav and Michael Frank working on control theoretic models of behavioral regulation.

Ryan Thorpe (rotational NSGP student)

Ryan calls the Northwestern US home and he attended college at Walla Walla University in southern Washington state. Recently completing a master’s in biomedical engineering from Brown, Ryan’s research interests are twofold: 1) developing multi-scale models that make meaningful predictions about underlying biophysical states that mediate somatosensory
processing, and 2) modeling the cognitive components of evidence accumulation that provide a
basis for how novel incoming information is processed. As a Neuroscience PhD student, his
current project involves modeling the effect of working memory load on variable learning rate
in a dynamic change-point task. If his ultimate career goal wasn’t to become a professor, Ryan
would strive to be a stay-at-home plant dad and climbing enthusiast.


Undergraduate Research Assistants

Erin Bugbee

Erin is a senior at Brown concentrating in Statistics and Behavioral Decision Sciences, with a specialization in Human and Machine Learning. She is interested in how ideas from artificial intelligence and statistics can be used to better understand the mind, as well as how we can build machines that learn in human-like ways.

Jake Szykowny

Jake is a junior from Avon, CT concentrating in Neuroscience. He is interested in the diseases of the brain, especially those concerning age-related cognitive decline. Enjoys teaching Neuroscience classes (Brown Brain Bee), participating in club hockey and lacrosse, and producing his own music.

Katie Unger

Katie is a first-year undergraduate student at Brown studying neuroscience. She is examining how grid cell firing is correlated to learning in changing circumstances.  

Kaitlyn Mi

Kaitlyn is a sophomore studying Cognitive Neuroscience. She is interested in higher-order cognitive functions and applications of behavioral-cognitive modeling to clinical settings. In her spare time, she listens to NCT and pets dogs on the main green.

Martin Contreras Carrera

Martin is a junior at Brown and is examining how learning and perceptual regularization are regulated according to the recent statistical context of the environment.

Sophie Kush

Sophie is a junior from Upstate, NY studying Neuroscience at Brown. She is interested in investigating how GABA affects many learning and decision making processes and finding the best way to analyze GABA levels in the brain. She enjoys playing Lacrosse, Ice Hockey, and Ultimate Frisbee in her spare time. 


Lab Alumni

Aansh Shah

Aansh is a senior at Brown and is using machine learning approaches to understand changes in brain dynamics induced by pharmacological manipulations.

Charlene Wang

Charlene is a junior at Brown and is developing computationally efficient methods for inference in dynamic and repeating contexts.