Michael J. Frank

Edgar L. Marston Professor of Cognitive, Linguistic and Psychological Sciences

Ph.D., Neuroscience & Psychology, University of Colorado at Boulder, 2004
M.A., Electrical Engineering, University of Colorado at Boulder, 2000
B.S., Electrical Engineering, Queen's University, 1997

Hometown: Montreal, Canada

My interests include computational modeling of neural mechanisms underlying
reinforcement learning, decision making, working memory and inhibitory control. I develop neural network and mathematical models of interactions between basal ganglia, frontal cortex, and hippocampus, and modulation of these brain areas by dopamine and other neuromodulators. I test theoretical predictions of the models using various neuropsychological, electrophysiological, pharmacological, neuroimaging and genetic techniques.

Phone: (401) 863-6872
Fax: (401) 863-2255
Office: Metcalf 335

CV
Personal Website
Google Scholar
michael_frank@brown.edu

Nadja Ging-Jehli

Ph.D., Psychology & Neuroscience, The Ohio State University, 2022
M.A., Psychology & Neuroscience, The Ohio State University, 2019
M.A., Experimental & Behavioral Economics, University of Zurich, 2017
B.A., Economics, University of Zurich, 2015
B.S., Business Administration, Zurich University of Applied Sciences, 2012

Nadja R. Ging-Jehli is a postdoctoral research fellow in computational psychiatry at Brown. She received two Master’s degrees – one from OSU in Psychology and Neuroscience, and one from University of Zurich in Switzerland, in Experimental and Behavioral Economics. She has expertise in applying computational models and machine learning algorithms to various cognitive and social-cognitive tasks of clinical and non-clinical samples and in studying human behavior in laboratory settings. The thrust of her research is to use Computational Psychiatry to find better ways to understand and to tailor treatments for psychiatric conditions such as attention-deficit/hyperactivity disorder (ADHD) and mood disorders (e.g., Depression, Bipolar). She is currently working on a unified modeling framework to characterize disorder-specific and transdiagnostic features across contexts and over time. Aside from the computational psychiatric application, Nadja is also studying the underlying neuro-cognitive mechanisms of conflict processing and value-based decision-making, in general. To find out more: Personal website.

nadja@gingjehli.com

Alex Fengler

Ph.D., Cognitive Science, Brown University, 2022 B.S., International Business
M.S., Neuroeconomics
MPhil, Statistics

Hometown: Bergheim, Germany

My research focuses on the application and development of computational methods in order to facilitate fast Bayesian inference for stochastic cognitive process models that do not possess closed-form likelihood functions. Such 'likelihood-free' inference methods aim to free the scientist from a priori consideration of statistical niceties in her exploration of plausible generative models (theories) of relevant data, while at the same time still maintaining the ability to perform Bayesian inference on so created models.

alexander_fengler@brown.edu

Rachel Rac-Lubashevsky

Ph.D., Brain and Cognitive Sciences, Ben-Gurion University of the Negev, 2018

I am interested in the controlled decision that guides working memory updating. More specifically, I am interested in the cognitive and physiological properties of this decision and how this voluntary decision to update is shaped by cognitive effort, reinforcement learning and sequential effects and what role dopamine plays in implementing this decision. I aim to combine behavioral experiments (such as working memory and cognitive control tasks) with EEG and eye-blink rate measures as well as with computational modeling to understand these questions.

rachel_ratz-lubashevsky@brown.edu

Rex Liu

Co-mentored by Thomas Serre

Ph.D., Applied Mathematics & Theoretical Physics, University of Cambridge, 2015

I am generally interested in what underlies the cognitive flexibility that is the hallmark of human and animal intelligence and why it is still lacking in artificial intelligence. More specifically, I am focusing on the question of how humans and animals learn useful, structured representations both of the world around them and of their behavioural policies. I am also interested in how we are able to build such representations by distilling out the essential features of the world around us from extremely high dimensional sensory information. And when we encounter new environments or tasks, I am interested in how we can use these representations to draw on knowledge that we already have to solve new problems or accomplish new goals rather than learning everything entirely from scratch. I am approaching these topics using a combination of Bayesian, deep neural network, and other statistical learning approaches.

rex_liu@brown.edu

Guillaume Pagnier

Co-Mentored by Wael Asaad

Doctoral Candidate, Neuroscience

M.S., UMass Amherst, 2015
B.S., UMass Amherst, 2013

I am predominantly interested in the neural circuits of decision making and reward anticipation. To answer this (very broad) question, I am currently investigating how subthalamic nucleus (STN) deep brain stimulation (DBS) affects cost/benefit decision making in Parkinson's disease patients.

guillaume_pagnier@brown.edu

Alana Jaskir

Doctoral Candidate

B.A. Computer Science, Princeton, 2017
Certificate in Cognitive Science, Princeton, 2017

I am interested in computational models of human learning and decision-making. More specifically, I want to investigate how the brain learns structure and creates useful representations in order to generalize in new contexts. I hope to explore multiple levels of analysis, from biologically plausible neural networks to more abstract Bayesian and reinforcement learning models.

alana_jaskir@brown.edu

Krishn Bera

Doctoral Candidate

M.S., Computational Humanities, IIIT-Hyderabad, 2021
B.Tech., Computer Science, IIIT-Hyderabad, 2019

My general interests lie in investigating the computational mechanisms underlying decision-making and learning in the human brain.

Krishn Bera

krishn_bera@brown.edu

Aneri Soni

Co-mentored by Thomas Serre

Doctoral Candidate, Neuroscience

B.A., Psychology, Cornell University, 2018

I am excited to be working in both Dr. Frank's and Dr. Serre's lab, where I use a computational approach to answer questions in neuroscience. I hope to combine concepts such as working memory, learning, action selection, with tools such as neural networks, statistics, and dynamical systems.

aneri_soni@brown.edu

Joonhwa Kim

Doctoral Candidate, Neuroscience

B.S., Cognitive Science, Computing Specialization, UCLA, 2021

B.A., Linguistics & Philosophy, UCLA, 202

I am broadly interested in using computational cognitive neuroscience methods to study and characterize how people flexibly learn and apply knowledge of the world, particularly in uncertain and/or novel environments.

joonhwa_kim@brown.edu

An Vo

B.S. Mathematics, Brown University, 2021

I'm currently involved in expanding Frank Lab's hddm toolbox by developing pipelines that compare the performance of different inference methods. I'm interested in modelling and mechanisms of the olfactory system.

As the Lab Manager, I help coordinate the various lab projects, events, and meetings at LNCC. Please feel free to reach out to me for anything remotely related to the lab! Outside of the lab, I play, teach, and help with designing and developing board/card games. Sometimes I powerlift.

an_vo@brown.edu

Joshua Hewson

Mathematics, Williams College, 2022

I am interested in understanding intelligence on an abstract level, by exploring how the human brain - individually and in groups - achieves this. I would be keen to use both realistic and approximate models, looking at the brain on multiple levels of complexity. I am also interested in comparing biological intelligence to artificial intelligence, along with learning how to effectively interface between them.

joshua_hewson@brown.edu