Mehrdad Jazayeri, Ph.D
Investigator, McGovern Institute for Brain Research
Professor, Department of Brain and Cognitive Sciences
Director of Education, Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Zoe Boundy-Singer, Ph.D
Postdoc
Zoe is interested in computations and neurobiological basis of confidence in sensorimotor behavior. Her approach consists of a combination of computational modeling, perceptual experiments, and electrophysiological recordings. Zoe received her PhD from the University of Texas at Austin where she studied how activity in early sensory cortex informs perceptual decisions and confidence.
Ruidong Chen, Ph.D
Postdoc
Ruidong is interested in how the brain uses hierarchical structures to make sense of the world and understand other minds.
Ruidong obtained his PhD from Cornell University where he studied the role of basal ganglia in motor skill learning in songbirds.
Mohammad Amin Fakharian, Ph.D
Postdoc
Amin is interested in how the brain regulates and controls breathing and how breathing, in turn, influences decision-making and behavior. Amin received his Ph.D. from Johns Hopkins University, where he studied how cerebellar microcircuits ensure precise saccadic eye movements.
Johannes Lachner, Ph.D
Postdoc, Co-advised with Neville Hogan
Johannes develops physics-grounded, human-inspired robotic systems by combining motor neuroscience, AI, and differential geometry, with applications in safe human-robot interaction and robot-assisted rehabilitation..
Gabriel Stine, Ph.D
Postdoc
Gabriel is interested in the computational principles that underlie cognitive processing and in the biological principles that govern how computation is achieved by neural circuits. His research is focused on cortical, subcortical, and cerebellar interactions during learning. Gabriel received his PhD from Columbia University where he studied the neurobiology of perceptual decision making.
Nicholas Watters, Ph.D
Postdoc
Nick is interested in the neural basis of sample-efficient learning and structured generalization. He believes that both of these are in large part consequences of compositional representations and factorized computation. He studies these phenomena in the domain of intuitive physics and is particularly interested in how the brain represents and reasons about multiple visual objects.
Nick also has a long-standing interest in artificial intelligence and hopes that his research may ultimately contribute to the development of AI algorithms that show more human-like learning efficiency and behavioral flexibility.
Marisol Espinoza, Ph.D
Research Specialist
Marisol is interested in how the brain uses and represents the temporal structure of events in the world to guide perception and behavior. She received her PhD from the National Autonomous University of Mexico (UNAM), where she studied the neural and behavioral mechanisms underlying temporal pattern discrimination.
Jack Gabel
Graduate Student
AI systems can trounce a grandmaster match after match, yet struggle to set up a chess board. Meanwhile, humans effortlessly manipulate a huge variety of objects. What is the neural basis of this behavioral flexibility? To answer this question, Jack aims to model the brain as subjects interact with physical scenes of objects.
Sol Markman
Graduate Student
Sol is interested in how humans and animals actively seek out information to make inferences and resolve uncertainties about the hidden states of the world. How do we decide which information is worth our time, energy, and cognitive effort to pursue? She hopes to gain insights about the neural circuitry and dynamics that support the computations underlying these flexible information-seeking behaviors.
Kanishka Mitra
Graduate Student
Kanishka Mitra is a PhD student in EECS at MIT. His research focuses on how neural mechanisms in the brain support hierarchical planning and task decomposition—organizing goals and subgoals to solve complex problems. He studies how the brain builds and applies inductive biases to enable efficient problem-solving and generalization. By bridging neuroscience and machine learning, his work aims to inspire the design of more robust, efficient, and generalizable reinforcement learning algorithms.
Nikasha Patel
Graduate Student
Nikasha is interested in the neural mechanisms underlying our ability to exploit physical dynamics to achieve a goal, especially when we don’t have full controllability within our system. For example, how is a volleyball player able to pass a moving ball to a precise location on the court, or a child able to use a stick to reach something on a high shelf? She aims to tackle these questions through computational and experimental techniques, drawing inspiration from control theory and physically-grounded tasks.
Aída Piccato
Graduate Student
Aída is interested in understanding the neural basis of memory strength – the precision and confidence with which we recall information. She is using experimental and computational techniques to identify the computations linking changes in neural activity over time to patterns of remembering and forgetting. She is interested in how these findings might inform models of memory that exhibit human-like flexibility and capacity.
Meghana Potta
Graduate Student
Meghana’s research interests lie in understanding the neural basis of flexible behavior. She specifically focuses on how our brains infer context and use inferred contexts to create more detailed and accurate models of the world, which in turn guide adaptive behavior. Additionally, she is interested in studying the neural implementations of canonical neural computations and how they can result in cognitive deficits when they go awry. Meghana holds a Master’s degree from the Institute of Neuroinformatics at ETH Zurich, where she explored and studied the use of linear time-invariant systems to better understand neural dynamics.
HoKyung Sung
Graduate Student
Hokyung is interested in how our brains “make sense” of the world. Concretely, he hopes to understand the neural basis of cognitive phenomena involving violations-of-expectation and their resolutions, with a focus on characterizing the computational principles that underlie explanations and explanation-seeking curiosity. More broadly, he is enthusiastic about investigating fundamental questions about the mind that lie at the intersection of philosophy, artificial intelligence, and neuroscience.
Cheng Tang
Graduate Student
Cheng is interested in understanding how the brain composes new knowledge from known knowledge. Using neurophysiological recording on non-human primates, Cheng aims to find out the neural basis of the ability to solve novel problems with different compositions of known operations. He is also interested in comparing neural network models with biological data to test hypotheses about the brain, and developing biologically inspired artificial intelligence.
Chiara Caraccio
MD Student, HST Program
Chiara is interested in exploring questions at the intersection of neuroscience, artificial intelligence, and philosophy. She hopes to use tools from each discipline to probe the mind’s cognitive architecture. She is currently investigating the mental algorithms underlying short-term memory.
Katie Coughlin
Executive Assistant
Michal De-Medonsa
Website Manager
Yuelai (Mollie) Feng
Technical Associate I
Mollie is interested in the neural codes that shape our fundamental human experience—including spatiotemporal perception, memory, abstraction, and decision-making. She would like to explore these topics with electrophysiology experiments and dynamical systems modeling.
Valmiki Kothare
Robotics Engineer – Tabletop Project
Valmiki is interested in the intersection of neuroscience and robotics, from brain-computer interfaces to the study of the human brain as inspiration for robust, sample-efficient reinforcement learning in robot reasoning and motion. In his current role as Robotics Engineer for the Tabletop Project, Valmiki is designing the robotics control software and sensor data collection/processing pipeline that will automate electrophysiology experiments on physical reasoning tasks. Valmiki received his Master’s degree from Carnegie Mellon University, where his research focused on multi-objective path planning and multi-agent collaboration using reinforcement learning.
Srinidhi Naidu
Technical Associate I
Srinidhi is interested in the neural mechanisms underlying decision-making, specifically in how the brain differentiates rapid, short-term choices from deliberative, long-term ones. She is also curious about how the brain accumulates evidence over time to guide longer-term decisions. She hopes to explore these questions using electrophysiology recordings and computational modeling techniques.
Victor de Lafuente
National Autonomous University of Mexico
Ila Fiete
Massachusetts Institute of Technology
Stefano Fusi
Columbia University Medical Center
Valerio Mante
University of Zurich
Srdjan Ostojic
École de Neurosciences Paris
Maneesh Sahani
University College London
Rebecca Saxe
Massachusetts Institute of Technology
Krishna Shenoy
Stanford University
David Sussillo
Stanford and Google AI
Josh Tenenbaum
Massachusetts Institute of Technology
Postdoctoral Research Supervised (primary supervisor)
Mahdi Ramadan, PhD (2024-2025)
Aran Nayebi, PhD (2022-2024)
Sujaya Neupane, PhD (2018-2024)
Hansem Sohn, PhD (2015-2023)
Michael Yoo, PhD (2020-2022)
Rishi Rajalingham, PhD (2019-2022)
Egger, Seth, PhD (2013-2019)
Remington, Evan, PhD (2014-2019)
Wang, Jing, PhD (2013-2018)
Narain, Devika, PhD (2015-2017)
Aghdaee, Mehdi, PhD (2013)
PhD Students Supervised (primary supervisor)
Setayesh Radkani (2025)
Nicholas Watters (2025)
Mahdi Ramadan (2024)
Alexandra Ferguson (2023)
Eli Pollock (2022)
Morteza Sarafyazd (2021)
Nicolas Meirhaeghe (2021)
Visiting Members
Lucas Shoji, UROP, MEng Student (2024-2026)
Tara Sarma, MEng Student (2026)
Vlada Petrusenko, MEng Student (2025)
Mahdi Afsari, UROP Student (2024)
Albert Qin, UROP Student (2023)
Olivia Gozel, Ph.D (2023)
Isabella Milanes, MSRP Summer Intern (2023)
Adam Gosztolai, Ph.D (2023)
Jason Li, , UROP Student (2022-2023)
Xiaomei Fan, SCGB Fellow (SURF Program) (2022)
Sandy Wang, SCGB Fellow (SURF Program) (2022)
Chisom Ume, SCGB Fellow (SURF Program) (2022)
Clara Melhem, SCGB Fellow (SURF Program) (2022)
Dagim Belete (2021)
Shadi Tasdighi Kalat (2019)
Xiang Li (2017)
Lucy Lai, CSNE (2016)
Elliot Nauert, MSTP CBMM (2015)
Vivian Liu, MSRP (2014)
Chau Vu, UROP, MIT (2013)
Staff
Adhara Martellini (2023 – 2025)
Neelima Valluru (2022 – 2024)
Jack Gabel (2021-2023)
T. Vincenza Parks (2016-2020)
Adam Akkad (2017-2020)
Eghbal Asl Hosseini (2015-2016)
Rossana Chung (2013-2018)