Lab Director

Jonathan Cohen


Leigh Nystrom

Declan Campbell

graduate student

I am a first-year graduate student in computational neuroscience broadly interested in the neural mechanisms underlying cognitive flexibility and abstraction. I am particularly excited about recent research exploring the uses of episodic memory in planning and reasoning, as well as the interactions between cortical and hippocampal memory systems that support these dynamic cognitive processes.

Zack Dulberg

graduate student

I am a family physician and current PhD candidate in neuroscience at Princeton University. I grew up near Toronto and completed my B.Sc. in physics and physiology at McGill University, my M.D. from the University of Toronto and my medical residency in Ottawa. My research now focuses on using modern tools of artificial intelligence, such as deep reinforcement learning, to build models of human cognition. More specifically, asking the question: are there benefits to constructing artificial agents with “multiple selves”, and could those benefits explain why humans experience psychological conflict?

Steven Frankland


I am interested in the computational principles and neural systems that allow the human mind to be so flexible in some cases—for example, language, reasoning, and planning—and so capacity limited in others—for example, short term forms of memory and attention. My recent theoretical work has approached these questions from the perspectives of information-theory and classic neural network formalisms, in pursuit of some general principles.

Tyler Giallanza

graduate student

Many aspects of cognition, such as learning, planning, and memory, must navigate the trade-off between specificity and generality. For example, optimizing performance for known tasks while remaining capable of flexibly learning new tasks appears to come naturally to humans but has been difficult to model. I am broadly interested in the role that context and abstraction play in shaping this balance, seeking to understand how the current internal and environmental conditions interact with abstract representations of goals and tasks. My research approaches these questions using a combination of computational modeling and the analysis of behavioral experiments, with the ultimate goal of understanding the normative computational principles underlying these behaviors.


Bryant Jongkees


I am a former postdoc and current collaborator from Leiden University, the Netherlands. My research focuses on process models of adaptive cognitive control, which aim to capture the ways in which agents (both artificial and human/natural) adjust their information processing and decision making in a rational, context-appropriate manner. I am particularly interested in how agents learn to optimally balance critical trade-offs such as stability versus flexibility in attention and speed versus accuracy in responding.

Kamesh Krishnamurthy


I am a theorist interested in problems at the intersection of machine learning, neuroscience and biophysics. I am interested in understanding the principles behind how networks of neurons can form cognitive maps and learn abstract relations.

Currently, I am a C.V. Starr Fellow and a CPBF Fellow at Princeton University, hosted by the Department of Physics and the Princeton Neuroscience Institute. Prior to this, I spent a semester as a Simons-Berkeley Research Fellow participating in The Brain and Computation program at the Simons Institute for Theory of Computing. I completed my graduate studies at the University of Pennsylvania.

Sreejan Kumar

graduate student

I am a PhD candidate at the Princeton Neuroscience Institute. Before that, I graduated from Yale University with majors in Computer Science and Statistics & Data Science. During undergrad, I worked with Marvin Chun and Nicholas Turk-Browne (dept of Psychology). 

A core essence of human intelligence is the constant drive to understand the world around them. I am broadly interested in how human brains take experience in the world and extracts abstract knowledge that helps them learn faster. I'm interested in studying this process in a variety of domains such as reinforcement learning, language processing, and visual perception. I utilize a combination of artificial neural network simulations, large-scale online behavior experiments, and brain imaging analysis in my research. 

Javier Masís


I'm broadly interested in learning and decision making. Currently, I study how agents take learning and information prospects into account when making choices in the short and long term combining human behavioral experiments with computational modeling. 

I am a Presidential Postdoctoral Research Fellow at the Princeton Neuroscience Institute, where I work on cognitive modeling and human behavior with Jonathan D. Cohen. I earned my Ph.D. in Biology at Harvard University, where I worked on strategic decision making in rodents with David D. Cox and Andrew M. Saxe. I obtained an A.B. in Molecular Biology summa cum laude from Princeton University.

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Shanka Subhra Mondal

graduate student

I am a fourth-year PhD student in Electrical and Computer Engineering, with a B.Tech in Electronics and Electrical Communications Engineering from the Indian Institute of Technology, Kharagpur where I worked on various applications of deep learning for biomedical imaging, systems, and computer vision. My current research involves using ideas from neuroscience and cognitive science to build artificial neural network models that can demonstrate sample efficiency and systematic generalization as shown by humans.


Harrison Ritz


I'm interested in how humans and other animals achieve their goals through planning and hard work, often from the perspective of (optimal) control theory.

My research uses behavioral experiments (e.g., psychophysics, model-based planning), neuroscience (e.g., fMRI, OP-MEG, iEEG/ephys collaborations), and computational modelling (e.g., evidence accumulation, dynamical systems, inverse optimal control) to triangulate how we control our thoughts and actions.

I completed my MSc at University of Western Ontario with Ingrid Johnsrude and my PhD at Brown University with Amitai Shenhav, Michael J. Frank, and Matthew Nassar (hon).

I am currently a C.V. Starr Postdoctoral Fellow at the Princeton Neuroscience Institute with Jonathan Cohen and Nathaniel Daw.


Simon Segert

graduate student

As we go about our lives, we encounter a staggering array of objects and people, and need to keep track of not only their individual properties but also the complex relationships that exist between them. While many animals can perceive and reason about tangible objects, what sets humans apart is our remarkable ability to reason at a higher level of abstraction and understand the complex, higher-order relationships that exist between them. We can understand the relationship between a parent and child, or between a student and teacher, and use this knowledge to make predictions about how people might behave in a novel situation. But how is it that such a complex ability can be implemented in a biological neural network like the human brain, and how can we use the principles underlying this implementation to design more powerful and flexible artificial intelligence systems? In my research I (1) use a combination of behavioral experimentation, computational modeling, and mathematical analysis to reverse-engineer the cognitive and biological mechanisms that allow people to reason relationally and (2) use the insights obtained thereby to design and build artificial systems that can reason and behave more like people do. 

William Wolf

research specialist

I am a lab manager in the NCC Lab. I completed my B.S. in Biopsychology, Cognition, and Neuroscience at the University of Michigan in 2020 with a minor in Environmental Science. There I worked with James Hoeffner and Alexandra Rosati.

My primary research interest is the application of neuroscientific methods in the study of psychiatric disorders and their treatments. In the NCC Lab, my work largely focuses on running behavioral experiments and conducting neuroimaging research using fMRI. Going forward, I plan on attending law school, where I aim to apply my background towards shaping policy within the emerging area of Science and Technology Law.

Lab Alumni

Postdoctoral Trainees

Graduate Students

Research Specialists