Our Team

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Carlos Brody

Carlos Brody did his Ph.D. in Computation and Neural Systems at Caltech, in John Hopfield’s group, then did a computational postdoc in Ranulfo Romo’s monkey neurophysiology lab in Mexico (which is Carlos’ country of origin). After a short second postdoc at NYU with Tony Movshon, he began his first faculty position at Cold Spring Harbor Laboratory, where he led a computational group until the allure of exploring cognitive processing in rodents led him to start doing experiments. He moved to Princeton in 2007, and since 2008 has been an Investigator of the Howard Hughes Medical Institute. His group combines high-throughput behavior, electrophysiological recordings, optogenetic perturbations, and computational modeling to understand the neural circuit mechanisms that underlie cognitive processing.


Mark S. Goldman

Mark Goldman received a Ph.D. in physics from Harvard University in 2000 and did his postdoctoral work in theoretical neuroscience at the Massachusetts Institute of Technology. He is currently the Joel Keizer Chair in Theoretical and Computational Biology at UC Davis and an HHMI Professor. Dr. Goldman’s research uses mathematical modeling and computer simulations to address the cellular, synaptic and circuit mechanisms underlying a wide range of neurobiological functions including short-term memory storage, sensory processing, and motor control. Of particular relevance to the BRAIN CoGS project, a large set of his work has focused upon mechanisms by which “neural integrator” circuits can temporally accumulate signals into a short-term memory buffer. Dr. Goldman is a recent co-director of the Marine Biological Laboratory’s Methods in Computational Neuroscience Course (2013–2017).


Jonathan Pillow

Jonathan attended the University of Arizona, where he studied mathematics and philosophy, then spent a year as U.S. Fulbright fellow in Morocco before attending graduate school at NYU, where he received a Ph.D. in neuroscience working with Eero Simoncelli on statistical models of spike trains. He moved to London for a postdoctoral fellowship at the Gatsby Computational Neuroscience Unit at UCL, and in 2009 became an assistant professor at the University of Texas at Austin in the department of Psychology. In 2014, Jonathan moved to Princeton, where he is currently an associate professor in the Princeton Neuroscience Institute, Psychology department, and the Center for Statistics and Machine Learning. Jonathan's current research sits at the border between neuroscience and statistical machine learning, focusing on computational and statistical methods for understanding how large populations of neurons transmit and process information.


Sebastian Seung

Sebastian Seung is Anthony B. Evnin Professor in the Neuroscience Institute and Computer Science Department at Princeton University. Seung has done influential research in both computer science and neuroscience. Over the past decade, he helped pioneer the new field of connectomics, applying deep learning and crowdsourcing to reconstruct neural circuits from electron microscopic images. His lab created EyeWire.org, a site that has recruited over 250,000 players from 150 countries to a game to map neural connections. His book Connectome: How the Brain's Wiring Makes Us Who We Are was chosen by the Wall Street Journal as Top Ten Nonfiction of 2012. Before joining the Princeton faculty in 2014, Seung studied at Harvard University, worked at Bell Laboratories, and taught at the Massachusetts Institute of Technology. He is External Member of the
Max Planck Society, and winner of the 2008 Ho-Am Prize in Engineering.


David Tank

David W. Tank is the Henry L. Hillman professor at Princeton University and Co-Director of the Princeton Neuroscience Institute. He also directs the Bezos Center for Neural Circuit Dynamics. At the Simons Foundation, he is the Director of the Simons Collaboration on the Global Brain. His research interests include the measurement, analysis, and modeling of neural circuit dynamics. At Bell Laboratories he contributed to the development of attractor network models of neural decision-making, the development of functional MRI imaging, and the development of cellular resolution optical imaging of neural dynamics. More recently, his work has focused on the mechanisms of persistent neural activity and the development and application of rodent virtual reality systems combined with large-scale optical recording and electrophysiology to study neural circuit dynamics during navigation and decision making.


Sam Wang

Sam Wang is Professor in the Neuroscience Institute and the Department of Molecular Biology, with affiliations in quantitative and computational biology, cognitive science, law and public affairs, and information technology policy. His neuroscience laboratory (synapse.princeton.edu) investigates how brains learn from sensory experience in adulthood and development, with relevance for autism. Research focuses on the cerebellum’s role in cognition and social processing, using data science, advanced microscopy and electrophysiology, molecular probe design, and automated behavioral tracking. He is co-author of over eighty articles on neuroscience and election policy, as well as two books (Welcome To Your Brain and Welcome To Your Child's Brain), each translated into over 20 languages. Before joining the Princeton faculty in 2000, Wang studied at Caltech and Stanford and worked at Bell Laboratories. To read about Sam Wang's election data work, see election.princeton.edu and gerrymander.princeton.edu.


Ilana Witten

Ilana graduated from Princeton University with an A.B. in Physics in 2002, and received her Ph.D. in Neurosciences from Eric Knudsen‘s lab at Stanford University in 2008, while also collaborating with Haim Sompolinsky. She subsequently completed a postdoctoral fellowship in Karl Deisseroth‘s lab in the Department of Bioengineering at Stanford. Since 2012, she has been a faculty member in the Neuroscience Institute and Department of Psychology at Princeton University. Her lab studies the neural circuits for reward learning and decision making, with a focus on the role of dynamics and feedback.



Mikio Aoi

Dr. Aoi is a postdoctoral associate in the lab of Jonathan Pillow. His research is broadly aimed at the development of innovative statistical and machine learning frameworks for studying how neuronal populations manage computational challenges through complex interactions. Before pursuing an interest in neuroscience he earned a bachelor’s degree in Kinesiology from California State University, Long Beach and a Ph.D. in Mathematical Biology from North Carolina State University studying cerebrovascular function in stroke patients. From 2011-2014 he worked with Uri Eden and Mark Kramer in the Department of Mathematics at Boston University where he developed statistical methods for assessing rhythmic synchrony in neuronal populations. He has been working in the Pillow lab since 2014 developing scalable methods for analyzing high dimensional datasets of neuronal activity in animals performing perceptual decision making tasks.


Avinash Avinash

Avinash is a Ph.D. student in the lab of Mark Goldman at UC Davis. His general interests lie in using machine learning techniques to understand the workings of neural circuits during learning and memory formation. Currently, Avinash is working on implementing a neural circuit that performs temporal difference learning using sequential firing to bridge time delays. Previously, he received a BS in Physics at Indian Institute of Science located in Bangalore, India where he primarily worked on open quantum systems and quantum field theory.


Scott Bolkan

After obtaining his bachelor’s degree in Human Biology with a concentration in Neuroscience at Stanford University, Dr. Bolkan went on to complete a Ph.D. in Neurobiology and Behavior at Columbia University in 2017. Working in the laboratories of Dr. Joshua Gordon and Dr. Christoph Kellendonk, he investigated how reciprocal circuitry interconnecting the mediodorsal thalamus and prefrontal cortex supports component processes of spatial working memory and other cognitive behaviors in mice. Now he is investigating the striatal substrates supporting working memory guided decision-making in the Witten lab. Dr. Bolkan’s core research interest is to understand how local and distributed neural circuits contribute to component operations mediating cognitive behaviors. Towards this goal his work employs a variety of tools for directly measuring and manipulating neural activity in genetically defined neural populations, as well as computational techniques for the analysis of neural dynamics.


Brian DePasquale

Dr. DePasquale received a BS in physics from Fordham University in 2005 and a Ph.D. in neurobiology and behavior from Columbia University in 2016 as a NSF Fellow. Under the tutelage of Dr. Larry Abbott, during his Ph.D. he developed algorithms for training artificial neural networks and used these models to describe how populations of neurons can give rise to low-dimensional activity patterns for performing computations. He began his postdoctoral research at the Princeton Neuroscience Institute in 2016 in the labs of Dr. Carlos Brody and Dr. Jonathan Pillow. Broadly, his research focuses on the development and use of mathematical models to describe the relationship between neural activity and behavior.


Zahra Dhanerawala

Zahra studied biochemistry and mathematics at Simmons College before joining Princeton University as a Research Specialist in 2018. As an undergraduate she was involved in research on mechano-activated signaling in the endothelium, flexible behaviors in larval zebrafish, and mathematical models of ion channel dynamics. Ms. Dhanerawala assists in various microscopy and computational efforts for all the labs in the BRAIN CoGS collaboration that study brain-wide activity and connectivity. She has helped develop tools for automated cell detection in light-sheet microscopy images in collaboration with the Seung lab and Cold Spring Harbor Laboratory and applied statistical models to contribute to the understanding of cerebello-thalamo-cortical connectivity and cerebellar topography.


Efthymia (Mika) Diamanti

Efthymia (Mika) Diamanti is a postdoctoral researcher in the Tank lab. She is interested in how inter-areal interactions contribute to the broad emergence of cognitive signals in the brain. Mika obtained a PhD in systems neuroscience from University College London under the supervision of Matteo Carandini. Her PhD work focused on visual information processing during navigation. She has a multidisciplinary background, including two undergraduate degrees, in law and physics, from University of Athens, Greece and a master’s degree in physics from Imperial College London.


John D’Uva

John graduated with a B.S. from the University of Miami where he concentrated on topics in Econometrics and Game Theory while playing Wide Receiver for the Hurricanes’ football team. Working with Dr. Sam Wang, John is currently developing convolutional neural networks for extracting biological signal from light sheet microscopy images, and then applying statistical methods to combine these datasets with behavioral measures to uncover regional/functional associations throughout the brain. John’s background in Computer Science has also given him the opportunity to take a lead role in the lab’s goal to produce and circulate accessible, user-friendly software tools for light sheet data management and analysis. John will be applying for Computational Neuroscience Ph.D. programs this fall to further explore the intersection between technology, statistics, and neural dynamics. He is interested in developing novel approaches for mapping regional connectivity and quantifying the physical changes in the brain that occur during encoding.


Ben Engelhard

Dr. Engelhard obtained his bachelor degree from the Technion - Israel Institute of Technology, Haifa, Israel. He then joined the lab of Prof. Eilon Vaadia in The Hebrew University of Jerusalem, where he got his Ph.D. in computational neuroscience in 2015. Dr. Engelhard investigated cortical dynamics during Brain-Machine –Interface learning, as well as the relationship between oscillatory activity, single-neuron synchrony, and behavior. He moved to the Princeton Neuroscience Institute in 2015 for his post-doctoral research in the laboratories of Dr. David Tank and Dr. Ilana Witten. He is interested in the behavioral role of different subcortical neural systems, and in particular in the role of midbrain Dopamine neurons in complex behavior. He uses cell type specific, cellular-resolution deep-brain imaging techniques in mice performing a navigation-based, accumulation of evidence decision-making task.


Austin Hoag

Dr. Hoag is an observational astrophysicist who received his Bachelor's degree in Physics from Colby College and his PhD in Physics from UC Davis. His research focused on the Epoch of Reionization, a phase transition triggered by the birth of the first stars and galaxies in the first billion years of the Universe. Before coming to Princeton, he was a postdoctoral researcher in the Division of Astronomy and Astrophysics at UCLA as a member of Prof. Tommaso Treu's research group. Dr. Hoag joined Princeton in July 2019 as a software developer, bringing his image and signal processing expertise from astrophysics to develop a robust and user-friendly light sheet microscopy pipeline for the BRAIN CoGS collaboration.


Mark Ioffe

Dr. Ioffe received his Ph.D. in Physics from Princeton in 2017, working with Profs. Michael J. Berry II and William Bialek. His research used a mixture of experimental and computational methods to probe adaptation in the neural coding of populations of retinal ganglion cells. He started his postdoctoral work at the Princeton Neuroscience Institute in the Spring of 2017, working with Profs. David Tank and Carlos Brody. His current focus is on optical methods for experimental neuroscience, with the goal of applying these methods to perturb neural dynamics in behaving animals.


Sue Ann Koay

Dr. Koay received Bachelor degrees in Physics and Computer Science from San Jose State University. She then pursued a Ph.D. in Physics at the University of California, Santa Barbara, initially in string theory and later in experimental high energy physics with the group of Dr. Joseph Incandela. She worked on a variety of research projects at the Large Hadron Collider, CERN (CMS experiment) centered around the search for dark matter, ranging from technical developments for event triggering and particle reconstructions, to background estimations and simplified frameworks for theories of Supersymmetry. With a postdoctoral Dicke Fellowship from the Department of Physics at Princeton University, she transitioned to the field of Neuroscience. She is working in the laboratories of Dr. David Tank and Dr. Carlos Brody at the Princeton Neuroscience Institute in studying working memory using a mouse animal model. Dr. Koay’s interests are in the confluence of experiment and theory and what this may tell us about principles of operation of the brain.


Stephen D. McLaughlin

Stephen McLaughlin received his Ph.D. in Electrical Engineering at Brigham Young University in 2018. While there, he worked in the holography lab, developing leaky-mode spatial light modulators. His publications focused primarily on improvements to frequency controlled light output and the integration of optical systems. Meanwhile, his dissertation concerned possible new applications for leaky-mode spatial light modulators in the realm of optogenetics. Now, he continues to apply his optical engineering background to the construction, maintenance, and improvement of the Bezos Center microscopes.


Edward H. Nieh

Dr. Nieh received his Bachelor’s and Master’s degrees in Bioengineering from the University of Pennsylvania, working with Dr. Brian Litt on automated seizure detection using machine learning in 2010. From there, he joined Dr. Kay Tye’s lab at the Massachusetts Institute of Technology, where he obtained his Ph.D. in Neuroscience in 2016. His work centered around mapping the neural circuits between the lateral hypothalamus and ventral tegmental area and their roles in controlling motivated behaviors such as feeding and social interaction. He joined the labs of David Tank and Carlos Brody for his postdoctoral work in the fall of 2016, where he is now studying how CA1 neurons in the hippocampus form sequences during an evidence accumulation and decision making task.


Marlies Oostland

Dr. Oostland is a Marie Skłodowska-Curie Fellow combined in the lab of Sam Wang at PNI, and the lab of Michael Brecht at the Humboldt-Universität zu Berlin (Germany). She is currently studying the neuronal computations in the cerebellum underlying the cognitive process of decision-making, as well as the perception and prediction of touch and tickling. Before joining the BRAIN CoGS team, Dr. Oostland obtained her MSc and PhD in Neuroscience at the University of Amsterdam (the Netherlands), which included a research project at the University of Cambridge (UK). She then did postdoctoral research in the labs of Matt Nolan and Ian Duguid at the University of Edinburgh (UK), where she performed in vivo whole-cell patch clamp recordings in awake behaving mice to study the role of HCN1 channels in the inferior olive. Dr. Oostland joined the lab of Sam Wang at the Princeton Neuroscience Institute in 2018.


Lucas Pinto

Dr. Pinto obtained his medical degree from the Federal University of Minas Gerais, Brazil, in 2006. He then did a Master's degree in physiology at the same university, where he studied visual processing in owls with Jerome Baron. Dr. Pinto got his PhD in neuroscience from the University of California, Berkeley in 2014, working in in Yang Dan's laboratory. He investigated how circuits downstream of the sensory cortex participate in perceptual decision-making. He moved to the Princeton Neuroscience Institute in 2015 for his post-doctoral research in the laboratories of Dr. David Tank and Dr. Carlos Brody. Dr. Pinto is broadly interested in neural mechanisms underlying cognition, both at the local circuit level and in terms of large-scale interactions between different brain areas. He uses a combination of recording, perturbation and computational techniques to study decision-making behavior. Personal website.


Alexander Riordan

Alexander Riordan is a Ph.D. candidate in the Tank Lab, whose goal is to understand how neuronal interactions produce activity patterns underlying cognition. His approach shifts from computation to experiment as needed. Currently he is combining imaging technologies to test circuit models of memory and navigation. Previously, he has co-developed machine learning methods for cell detection in collaboration with Sebastian Seung, circuit models of cognitive flexibility with Carlos Brody, and nonlinear dynamical models of odor-tracking with Nathan Urban and Bard Ermentrout. His experimental work with Jan Thornton explored therapeutic roles of hormones in schizophrenia. Riordan received a bachelor's degree in mathematics from Oberlin College, and a master’s in neuroscience from Princeton University.


Abigail Russo

Dr. Russo received her Bachelor’s degree in Psychology from Brandeis University in 2013, and a Ph.D. in Neuroscience at Columbia University in 2019. Working with Dr. Mark Churchland, she developed analytic tools for identifying computation-dependent signatures of neural dynamics in primary motor cortex and in the supplementary motor area. She started her postdoctoral work at Princeton in 2019, working with Drs. Carlos Brody and Jonathan Pillow. Her research interests focus on understanding circuit-level neural computation through large-scale neural recordings and computational modeling.


Manuel Schottdorf

Dr. Schottdorf completed his undergraduate education in physics and philosophy at the University of Würzburg in Germany, supported by a Max Weber scholarship run by the German Academic Scholarship Foundation. He then obtained two Master degrees, one in 2011, working in condensed matter physics from Rutgers University, and one in 2013, working in theoretical neuroscience in Göttingen. In 2018, he completed his PhD research in the labs of Fred Wolf at the Max Planck Institute for Dynamics and Self-Organization and Walter Stühmer at the Max Planck Institute for Experimental Medicine. For his work on the reconstitution of visual cortical feature selectivity, he was selected as a recipient of a Boehringer Ingelheim Fonds PhD Fellowship, and won the Otto Hahn medal of the Max Planck Society. Dr. Schottdorf moved to the Princeton Neuroscience Institute in 2018 to study the neuronal basis of cognition.


Kelly M. Seagraves

Kelly M. Seagraves is a CV Starr Fellow in the Neuroscience Institute, working in the lab of Prof. Sam Wang. She received her Bachelor’s degree in Molecular, Cellular, and Developmental Biology from the University of Colorado at Boulder, and her PhD in Zoology from the University of Cambridge as a member of HHMI’s Janelia Graduate Scholars program. Her aim is to understand how the brain controls social interactions and social decision making. To this end, she investigated the acoustic cues used by crickets to perform sound localization in Prof. Berthold Hedwig’s lab at Cambridge, and showed that mice are susceptible to social audience effects in the lab of Dr. Roian Egnor at Janelia. As a postdoc with Dr. Kristin Branson, she used computer vision and machine learning techniques to analyze large and complex behavioral data sets. At Princeton Dr. Seagraves is using behavioral experiments and light sheet imaging to uncover the brain regions associated with the social learning phenomenon of observational learning.


Iris Stone

Iris Stone is a Ph.D. student working in collaboration with Jonathan Pillow and Ilana Witten. Broadly speaking, her interests include understanding both the neural circuitry and behavior that support decision-making and social interactions. Her current work includes using latent-state models to identify the discrete structures underlying these cognitive processes. Previously, she received a B.S. in Physics from George Mason University, where she studied the use of organic and nanomaterials for applications in biomedicine and neuroscience.


Stephan Y. Thiberge

Dr. Stephan Thiberge is the director of the Imaging Core Facility of the Bezos Center for Neural Dynamics. He received his PhD in Physics in 1999 at the University of Nice-France. As part of a post-doctoral work at the Weizmann Institute of Sciences in Israel, he developed wet-SEM, a method permitting the observation of wet samples in scanning electron microscopes. He joined Princeton in 2003 as a post-doctoral fellow working in synthetic biology. In 2006, under the direction of David Tank, he specialized in the field of optics and microscopy, managing the Imaging Core Facility of the Lewis-Sigler Institute.


Adrian Wanner

After completing his undergraduate studies in Interdisciplinary Sciences with majors in theoretical physics and neuroinformatics at ETH Zurich, Switzerland, Adrian joined the labs of Prof. Dr. Richard Hahnloser, at ETH Zurich, and Prof. Dr. Tatyana Sharpee, at the Salk Institute in La Jolla, California, to work on new algorithms for estimating receptive fields of neurons in the visual cortex of macaque monkeys and the auditory system of zebrafinches. In the course of his doctoral work on the olfactory system of zebrafish in the lab of Prof. Dr. Rainer Friedrich at the Friedrich Miescher Institute for Biomedical Research in Basel, Switzerland, Adrian became an expert in multiphoton calcium imaging and large-scale electron microscopy-based circuit reconstruction. In September 2017, Adrian was awarded the C.V. Starr Fellowship at Princeton University to study the neuronal basis of working memory in mice, in collaboration with Prof. Dr. Sebastian Seung and Prof. Dr. David Tank.


Zhihao Zheng

Dr. Zhihao Zheng obtained his medical degree from Sun Yat-sen University, China, in 2009. Dr. Zheng completed his PhD in neuroscience under the mentorship of Dr. Davi Bock in the joint program between Janelia Research Campus, HHMI and the Solomon H. Snyder Department of Neuroscience at Johns Hopkins University. During his PhD, Dr. Zheng acquired a whole-brain electron microscopy (EM) volume of an adult fly brain and used the EM volume to map the network architecture of olfactory inputs to the mushroom body, a center for learning and memory in the fly brain. After graduating from PhD, he joined Dr. David Tank’s and Dr. Sebastian Seung’s labs to study how network connectivity of neurons relates to circuit functions or dysfunctions.


David Zoltowski

David Zoltowski is a Ph.D. student in the lab of Jonathan Pillow. He is generally interested in using statistical models to characterize neural responses and dynamics underlying complex behaviors. His specific research interests include latent variable models of neural activity during decision making and scalable inference methods for analyzing neural data. Previously, he received a BS in electrical engineering from Michigan State University and an M.Phil. in Engineering from the University of Cambridge as a Churchill Scholar, where he worked on models of the representation of uncertainty during decision making with Máté Lengyel.


Project Manager


Brigitte Stark

Brigitte Stark received her master’s in human genetics from the Johannes Gutenberg University in Mainz, Germany, where she completed her thesis on Hodgkin's lymphoma. She also holds a degree in science journalism from Virginia Commonwealth University. After moving to Princeton in 1996, she worked for the Institute for Advanced Study as a newsletter editor and taught German for many years. Before joining BRAIN CoGS, Brigitte managed the office of SCIENION, a German medical engineering start-up. Now she combines her organizational talent and passion for science to support all the researchers on her team.




Ben Deverett

Ben studied neuroscience, computer science, and music at McGill University for his undergraduate degree. He joined the MD/PhD program at Rutgers and Princeton in 2013, and he obtained a Ph.D. in molecular biology and neuroscience in 2019. In the BRAIN CoGS collaboration, he used two-photon calcium imaging and optogenetics to study the role of the cerebellum in perceptual decision-making.


Tom Pisano

Tom Pisano is an M.D./Ph.D. candidate and a NIH Ruth L. Kirschstein Predoctoral fellow. As a former member of the Wang Lab, his dissertation research concerns the long distance connectivity of the posterior cerebellum. His research involves a combination of viral tracers, computational neuroanatomy, machine learning and whole brain clearing techniques to look at the topographical organization of cerebellar connections to the rest of the brain. His interests include cerebello-thalamo-cortical connections and the cerebellum's contribution to non-motor behavior. After defending in the spring of 2019, he returned to to Rutgers Medical school, where he will complete his medical training in 2021.


Benjamin Scott

Dr. Scott is an Assistant Professor in the Department of Psychological and Brain Sciences at Boston University. He received his BA from the University of Chicago and his Ph.D. from the Massachusetts Institute of Technology where he was named a Poitras Fellow in Biomedical Engineering. He did postdoctoral research at Princeton University in the laboratories of Drs. David Tank and Carlos Brody. Dr. Scott’s research sits at the interface between biology and neuroscience, and a consistent theme in his work has been the development of new tools for imaging structure and function in the intact nervous system. Examples of his past accomplishments include the generation of techniques for genetic modification in birds, the discovery of a unique form of cell migration in the adult brain, and the development of a system that trains rodents to dock to a two-photon microscope, facilitating in vivo, functional imaging during complex learned behaviors. www.scottcognitionlab.com