neural coding & computation group
Principal Investigator  
Jonathan Pillow
Jonathan is a professor in the Princeton Neuroscience Institute (PNI), with an affiliation to the Center for Statistics & Machine Learning (CSML) and the Program in Applied & Computational Mathematics (PACM). He received a Ph.D. in neural science from NYU (supervised by Eero Simoncelli), and was a postdoc at the Gatsby Computational Neuroscience Unit at UCL. Jonathan was an assistant professor at UT Austin (20092014) before moving to Princeton in 2014.  
 
Postdocs  
Matthew Creamer (joint with Andy Leifer) Matt is a C. V. Starr Fellow at the Princeton Neuroscience Institute and is jointly advised by Andrew Leifer and Jonathan Pillow. He is currently working on modeling wholebrain calcium dynamics and characterizing functional changes between neurons during learning in C. elegans. Matt received his PhD from Yale, working with Damon Clark studying how animals detect visual motion cues and use these cues to regulate their walking speed. When not in lab, he enjoys rock climbing, running, dungeons and dragons, and video games  
Zeinab Mohammadi
Zeinab received her Masters and PhD in Electrical Engineering from the University of Colorado, where she developed a new realtime spike sorting algorithm (EGNG) to analyze the HighDensity Microelectrode Array (HDMEA) data such as Neuropixels probe data. Generally, she is interested in the intersection of machine learning, signal processing and computational neuroscience to develop algorithms for analyzing the neural activity. Zeinab's current research includes using GLMHMM to model animal behaviors and multiregion neural analysis methods.  
Rich Pang (joint with Mala Murthy, Josh Shaevitz, & Bill
Bialek) Rich received his Ph.D. in neuroscience from the University of Washington, where he used computational techniques to address a variety of questions about animal behavior and neural information processing. He is currently working on theorydriven analysis and modeling methods for understanding acoustic communication and its neural substrates. More generally, he is interested in applying techniques from physics, statistical modeling, and network science to understand how neural circuits represent and manipulate the many complex information structures making up daytoday experience.  
Dean
Pospisil
Dean received his Ph.D. in neuroscience from the University of Washington with Dr. Wyeth Bair. There he jointly developed methods for statistical estimation and the application of neural network models to gain insights into cortical visual representation. He is currently working on the statistical integration of population neural dynamics with causal manipulation data, eigenvalue estimation, and interpretability of neural network models.  
Alex
Riordan Alex is a hybrid computationalexperimental neuroscientist and NIH DSPAN Fellow at the Princeton Neuroscience Institute. He received his PhD in neuroscience from Princeton University with David Tank. There he developed methods for deep brain connectomics on functionallyidentified neurons, then applied these techniques to grid cells in entorhinal cortex. He also codeveloped machine vision approaches to identifying neurons in largescale calcium imaging data. Alex’s current work focuses on statistical and reinforcement learning techniques to accelerate learning in closedloop behavioral tasks. More generally, he seeks to provide mechanistic links between levels of abstraction in neuroscience by combining quantitative theory and experiment.  
Anuththara
Rupasinghe Anuththara received her Ph.D. in Electrical Engineering from the University of Maryland, College Park, working with Dr. Behtash Babadi. Her dissertation research focused on introducing Bayesian models and methods to directly infer latent spectral and temporal network organizations in the brain from highdimensional neural data, specifically neuronal spiking data, twophoton calcium imaging data, and wholebrain lightsheet microscopy imaging data. She is currently working on developing a continuous time formulation for overdispersed spiking processes. In general, she is interested in the intersection of statistical signal processing and computational neuroscience, to develop new statistical tools that would facilitate gaining better insights into brain functionality.  
 
Students  
Kevin Chen (joint with Andy Leifer) Kevin is a 6thyear PhD student in PNI, with B.S. degree from National Taiwan University and M.S. research at Academia Sinica, where he studied predictive coding in the retina. After coming to Princeton, he was fascinated by neural dynamics and behavior in C. elegans. He is broadly interested in statistical models for animal behavior, biophysical models, and dynamics in neural networks.  
Yousuf ElJayyousi (joint with Ilana Witten) Yousuf is a 2ndyear PhD student at PNI with a BS in Biomedical Engineering from the University of Missouri. There, he worked on experiments and analyses investigating striatal contributions to goaldirected behavior in rodents with Professor Ilker Ozden. At Princeton, he is jointly advised by Professors Jonathan Pillow and Ilana Witten. His current research interests focus on elucidating neural mechanisms of balancing exploreexploit strategies using reinforcement learning/hidden Markov models.  
Victor Geadah Victor is a 3rdyear PhD student in applied mathematics (PACM), with a MASt (Part III) in applied mathematics from the University of Cambridge and a B.Sc. in mathematics from the University of Montreal, where he worked on dynamic coding in recurrent neural networks with Dr. Guillaume Lajoie. He is interested in dynamical systems theory and probabilistic machine learning, and their applications to neuroscientific inquiries. Current research projects target how neural and latent dynamics can support representations for cognitive processes.  
Orren KarniolTambour
Orren is a 5thyear PhD Student in PNI, with an MS in Symbolic Systems from Stanford University and BA in Economics from Brandeis University. Previously, he worked on encoding models for a retinal prosthesis with EJ Chichilnisky at Stanford, and spent time doing research in industry, at an infectious disease genomics startup and a hedge fund. His current research focus includes statistical modeling of multiregion neural activity underlying cognitive behavior.  
Aditi
Jha Aditi is a 5thyear PhD student in Electrical Engineering with a B.Tech in Electrical Engineering from Indian Institute of Technology Delhi, where she worked on compositionality in convolutional neural networks with Sumeet Agarwal. She is interested in generative models, Bayesian statistics and their applicability in understanding neural data. Her current research focuses on sufficient dimensional reduction for fMRI using a generative approach.  
Yoel Sanchez
Araujo (joint with Nathaniel Daw) I'm a 5th year PhD student at PNI. Broadly speaking, I'm interested in the intersection between Neuroscience and Artificial Intelligence, and statistics. I'm particularly interested in leveraging ideas and methods from Reinforcement Learning and Bayesian models of behavior and cognition to inform Neuroscientific investigations. Currently I am jointly advised by Professors Jonathan Pillow and Nathaniel Daw, and closely collaborate with Professor Ilana Witten. Before starting my PhD I was a research assistant working with Professor Nathaniel Daw at Princeton. My work for the most part involved writing out a Bayesian model of change point detection and before that I worked at MIT for 2 years as an RA.  
Bichan Wu
(joint with Ilana Witten) Bichan is a 3rdyear PhD student in PNI. She majored in psychology and mathematics as an undergrad in Peking University mostly working with human behavioral experiments. She then spent 2 years as an RA in Kastner lab at PNI woking on monkey training and ephys data analysis. As a graduate student she’s mainly interested in probabilistic modeling with single neuron data.  
 
 
 
Alumni  
