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  
Helena
Liu Helena received her Ph.D. in Applied Mathematics from the University of Washington, supervised by Prof. Eric SheaBrown, where she also collaborated with Prof. Guillaume Lajoie at Mila  AI Institute and Profs. Stefan Mihalas, Stephen Smith, and Uygar Sumbul at the Allen Institute. There, she worked at the nexus of computational neuroscience and deep learning, aiming to exploit the rapid advancement in deep learning tools and largescale neural recordings to study how the brain learns to process information. She is currently integrating her previously theorydriven work with datadriven approaches and fitting models to animal data, examining the different learning strategies employed. Outside of research, she enjoys playing the piano, reading, and going to the gym.  
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 Ph.D. 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  
Anushri Arora
Anushri is a 2ndyear Ph.D. Student in Computer Science, with an MS in Computer Science from Columbia University and a B.Tech in Computer Engineering from NMIMS University, Mumbai. At Columbia, she worked on graphical models to infer invivo competitive dynamics between bacteriatumor populations with Itsik Pe'er and Tal Danino. She then spent two years developing deeplearning models for liquid biopsies with Dan Landau at Cornell. Her current research focuses on interpreting dynamics in lowrank RNNs and how they're affected by external inputs.  
Kevin Chen (joint with Andy Leifer) Kevin is a 6thyear Ph.D. 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 3rdyear Ph.D. 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 4thyear Ph.D. 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 6thyear Ph.D. 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.  
Yoel Sanchez
Araujo (joint with Nathaniel Daw) I'm a 6th year Ph.D. 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 Ph.D. 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 4thyear Ph.D. 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  
