Joint Faculty

  • Afonso S. Bandeira

    Assistant Professor of Mathematics and Data Science
    Applied Mathematics, Optimization, Probability, Information Theory, Signal Processing, Mathematics of Data Science
  • Sam Bowman

    Assistant Professor of Linguistics and Data Science
    Natural language processing, artificial neural networks, and computational semantics.
  • Joan Bruna

    Assistant Professor of Computer Science and Data Science
    Machine Learning, Signal Processing and High-Dimensional Statistics
  • Kyunghyun Cho

    Assistant Professor of Computer Science and Data Science
    Research focuses in the area of Natural Language Processing (NLP)
  • Jacob Eisenstein

    Associate Professor of Computer Science & Data Science
    (Starting Fall 2019)
  • Carlos Fernandez-Granda

    Assistant Professor of Mathematics and Data Science
    Research focuses on developing and analyzing optimization-based methods to tackle problems in applications such as neuroscience, computer vision and medical imaging.
  • He He

    Assistant Professor of Computer Science and Data Science
    (Starting Fall 2019)
  • Julia Kempe

    Director of the Center for Data Science; Professor of Computer Science and Mathematics
  • Brenden Lake

    Assistant Professor of Psychology and Data Science
    Computational cognitive modeling; Building machines that learn and think like people; Structured probabilistic models; Deep learning
  • Brian McFee

    Assistant Professor of Music Technology and Data Science
    Machine learning, Music Information Retrieval, Recommender Systems, Multimedia Signal Processing
  • Rajesh Ranganath

    Assistant Professor of Computer Science and Data Science
    Probabilistic modeling, approximate inference, Bayesian nonparametric statistics, and their applications to medicine, neuroscience, and biology.
  • Cristina Savin

    Assistant Professor of Neural Science and Data Science
    Computational Neuroscience, theoretical modelling, computer simulations and data analysis
  • Arthur Spirling

    Deputy Director of Center for Data Science; Director of Graduate Studies for Center for Data Science MS Program; Associate Professor of Politics and Data Science


    Research centers on quantitative methods for analyzing political behavior, and he is particularly interested in institutional development and the use of text-as-data.
  • Julia Stoyanovich

    Assistant Professor of Data Science, Computer Science and Engineering
    Computer Science; Data and knowledge management; Ethical data management and analysis; Management and analysis of evolving graphs; Management and analysis of preference data

Visiting Scholar

  • Jean Ponce

    Visiting Faculty, Distinguished INRIA Professor

Associated Faculty

  • Richard Bonneau

    Professor of Biology, Computer Science, and Data Science


    Systems biology and protein modeling.
  • Kyle Cranmer

    Professor of Physics and Data Science
    Collaborative statistical modeling; Statistical methods in particle physics; Data preservation and open access; Digital publishing; Data-analysis
  • Vasant Dhar

    Director of Graduate Studies for Center for Data Science PhD Program; Professor of Data Science and Information Systems
    Prediction; Data Mining; AI, Decision Making
  • Rob Fergus

    Associate Professor of Computer Science and Data Science
    Computer Vision; Large scale object recognition; Deep learning; Machine learning; Statistical methods in astronomy; Computational photography
  • Juliana Freire

    Professor of Computer Science, Engineering, and Data Science: Joint Faculty Starting Fall 2019
    Large-scale information integration; Information visualization and visual analytics; Provenance management;  Big data management and analysis
  • Jennifer Hill

    Professor of Applied Statistics and Data Science
    Causal inference; Missing data; Bayesian nonparametrics
  • David W. Hogg

    Professor of Physics and Data Science
    Astronomy; Cosmology; Probabilistic inference; MCMC
  • Panos Ipeirotis

    Associate Professor of Information, Operations and Management Sciences, and Data Science
    Crowdsourcing; Text mining; Web mining; Data mining; Machine learning; Databases
  • Mik Laver

    Professor of Politics and Data Science; Dean for the Social Sciences
    Crowd-sourced data coding; Automated text analysis; Agent-based modeling
  • Yann LeCun

    Founding Director of the Center for Data Science; Professor of Computer Science, Neural Science, Data Science, and Electrical and Computer Engineering
    Machine learning; Computer vision; Mobile robotics; Computational neuroscience.
  • Foster Provost

    Professor of Information Systems and Data Science
    Data science; Data mining; Knowledge discovery; Machine learning; Predictive modeling; Integrating human and machine computation; Learning; Inference in network data;  Social network analysis, Crowdsourcing; Micro-outsourcing systems
  • Claudio Silva

    Professor of Data Science, Computer Science, and Engineering. Joint Faculty Starting Fall 2019.
    Data Science, Urban Computing, and Sports Analytics; Visualization and graphics; Geometry processing
  • Eero Simoncelli

    Professor of Neural Science, Mathematics, Data Science, and Psychology
    Probabilistic analysis and representation in biological and machine vision (and audition); Statistical signal and image processing
  • S. R. Srinivasa Varadhan

    Professor of Mathematics and Data Science
    Various aspects of stochastic processes; Diffusion processes and their connection to the theory of partial differential equations; Scaling limits of large systems; Large deviations and the analysis of rare events

Adjunct Professor

  • Brian d’Alessandro

    Adjunct Assistant Professor, Center for Data Science
    Machine learning applications, data driven product development
  • Iddo Drori

    Adjunct Associate Professor, Center for Data Science
    Machine learning, Deep Learning, Optimization and Computational Linear Algebra
  • David Rosenberg

    Adjunct Associate Professor, Center for Data Science
    machine learning, sequence prediction, natural language processing

Postdoc Researcher

  • Wai Keen Vong

    Moore-Sloan Post-doc Researcher
    My research lies at the intersection of computational cognitive science and artificial intelligence. I’m particularly interested how people learn concepts and categories, and how this kind of knowledge can be acquired socially, ranging from teaching, question asking and dialog. My research is conducted using behavioural experiments and computational modeling (with a combination of Bayesian models and deep learning).
  • Emin Orhan

    Moore-Sloan Post-doc Researcher
    I obtained my PhD in Brain & Cognitive Sciences from the University of Rochester. I was previously a postdoc at the Center for Neural Science at NYU and then jointly at Rice University / Baylor College of Medicine in Houston. My research interests lie at the intersection of machine learning, cognitive science, and computational neuroscience. Broadly speaking, my research has three main goals: 1) Understanding how current deep learning models and methods work, as well as characterizing their failure modes. 2) Comparing the behavior of deep learning models with qualitative and quantitative data from cognitive science and experimental neuroscience to better understand the mechanistic underpinnings of natural intelligence, and also to point out ways in which these models can be improved. 3) Based on the insights gleaned from the first two goals, improving the current generation of deep learning models.  
  • Thomas Laetsch

    Moore-Sloan Post-doc Researcher
    Data science in sociology; foundational theory in data science, including topics in artificial neural networks and non-commutative regression
  • Katharina Kann

    Moore-Sloan Post-doc Researcher
    The main focus of my research lies on deep learning for natural language processing. In particular, I am interested in morphology (the study of the internal structure of words) and general approaches for the low-resource setting. Examples for the latter include multi-task learning or cross-lingual transfer learning.
  • Richard Galvez

    Moore-Sloan Post-doc Researcher
    Generally, my research lies on the boundary between astrophysics/cosmology and data science, in which fields I often delve a bit deeper. I believe the potential of machine/deep learning algorithms applied to large-scale cosmological and astrophysical surveys is limitless.
  • Fernando Chirigati

    Moore-Sloan Post-doc Researcher
    My research interests are mainly in the area of data management, including provenance management and analytics, large-scale data analytics, data mining, information retrieval, and computational reproducibility. I'm also the Reproducibility Editor of Elsevier's Information Systems Journal, and one of the architects of ReproZip, a tool that facilitates reproducibility of existing computational experiments.
  • Andreu Casas

    Moore-Sloan Post-doc Researcher
    My research interests encompass the areas of political communication, public policy processes, and computational social sciences. I am particularly interested in how social movements and interest groups influence the political agenda and the decision making process in the current media environment. My methodological interests and strengths are natural language processing (text as data), computer vision (images as data), and machine learning and artificial intelligence in general.
  • Johann Brehmer

    Moore-Sloan Post-doc Researcher
    Coming from a background of theoretical physics, my research interests now focus on the interface between particle physics and data science. In particular, I am excited about the Higgs boson and what we can learn about it at the LHC, effective field theories, likelihood-free inference, statistical methods in particle physics, and information geometry. I split my time between the CCPP and NYU's Center for Data Science.
  • Neil Bramley

    Moore-Sloan Post-doc Researcher
    Much of my research has focused on how people learn about causal structure and how they gather new information during extended interactions with their environment. One of my key contributions is the idea of incremental, boundedly rational learning mechanisms in which wholesale belief change takes place through a sequence of local changes.

Data Science Fellow

Research Engineer

  • Heiko Müller

    Research Engineer
    Data Curation; Data Integration; Data Quality; Sensor Data Management and Analytics; Bioinformatics
  • Stefan Karpinski

    Research Engineer at CDS Science and Co-founder of Julia Computing, Inc.
  • Harish Doraiswamy

    Research Engineer
    visualization, topology-based techniques, computer graphics, database systems, and parallel / gpu algorithms

Affiliated Faculty

  • Karen Adolph

    Professor in the Department of Psychology and the Center for Neuroscience
    Perceptual-motor learning and development; Open video data sharing
  • Neal Beck

    Professor of Politics
    Political methodology, more specifically longitudinal data and non-linear methods
  • Juan Bello

    Associate Professor of Music and Music Education
    Computer-based analysis of musical signals and its application to music information retrieval; Digital audio effects and interactive music systems; Machine learning; Data mining; Audio signal processing
  • Gérard Ben Arous

    Professor of Mathematics; Director of the Courant Institute of Mathematical Sciences; Vice Provost for Science
    Probability theory and its applications
  • Michael Blanton

    Associate Professor in the Department of Physics
    Astronomical spectroscopy and image analysis- clustering statistics in large-scale galaxy maps- demographics of the galaxy population
  • Jan Blustein

    Professor of Health Policy and Medicine
    Quantitative methods in policy research; Health management
  • Adam Brandenburger

    J.P. Valles Professor, Stern School of Business
    Game theory, Business strategy, Quantum information
  • Andrew Caplin

    Silver Professor of Economics
    Combining theoretical and machine learning methods to optimize financial offers and financial decisions at household level
  • Xi Chen

    Assistant Professor of Information, Operations and Management Sciences
    Machine learning, High-dimensional statistics, Optimization under Uncertainty, Operations Research
  • Anna Choromanska

    Assistant Professor of Electrical and Computer Engineering
    Machine learning both theoretical and applicable to the variety of real-life phenomena
  • Rumi Chunara

    Assistant Professor of Computer Science and Engineering
    Developing computational and statistical methods across data mining, natural language processing, spatio-temporal analyses and machine learning, to study population-level public health
  • Gloria Coruzzi

    Carroll & Milton Petrie Professor
    Gene network analysis; Data mining and visualization; Systems biology and phylogenomics
  • Rajeev Dehejia

    Professor of Public Policy
    Applied econometrics (external validity in experimental and quasi-experimental methods, propensity score and matching methods, and Bayesian applied econometrics), development economics (child labor, microcredit, and financial development and growth), labor economics (financial incentives and fertility decisions, labor standards), and public economics (religion and consumption insurance)
  • Rohit Deo

    Professor of Statistics and Operations Research
    Long memory time series; Financial data modeling
  • Paul DiMaggio

    Professor of Sociology
    Computational text analysis; Sentiment analysis; Social network analysis; Schema detection in opinion data with population heterogeneity
  • Dustin T. Duncan

    Assistant Professor of Population Health
    Social Epidemiology; Spatial Epidemiology; Neighborhoods; Health Disparities
  • Halina Frydman

    Professor of Statistics and Operations Research
    Survival analysis; Stochastic models in finance and labor economics; Corporate credit ratings migration; Mixtures of Markov chains
  • Krzysztof J. Geras

    Affiliated Faculty
    My main interests are unsupervised learning with neural networks, model compression, transfer learning and evaluation of machine learning models.
  • Judith D. Goldberg

    Professor of Biostatistics
    Statistical methods for the design, conduct, and analysis of clinical and translational research; Statistical methods for epidemiology Survival analysis Statistical methods for the analysis of observational data Statistical issues in medical screening  
  • Jonathan Goodman

    Professor of Mathematics
    Monte Carlo methods; Bayesian methods in astrophysics and finance; Stochastic and deterministic optimization
  • Leslie Greengard

    Professor of Mathematics
    Quantitative methods in biology and medicine; Scientific computing; Electromagnetics; Acoustics; Fluid dynamics; Solid mechanics.
  • Sinan Gunturk

    Associate Professor of Mathematics
    Mathematics of analog-to-digital conversion; Sampling and quantization theory; Sparse representations and redundant representations of data in signal processing; Approximation theory and harmonic analysis methods in data compression
  • Todd Gureckis

    Associate Professor of Psychology
    Computational cognitive science, Unsupervised learning, Active Learning,  Human decision making, Research applications of crowdsourcing
  • Peter Halpin

    Associate Professor
    Psychometrics; Educational data mining; Computer supported collaborative learning and assessment; Scalable methods for non-stationary time series
  • Daphna Harel

    Assistant Professor of Applied Statistics
    Psychometrics; Item response theory; Measurement in the applied health sciences; Model misspecification; Crowdsourcing
  • David Heeger

    Professor of Psychology and Neural Science
    Computational neuroscience (developing and testing computational theories of brain function); Characterizing how the activity of large numbers of neurons represent sensory stimuli, motor actions, and cognitive states; Dimensionality reduction; Vision and image processing; Statistics of images; Bayesian estimation, inference, and prediction
  • Lisa Hellerstein

    Professor of Computer Science and Engineering
    Computational learning theory; Machine learning; Algorithms; Complexity theory; Discrete mathematics
  • Ming Hu

    Assistant Professor of Biostatistics
    Bayesian analysis in bioinformatics and statistical genetics, with particular focus on analyzing the next generation sequencing data
  • Clifford M. Hurvich

    Leonard N. Stern Professor of Statistics & Operations Research, Doctoral Coordinator of IOMS-Statistics
    Time Series Analysis; Model selection for parametric models as well as smoothing parameters and regularization parameters; FFT-Based Algorithms for solving large ill-conditioned Toeplitz systems with applications to forecasting; Point process methods with applications to high-frequency financial data; Long-Range Dependence (scaling laws)
  • Jennifer Jacquet

    Assistant Professor of Environmental Studies
    Cooperation, Conservation Science, Climate Change, Overfishing, Reputation, Social Approval, Wildlife Trade
  • John Jost

    Professor of Psychology and Politics
    Experimental social psychology; Public opinion survey research methods; Quantitative and qualitative analysis of behavioral data in the social sciences
  • Robert Kohn

    Professor of Mathematical Science
    Image processing, inverse problems, quantitative finance, regret-minimization-based methods for prediction; The calculus of variations; Pattern-formation problems from materials science (describing and explaining material microstructure and its consequences)
  • Petter Kolm

    Director of the Mathematics in Finance M.S. Program, Clinical Associate Professor of Mathematics
    Algorithmic and quantitative trading strategies, Econometrics, Data exploration, Forecasting models, High frequency trading, Portfolio construction, Portfolio optimization, Transaction costs, Risk management
  • Steven Koonin

    Director of NYU Center for Urban Science and Progress (CUSP); Professor of Information, Operations & Management Sciences
    Global environmental science
  • Peter Lakner

    Associate Professor of Statistics and Operations Research
    Probability; Stochastic processes; Stochastic optimization and control
  • Jinyang Li

    Associate Professor
    Distributed and networked systems, the systems aspect of Big Data, wireless and mobile systems
  • Mengling Liu

    Associate Professor of Biostatistics
    Semiparametric modeling and inference for survival data, including survival endpoint in joint analysis with longitudinal data
  • Ying Lu

    Associate Professor of Applied Statistics
    Model selection and hypothesis testing, statistical methodology for health and behavioral sciences, applications in biomechanical data and wearable computing
  • Wei Ji Ma

    Associate Professor of Neural Science and Psychology
    Mathematical models of human perception, memory, and decision-making. Neural population coding and neural networks
  • Andrew Majda

    Morse Professor of Arts and Sciences, Professor of Mathematics and Atmosphere/Ocean Science
    Modern applied mathematics: merging asymptotic methods, numerical methods, physical reasoning and rigorous mathematical analysis
  • Suzanne McIntosh

    Clinical Associate Professor of Computer Science
    High performance computing architectures, Big Data analytics, realtime systems, secure software engineering, virtualization
  • Edward Melnick

    Professor of Statistics
    Analysis of time series data; Developing time series models and their statistical properties; Issues related to risk and especially to homeland security
  • Joel Middleton

    Visiting Assistant Professor of Applied Statistics
    Data-driven politics; Design-based estimation and causal inference in randomized experiments; Experiments in voter behavior and political persuasion
  • Bud Mishra

    Professor of Computer Science and Mathematics
    Bayesian and Empirical Bayesian analysis; Shrinkage; Rate-distortion theory; Redescription; Phenomenological models; Model checking and causality analysis
  • Jonathan Nagler

    Professor of Politics
    Methodology; voting behavior; social-media, turnout; Latino voting; the economy and campaigns and elections
  • Chuck Newman

    Silver Professor of Mathematics
    Probability Theory, especially interacting particle systems and percolation models; Statistical Physics, especially Ising, spin glass and coarsening models Monte Carlo and Analytic approaches to the above areas
  • Bijan Pesaran

    Associate Professor of Neural Science
    Neural dynamics and decision making; Brain-machine interface
  • Michael Purugganan

    Dorothy Schiff Professor of Genomics, Professor of Biology, Dean for Science
    Evolutionary and ecological genomics of plant adaptations.
  • Keith Ross

    Leonard J. Shustek Chair Professor in Computer Science
    Data-driven privacy analysis, online social networks, applied probability, Markov decision processes
  • Marc Scott

    Associate Professor of Applied Statistics
    Computationally Intensive Statistics; Categorical Data Models & Clustering Techniques; Statistics in Social Science and Health Applications
  • Yongzhao Shao

    Professor of Biostatistics and Deputy Director of NYU Cancer Institute Biostatistics Shared Resources (BSR)
    Statistical methodology and applications to medical research
  • Dennis Shasha

    Professor of Computer Science
    Computational methods in biology, finance, and wireless communication; Pattern recognition; Querying in trees and graphs; Pattern discovery in time series; Cryptographic file systems; Database tuning
  • Jeff Simonoff

    Professor of Statistics
    Applications of statistics; Statistical methodology; Statistical properties of modern data analytic methods
  • Alexander Statnikov

    Assistant Professor, Department of Medicine, Division of Clinical Pharmacology, NYU School of Medicine; Director, Computational Causal Discovery Laboratory, Center for Health Informatics and Bioinformatics
    Computational causal discovery, variable selection, and supervised learning in high-dimensional data; Comprehensive empirical benchmarking of various machine learning methodologies
  • Torsten Suel

    Professor of Computer Science and Engineering
    Web Search Technology; Algorithms; Databases; Data Compression; Distributed Computation
  • Arun Sundararajan

    Associate Professor of Information, Operations and Management Sciences
    Online privacy; Social network analysis; Computational social science; Causal inference; Econometrics; Text mining
  • Esteban Tabak

    Professor of Mathematics, Chair of the Department of Mathematics
    Density estimation; Dimensional reduction; Classification; Data-driven optimal transport; Bio-statistics; Data-based medical diagnosis
  • Aaron Tennebein

    Professor of Statistics and Actuarial Science
    Sampling; Regression analysis; Application to actuarial problems in mortality estimation; Risk theory
  • Joshua Tucker

    Professor of Politics. Director, NYU Jordan Center for the Advanced Study of Russia
    Comparative political behavior. Social media and politics. Post-communist politics
  • Eric Vanden-Eijnden

    Professor of Mathematics
    Development of mathematical tools and numerical methods for the analysis of dynamical system which are both stochastic and multiscale.
  • Sharon Weinberg

    Professor of Applied Statistics and Psychology
    Application of quantitative methods in the social sciences
  • Margaret Wright

    Professor of Computer Science
    Optimization methods in science and engineering, especially derivative-free methods and constrained nonlinear optimization.

Affiliated NYU Shanghai Faculty