Statistics 960 (SGS-STATS)
STATISTICS 960
Degree Programs Offered: M.S., Ph.D.
Please visit the Statistics website.
Co-Graduate Director: Professor Tirthankar Dasgupta, 504 Hill Center for the Mathematical Sciences, Busch Campus (848-445-7271) td370@stat.rutgers.edu
Co-Graduate Director: Professor Han Xiao, 451 Hill Center for the Mathematical Sciences, Busch Campus (848-445-7640) hxiao@stat.rutgers.edu
Program Description
The Statistics program is for students with strong mathematical backgrounds who wish to pursue research-oriented careers in academia and industry. Students receive an in-depth study in probability theory, mathematical statistics, statistical computing, and special topics in modern data science and machine learning.
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Campus Locations
Busch Campus - New Brunswick
Faculty
Warheed U. Bajwa, Professor of Electrical and Computer Engineering, SOE; Ph.D., Wisconsin (Madison)
Machine learning; high-dimensional statistics; statistical signal processing; networked systems; applications in biological, medical, and physical sciences
Pierre Bellec, Associate Professor of Statistics, SAS; Ph.D., ENSAE ParisTech (France); 2012: Part III (MASt), Cambridge, UK. 2011: Dipl. d'Ingénieur, Ecole Polytechnique, France.
High dimensional statistics, aggregation of estimators, shape constrained problems in statistics, probability theory.
Matteo Bonvini, Assistant Professor of Statistics, SAS; Ph.D., Carnegie Mellon
Causal inference; nonparametric methods; semiparametric efficiency theory.
Steve Buyske, Professor of Statistics, SAS; Ph.D., Brown University; Rutgers
Statistical genetics; biostatistics; psychometrics; experimental design.
Javier F. Cabrera, Professor of Statistics, SAS; Ph.D., Princeton
Biostatistics, Data mining methodology for clinical trial data, Functional Genomics data, DNA and Protein array data, and DNA Barcode data. Statistical computing, graphics, and machine vision.
Rong Chen, Chair and Distinguished Professor of Statistics, SAS; Ph.D., Carnegie Mellon
Nonlinear and multivariate time series analysis; Monte Carlo methods, statistical computing, and Bayesian analysis; statistical applications in economics and business; statistical applications in bioinformatics
Yaqing Chen, Assistant Professor of Statistics, SAS; Ph.D., California (Davis)
Random objects taking values in a metric space; distributional data; manifold data; statistical applications in longitudinal studies, biological and medical sciences, and social sciences
Harry Crane, Professor of Statistics, SAS; Ph.D., Chicago
Probability theory and stochastic processes; clustering and other applications in genetics, network analysis, and homeland security; combinatorics and discrete mathematics
Tirthankar Dasgupta, Professor of Statistics, SAS; Ph.D., Georgia Institute of Technology;
Experimental design, causal inference, sequential exploration of complex surfaces, statistical applications in nanoscience and nanotechnology, statistical methodology in geometric shape error modeling and control, quality engineering and statistical process control.
Ruobin Gong, Associate Professor of Statistics, SAS; Ph.D., Harvard
Theoretical foundations of uncertainty reasoning, Bayesian and generalized Bayesian methodology, random sets, imprecise probability, and Dempster-Shafer theory of belief function. Statistical inference computation with differentially private data, and ethical implications of modern data science.
Derek Gordon, Associate Professor of Genetics, SAS; Ph.D., Stony Brook
Genetic linkage and association methods, Patient Derived Xenograft drug screening methods.
Mert Gürbüzbalaban, Associate Professor in Department of Management Science and Information Systems, RBS, Ph.D., New York University
Monte Carlo methods, high-dimensional statistics, optimization, machine learning, data science.
Qiyang Han, Associate Professor of Statistics, SAS; Ph.D. Washington
Mathematical statistics and high dimensional probability, abstract empirical process theory, and its applications to nonparametric function estimation (with a special focus on shape-restricted problems), Bayes nonparametrics, and high dimensional statistics.
Donald R. Hoover, Professor of Statistics, SAS; Ph.D., Stanford
Longitudinal methods; observational studies; clinical trials; health care utilization, Epidemiology, group randomization and multiple comparisons.
Liangyuan Hu, Associate Professor of Biostatistics; SPH; Ph.D., Brown
Causal Inference, Missing Data, Bayesian data analysis, Machine Learning
Yifan Hu, Assistant Professor of Statistics, SAS; PhD., Illinois (Urbana-Champaign)
Decision-making under uncertainty, with an intersection of optimization, statistics, and machine learning
Ying Hung, Professor of Statistics, SAS; Ph.D., Georgia Institute of Technology
Computer Experiments; design of experiments
Koulik Khamaru, Assistant Professor of Statistics, SAS; Ph.D., California (Berkeley)
Theory and application of statistics, machine learning and optimization.
John E. Kolassa, Distinguished Professor of Statistics, SAS; Ph.D., Chicago
Asymptotics; biostatistics; applications of statistics in nursing and criminal justice
Robert Kopp, Distinguished Professor of Earth and Planetary Science, SAS; Ph.D., California Institute of Technology
Understanding uncertainty in past and future climate change, with major emphases on sea-level change, the interactions between physical climate change and the economy, and the use of climate risk information to inform decision-making.
Regina Y. Liu, Distinguished Professor of Statistics, SAS; Ph.D., Columbia
Data depth, fusion learning, confidence distribution, resampling, nonparametric and robust statistics, aviation risk analysis
Yuetian Luo, Assistant Professor of Statistics, SAS; Ph.D. in Statistics, Wisconsin (Madison)
Distribution-free inference, robust statistics, algorithmic stability, computational complexity of statistical inference, tensor learning and non-convex optimization
Gemma Moran, Assistant Professor of Statistics, SAS; Ph.D., Pennsylvania
Flexible Bayesian models for analyzing high-dimensional data.
Nicole Pashley, Assistant Professor of Statistics, SAS; Ph.D., Harvard
Causal inference, experimental design and analysis using randomization-based framework
Harold B. Sackrowitz, Distinguished Professor of Statistics, SAS; Ph.D., Columbia
Inference and decision theory; multiple endpoint procedures, acceptance sampling; order-restricted inference
Anand Sarwate, Associate Professor of Electrical and Computer Engineering, SOE; Ph.D., California (Berkeley)
Distributed optimization and signal processing, machine learning and statistics, information theory, and privacy-preserving data analysis
Glenn Shafer, University Professor and Board of Governors Professor; Ph.D. Princeton
Game-theoretic statistics and history of probability and statistics.
Michael Stein, Distinguished Professor of Statistics, SAS; Ph.D., Stanford
Spatial and spatial-temporal statistics, extremes, environmental statistics, statistical climatology.
Zhiqiang Tan, Distinguished Professor of Statistics, SAS; Ph.D., Chicago
Nonparametric and semiparametric statistics; applications in causal inference; survey sampling; Monte Carlo integration
David E. Tyler, Distinguished Professor of Statistics, SAS; Ph.D., Princeton
Multivariate analysis; robust statistics; directional data; psychometrics; computer vision and time series; functional data analysis
Guanyang Wang, Assistant Professor of Statistics, SAS; Ph.D., Stanford
Markov chain Monte Carlo, Machine Learning, Probability, Quantum Computing
Sijian Wang, Professor of Statistics, SAS; Ph.D., Michigan
Big-data analytics, statistical learning, proteomics, cancer genomics, precision medicine, bioinformatics, high-performance statistical computing, survival data analysis, longitudinal data analysis and statistical modeling.
Han Xiao, Professor of Statistics, SAS; Ph.D., Chicago
Nonlinear and nonstationary time series; high dimensional analysis, algebraic statistics; random matrix theory
Minge Xie, Distinguished Professor of Statistics, SAS; Ph.D., Illinois
Statistical inference; latent models; longitudinal data analysis and estimating equations; robust statistics; biostatistics, foundation of data science, statistical model, asymptotics, and interdisciplinary research
Min Xu, Associate Professor of Statistics, SAS; Ph.D., Carnegie Mellon
Network analysis, nonparametric estimation, and high-dimensional statistics.
Cun-Hui Zhang, Distinguished Professor of Statistics, SAS; Ph.D., Columbia
Empirical Bayes; high-dimensional data; functional MRI, network data, semiparametric and nonparametric methods, survival analysis and incomplete data; statistical inference; probability theory
Linjun Zhang, Associate Professor of Statistics, SAS; Ph.D.,
Machine Learning, Deep Learning, High-Dimensional Statistics, Ethical Data Analysis (Data Privacy and Algorithmic Fairness)
Peng Zhang, Assistant Professor of Computer Science, SAS, Ph.D., Georgia Tech
Theoretical computer science, which studies computer science by using mathematical tools.