Director and Associate Director

Director: Minge Xie  (Ph.D. Illinois)

Dr. Xie’s main research interest lies in developing new statistical methodologies and theories for problems stemming from interdisciplinary research. His research interests include statistical applications in Bio-medical Sciences, Social Sciences, Industry, Engineering, and Environmental Sciences.  In the past six years, Dr. Xie has conducted research projects sponsored by grants from the National Science Foundation (NSF),  the National Institute of Health (NIH),  The Department of Veterans Affairs (VA), the Federal Aviation Administration (through RU IE department),  the American Diabetes Association, among others. 

Associate Director: Steven Buyske (Ph.D. Brown; Ph.D. Rutgers)

Dr. Buyske's main research interests are in statistical genetics, biostatistics, psychometrics, and experimental design.  He holds adjunct appointments in the Genetics Department and the Center of Alcohol Studies, and is actively involved in several interdisciplinary research projects with colleagues in the Genetics, Molecular Biology, and Psychology Departments, the Center of Alcohol Studies, and the UMDNJ Departments of Psychiatry, Neurology, and Surgery. Dr. Buyske's research projects are supported in part by grants from the National Institutes of Health as well as the National Alliance for Autism Research.

Contact Information

Telephone: 848-445-2690
FAX: 732-445-3428
E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Or, Write to:

Office of Statistical Consulting 
Department of Statistics
501 Hill Center-Busch Campus
Rutgers, The State University of New Jersey
110 Frelinghuysen Rd
Piscataway, NJ 08854-8019, USA

Faculty Advisory Board

Faculty and Areas of Expertise

  • Pierre Bellec. Associate Professor. Ph.D. ENSAE. High dimensional Statistics, aggregation of estimators, shape constrained problems in Statistics, Probability Theory.
  • Matteo Bonvini. Assistant Professor. Ph.D. Carnegie Mellon University. Teaching and research in Causal inference; nonparametric methods; semiparametric efficiency theory.
  • Steve Buyske. Associate Professor and Co-Undergraduate Director and Co-Undergraduate Data Science Director. Ph.D. Brown; Ph.D. Rutgers. Honors & Awards: School of Arts and Sciences Award for Distinguished Contributions to Undergraduate Education. Teaching and research in statistical genetics, biostatistics, psychometrics, item response, and experimental design.
  • Javier Cabrera. Professor.  Ph.D. Princeton. Teaching and research in biostatistics, data mining methodology for clinical trial data, functional genomics data, DNA & protein array data, and DNA barcode data; statistical computing, graphics, and machine vision. Fulbright Scholar.
  • Rong Chen. Distinguished Professor & Director, FSRM Program. Ph.D. Carnegie Mellon. Teaching and research in nonlinear-nonparametric time series analysis, Monte Carlo methods and statistical applications in bioinformatics, finance and engineering. Current co-editor, JBES. Elected Fellow: American Statistical Association, Institute of Mathematical Statistics. Elected member: International Statistical Institute.
  • Yaqing Chen. Assistant Professor. Ph.D. University of California, Davis. Teaching and research in 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. Ph.D. U. Chicago. Honors & Awards: Chancellor's Scholar (2018-2020).  Teaching and research in combinatorial stochastic processes, probabilistic symmetry, discrete probability theory, applied probability, combinatorial statistical inference.
  • Tirthankar Dasgupta. Professor and Co-Graduate Director. Ph.D. Georgia Institute of Technology. Teaching and research in 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. Assistant Professor. Ph.D. Harvard. Teaching and research in 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.
  • Zijian Guo. Associate professor.  Ph.D. University of Pennsylvania (Statistics). Teaching and research in High-dimensional statistics, Nonparametric Statistics, Causal Inference, Econometrics and Applications to Health and Genomics Data.
  • Qiyang Han. Associate Professor. Ph.D. University of Washington. Teaching and research in 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 nonparametric, and high dimensional statistics.
  • Donald Hoover. Professor. Ph.D. Stanford. Teaching and research in clinical trials, epidemiology, longitudinal methods, group randomization and multiple comparisons. Core member: Institute for Health, Health Care Policy and Aging Research.
  • Ying Hung.   Professor.  Ph.D. Georgia Institute of Technology. Teaching and research in design of experiment, computer experiment, cell adhesion. Recipient of the 2014 Tweedie New Research Award.
  • Koulik Khamaru. Assistant Professor. PhD University of California, Berkeley
  • John Kolassa. Distinguished Professor. Ph.D. U. Chicago. Teaching and research in asymptotics, saddle point approximation, biostatistics, Former Associate Editor, Journal of American Statistical Association. Elected member:  International Statistical Institute. Elected Fellow: American Statistical Association, Institute of Mathematical Statistics.
  • Regina Y. Liu. Distinguished Professor. Ph.D. Columbia. Teaching and research in data depth, fusion learning, resampling, nonparametric and robust statistics, aviation risk analysis. Honors & Awards: Recipient of 2011 Stieltjes Professor, The Netherlands; Elected Fellow: American Statistical Association, Institute of Mathematical Statistics; IMS Medallion Lecturer; President, Institute of Mathematical Statistics • Former Editor: Journal of American Statistical Association, J. of Multivariate Analysis. Former Associate Editor: Annals of Statistics, Journal of American Statistical Association.
  • Gemma Moran. Assistant Professor. PhD University of Pennsylvania. Teaching and research in developing flexible Bayesian models for analyzing high-dimensional data, developing identifiable and interpretable deep generative models (especially variational autoencoders); improved tools for Bayesian model criticism. 
  • Nicole Pashley. Assistant Professor. PhD Harvard University. Teaching and researching in Causal inference, experimental design and analysis using randomization-based framework.
  • Harold B. Sackrowitz.  Distinguished Professor & Co-Undergraduate Director. Ph.D. Columbia. Teaching and research in statistical inference and decision theory, multiple endpoint procedures, order restricted inference. Elected Fellow: American Statistical Association, Institute of Mathematical Statistics.
  • Michael Stein. Distinguished Professor. Ph.D. Stanford University. Teaching and research in Spatial and spatial-temporal statistics, extremes, environmental statistics, statistical climatology.
  • Zhiqiang Tan. Distinguished Professor. Ph.D. U.  Chicago. Teaching and research in nonparametric and semiparametric statistics, causal inference, missing data problems, survey sampling, Monte Carlo integration. Associate Editor, Biometrics, Annals of Statistics.  NSF Career Award.
  • David E. Tyler. Distinguished Professor. Ph.D. Princeton. Teaching and research in multivariate analysis, robust statistics, directional data, computer vision and time series. Associate Editor, J. of the Royal Statistical Society-B, J. Statistical Planning and Inference.  Elected Fellow: Institute of Mathematical Statistics.
  • Guanyang Wang. Assistant Professor.  Ph.D. Stanford University. Teaching and research in Markov chain Monte Carlo, Machine Learning, Probability, Quantum Computing. 
  • Sijian Wang. Professor and Co-Director of FSRM and MSDS Programs. Ph.D. University of Michigan. Teaching and research in 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 and Co-Graduate Director. Ph.D. U.  Chicago. Teaching and research in nonstationary and nonlinear time series, high dimensional analysis, algebraic statistics, and random matrix theory.
  • Minge Xie. Distinguished Professor & Director, Office of Statistical Consulting. Ph.D. U. Illinois at Urbana-Champaign. Teaching and research in statistical models, inference, asymptotic, and interdisciplinary research. Elected Fellow: American Statistical Association. Elected Member: International Statistical Institute.  
  • Min Xu. Assistant Professor. Ph.D. Carnegie Mellon University (Machine Learning). Teaching and research in Network analysis, nonparametric estimation, and high-dimensional statistics.
  • Cun-Hui Zhang. Distinguished Professor and Co-Director of FSRM and MSDS programs. Ph.D. Columbia University. Teaching and research in high-dimensional data, empirical Bayes, functional MRI, network data, semiparametric and nonparametric methods, survival analysis, statistical inference and probability theory. IMS Medallion Lecturer. Elected Fellow: American Statistical Association, Institute of Mathematical Statistics.
  • Linjun Zhang. Assistant Professor.  Ph.D. of Statistics, The Wharton School, University of Pennsylvania (2019). Teaching and research in Machine Learning, Deep Learning, High-Dimensional Statistics, Ethical Data Analysis (Data Privacy and Algorithmic Fairness).