Department of Statistics and Operations Research (GRAD)
The department offers the master of science (M.S.) in statistics, analytics, and data science (STANDS) and doctor of philosophy (Ph.D.) in statistics and operations research (STOR).
The M.S. program is intended for students who wish to pursue careers in data science and analytics or as a preparation for continuing on to further graduate studies in related areas. This program focuses on training students in advanced quantitative thinking due to the increasing demand for skills in data-driven decision making in the modern world. It is possible for motivated students to complete the requirements in three semesters, though the typical duration is four. For students graduating with an undergraduate STAN degree, there is also an option of five-year M.S.-STANDS program.
The Ph.D. degree in STOR is designed for students planning a career in teaching or research. The educational and research profile of the STOR Ph.D. program is focused on the core disciplines of statistics, optimization, probability, and stochastic modeling. These disciplines have driven, and continue to drive, progress in data science and machine learning, as well as business and medical analytics. The STOR Department is one of the few in the U.S. that brings together experts in each of these disciplines under one academic roof. STOR offers a rigorous but flexible interdisciplinary Ph.D. program within which students can benefit from the strength and diverse expertise of the department’s core faculty, while also having the opportunity to interact with domain scientists and researchers working in other fields. STOR Ph.D. students complete foundational coursework in the four core disciplines before undertaking more specialized coursework and directed dissertation research. Dissertation research is completed under the supervision of one or more faculty advisers. Research topics may lie within a single core discipline, or may span several core disciplines. Many research topics involve interdisciplinary research, with active collaboration with faculty and students at UNC including Environmental Sciences, Biology, the Lineberger Comprehensive Cancer Center, Computer Science, Biostatistics, Economics, and the Carolina Center for Genome Sciences, as well as industry in the Research Triangle Park and across the U.S. The breadth and depth of the STOR Ph.D. program prepares graduates for a wide variety of careers, ranging from academia to industry, and from the public to the private sector. Recent graduates have taken jobs in mathematics, statistics, IE, OR departments, and high-tech, biotech companies, and government agencies, etc. The Ph.D. degree requires at least three (but usually five) years of full-time graduate study, predicated upon substantial undergraduate mathematical preparation. Research is a central component in the work of doctoral candidates. Research training consists of required core coursework as well as electives that are designed to bring students up to date in their research field and intensive one-on-one work with a faculty member on a specific dissertation topic. Doctoral students who want to pursue academic careers are provided with ample opportunities to teach introductory undergraduate courses, and they are given extensive training to develop their instructional skills. Doctoral students may also participate in paid internships with local industrial employers to gain experience in a business environment. Their professional skills are further enhanced by work on real-world projects with clients in the department's consulting courses. Several courses provide opportunities for students to give technical presentations and refine their communication skills.
Further information on the graduate degree programs can be obtained from the department's website.
Admissions and Financial Aid
Admission to the department is highly competitive, and preference is given to applicants who have solid technical preparation. Although the department welcomes promising students from all disciplines, entering students must have a substantial mathematical background and applicants must satisfy the entrance requirements of The Graduate School. A student admitted with a deficiency in any area must make up for it at the beginning of her or his graduate work. If the deficiency is not severe, this can be accomplished without interrupting the normal program.
Students can indicate on this application form whether they intend to pursue the M.S. degree program or a Ph.D.
Funding basics including links for financial aid are provided by The Graduate School.
Most of our Ph.D. students receive some form of financial support, such as Graduate School fellowships, departmental assistantships, research assistantships, or internships. Departmental assistantships involve grading or teaching an undergraduate course. Some of our students are supported as research assistants by faculty. Our supported students receive a tuition and fee waiver, and health insurance for the duration of their studies.
Courses
Numbered 400-999:
Degree Requirements
M.S. Program
The M.S. degree requires 30 credit hours of coursework and the completion of a master's project. Students can choose from a variety of courses, including a limited number from outside the department. Upon approval of The Graduate School, at most six credit hours may be transferred from another accredited institution or from within UNC–Chapel Hill for courses taken before admission to the M.S. program.
Ph.D. Program
The Ph.D. degree requires at least 45 semester hours of graduate coursework and the successful completion of a doctoral dissertation. Detailed information about specific courses, elective courses and allowable courses outside of the STOR department as well as potential course plans based on interests of accepted students are provided on the graduate admissions section of the program's website.
Statistics Courses for Students From Other Disciplines
A number of STOR courses in probability and statistics are of potential interest to students in other disciplines. At the advanced undergraduate/beginning graduate level, STOR 455 and STOR 556, provide an introduction to applied statistics, including regression, analysis of variance, and time series. STOR 435, STOR 535, and STOR 555 provide introductions to probability theory and mathematical statistics, respectively, at a postcalculus level while courses like STOR 538 and STOR 572 describe applications of the discipline to fundamental areas such as sports analytics and healthcare analytics.
The three graduate course sequences–(STOR 664, STOR 665), (STOR 654, STOR 655), and (STOR 634, STOR 635)–provide comprehensive introductions to modern applied statistics, theoretical statistics, and probability theory, respectively, at a more mathematical level. In each case it is possible to take only the first course in the sequence. Concerning mathematical prerequisites, STOR 664 and STOR 665 require a background in linear algebra and matrix theory, while the remaining courses require a solid background in real analysis.
Following the faculty member's name is a section number that students should use when registering for independent studies, reading, research, and thesis and dissertation courses with that particular professor.
Professors
Nilay Argon, Stochastic Models, Manufacturing and Healthcare Applications, Discrete Event Simulation
Shankar Bhamidi, Probability, Random Networks, MCMC, Probabilistic Combinatorial Optimization
Amarjit Budhiraja, Probability, Stochastic Analysis, Large Deviations
Jan Hannig, (Kenan Distinguished Professor,) Statistics, Fiducial Inference, Stochastic Processes
James Stephen Marron, (Amos Hawley Distinguished Professor), Object-Oriented Data Analysis, Visualization, Smoothing
Andrew Nobel, (Paul Ziff Distinguished Professor,) Machine Learning, Data Mining, Computational Genomics
Marianna Olvera-Cravioto, Applied Probability, Random Graphs, Heavy-Tailed Large Deviations, Weighted Branching Processes, Stochastic Simulation
Gabor Pataki, Convex Programming, Integer Programming
Vladas Pipiras, Time Series and Spatial Modeling, Extreme Value Theory, Streaming and Sampling Algorithms
Richard L. Smith, (Mark L. Reed Distinguished Professor,) Extreme Value Theory, Environmental Statistics, Spatial Statistics
Kai Zhang, Mathematical Statistics, High Dimensional Inference, Inference After Variable Selection, Large Deviation, Quantum Computing
Serhan Ziya, Stochastic Modeling, Healthcare Operations, Service Operations, Queueing Design and Control, Revenue Management
Associate Professors
Sayan Banerjee, Stochastic Analysis, Probabilistic Couplings, Interacting Particle Systems
Nicolas Fraiman, Random Structures, Combinatorial Statistics, Randomized Algorithms
Chuanshu Ji, Financial Econometrics, Computational Materials Science, Monte Carlo Methods
Quoc Tran-Dinh, Numerical Optimization, Theory and Algorithms for Convex Optimization and Nonconvex Continuous Optimization
Zhengwu Zhang, Brain Connectomics, Medical Imaging, Machine Learning, Bayesian Statistics, Shape and Functional Data Analysis
Assistant Professors
Guanting Chen, Sequential Decision Making and Learning Algorithms, Simulation and Applied Probability, Optimization and Applications in Operations Management
Xiangying Huang, Interacting Particle Systems, Spatial Stochastic Models, Algorithms on Random Graphs, Dynamic Random Networks
Daniel Kessler, Statistical Analysis of Networks, Post-Selective Inference, High-Dimensional Statistics, Human Neuroimaging, Computational and Cognitive Neuroscience, High Performance Computing
Yao Li, Machine Learning, Deep Learning, Adversarial Examples, Recommender System
Mo Liu, Data-Driven Decision-Making, Statistical Learning and Active Learning for Operations Management
Patrick Lopatto, Probability Theory and Applications, Causal Inference, Random Matrix Theory
Ali Mohammad Nezhad, Optimization, Computational Complexity, Real Algebraic Geometry, and Applied Topology
Michael O'Neill, Continuous Nonlinear Optimization, Stochastic Optimization Algorithms, Machine Learning and Data Science Applications
Ben Seeger, Stochastic Analysis, Mean Field Games, Interacting Agent Models
Fan Yao, Social Aspects of AI, Multi-Agent Modeling, Algorithmic Game Theory
Chudi Zhong, Interpretable Machine Learning, Human-Model Interaction
Hang Zhou, Statistical Modeling and Inference for Object Data, Functional Data with Complex Structure, Learning Theory, Dynamic Systems
Teaching Associate Professor
Oluremi Abayomi, Statistics, Data Science
Mario Giacomazzo, Statistics
Jeffrey McLean, Statistics
Teaching Assistant Professors
Teressa Bergland,
William Lassiter, Operations Research
Professor of the Practice
Glenn Sabin
Joint Professors
Joseph Ibrahim, (Alumni Distinguished Professor of Biostatistics,) Bayesian Methods, Missing Data, Cancer Research
Michael Kosorok, (Biostatistics,) Biostatistics, Empirical Processes, Semiparametric Inference, Machine Learning, Personalized Medicine, Clinical Trials, Dynamic Treatment Regimes
Jayashankar Swaminathan, (Benjamin Cone Research Professor, Kenan–Flagler Business School,) Supply Chain, Stochastic Models
Professors Emeriti
George S. Fishman
Douglas G. Kelly
J. Scott Provan
David S. Rubin
Gordon D. Simons
Shaler Stidham Jr.
Jon W. Tolle
Department of Statistics and Operations Research
