Department of Biostatistics (GRAD)
The Department of Biostatistics is recognized as a worldwide leader in research and practice. Members of the faculty are interested both in the development of statistical methodology and application of statistics in applied research. The research strengths include: development of new statistical methods to address pressing issues in medicine and public health sciences; design of innovative clinical trials that allow faster evaluation of new therapeutic agents; collaborative work focused upon important public health concerns, including infectious diseases, cancer, cardiovascular disease, obesity and drinking water safety; and utilization of strong quantitative skills to improve the health of human beings around the globe.
The mission of the Department of Biostatistics is to forge dramatic advances in health science research that benefit human health in North Carolina, the U.S., and globally through the development of profound and paradigm-shifting innovations in biostatistical methodology and the thoughtful implementation of biostatistical practice to solve public health problems.
For more information, please reference the Academic Information Manual on the department's website.
Master of Science (M.S.)
The master of science (M.S.) degree in the Department of Biostatistics provides students with research-oriented training in the theory and methodology of biostatistics and its application to solving problems in the health sciences.
Doctor of Philosophy (Ph.D.)
The doctor of philosophy (Ph.D.) degree in the Department of Biostatistics provides advanced, research-oriented training in theory and methodology of biostatistics to prepare individuals for careers in academia, government, and industry.
Courses
Numbered 400-999:
Public Health, Master's Program (M.P.H.) — Public Health Data Science Concentration
The Public Health Data Science concentration, one of the first applied data science programs situated within a school of public health, gives students the skills and knowledge to employ cutting-edge data science tools and respond to pressing public health issues with effective solutions. Data science draws upon multiple disciplines, combining the statistical skills to manipulate data and make inferences, the mathematical skills to model phenomena and make predictions, and the computer science skills to manage and analyze large data sets. Steeped in the public health context, our program offers a unique focus on leveraging the foundational statistical, mathematical, and computer science elements of data science to generate useful information from data sources relevant to public health.
Course Requirements
Requirements for the M.P.H. degree in the Public Health Data Science concentration
| Code | Title | Hours |
|---|---|---|
| M.P.H. Integrated Core | ||
| SPHG 711 | Data Analysis for Public Health | 2 |
| SPHG 712 | Methods and Measures for Public Health Practice | 2 |
| SPHG 713 | Systems Approaches to Understanding Public Health Issues | 2 |
| SPHG 701 | Leading from the Inside-Out | 2 |
| SPHG 721 | Public Health Solutions: Systems, Policy and Advocacy | 2 |
| SPHG 722 | Developing, Implementing, and Evaluating Public Health Solutions (MPH Comprehensive Exam administered in class) | 4 |
| M.P.H. Practicum | ||
| SPHG 703 | MPH Pre-Practicum Assignments | 0.5 |
| SPHG 707 | MPH Post-Practicum Assignments | 0.5 |
| M.P.H. Concentration | ||
| BIOS 512 | Data Science Basics | 3 |
| BIOS 650 | Basic Elements of Probability and Statistical Inference I | 3 |
| BIOS 635 | Introduction to Machine Learning | 3 |
| BIOS 645 | Principles of Experimental Analysis | 3 |
| EPID 710 | Fundamentals of Epidemiology | 3 |
| M.P.H. Electives | ||
| Electives (Graduate-level courses, 400+ level at Gillings, 500+ level at UNC) | 9 | |
| M.P.H. Culminating Experience | ||
| BIOS 992 | Master's (Non-Thesis) | 3 |
| Minimum Hours | 42 | |
Admissions
Please visit Applying to the Gillings School first for details and information. Application to the residential M.P.H. is a 2-step process. Please apply separately to (1) SOPHAS and (2) UNC–Chapel Hill (via the Graduate School application link that will be sent after completing the SOPHAS application). Visit the Graduate School Web site for more details. If you are interested in the online M.P.H., please visit the MPH@UNC website and fill out an inquiry form.
Milestones
- Master's Committee
- Master's Written Examination/Approved Substitute (Comprehensive Exam)
- Thesis Substitute (Culminating Experience)
- Residence Credit
- Exit Survey
- Master's Professional Work Experience (Practicum)
Master of Science in Biostatistics (M.S.)
The Master of Science (MS) program is designed to provide research-oriented training in the theory and methodology of biostatistics and its applications to the solution of problems in the health sciences.
Course Requirements
| Code | Title | Hours |
|---|---|---|
| Public Health Foundation Courses | ||
| SPHG 600 | Introduction to Public Health 1 | 3 |
| EPID 600 | Principles of Epidemiology for Public Health | 3 |
| or EPID 710 | Fundamentals of Epidemiology | |
| Core Courses | ||
| BIOS 511 | Introduction to Statistical Computing and Data Management | 4 |
| BIOS 660 | Probability and Statistical Inference I | 3 |
| BIOS 661 | Probability and Statistical Inference II | 3 |
| BIOS 662 | Intermediate Statistical Methods | 4 |
| BIOS 663 | Intermediate Linear Models | 4 |
| BIOS 667 | Applied Longitudinal Data Analysis | 3 |
| BIOS 680 | Introductory Survivorship Analysis | 3 |
| BIOS 691 | Field Observations in Biostatistics | 1 |
| BIOS 841 | Principles of Statistical Collaboration and Leadership | 3 |
| BIOS 843 | Seminar in Biostatistics (two semesters, 2 credit hours) 2 | 2 |
| Electives 3,4,5 | ||
| Six hours of course work that can include BIOS 635, 664, 665, and 668 or any course higher than 668 but not including 680 in Biostatistics. | 6 | |
| Thesis/Substitute or Dissertation Course | ||
| BIOS 992 | Master's (Non-Thesis) | 3 |
| Minimum Hours | 45 | |
- 1
Students with a prior public health degree are not required to take SPHG 600; exemptions are available for those with non-public health degrees from accredited SPHs. Students should discuss with their Academic Coordinator.
- 2
BIOS 843 Seminar must be taken two semesters for two credit hours after comprehensive exams.
- 3
Six hours of course work that can include BIOS 635, 664, 665, and 668 or any course higher than 668 but not including 680 in Biostatistics, or equivalent in the Department of Statistics and Operations Research (STOR) at UNC, or in the Department of Statistics at North Carolina State University (NCSU); these hours are considered individually and must be approved by the DGS.
- 4
Students interested in substituting a graduate level course (600 level or higher) outside of the Gillings School of Global Public Health should submit a request to the Academic Coordinator for review by the DGS for consideration.
- 5
700-level courses as approved by DGS would also count. Please refer to your student specialist. Please note that BIOS 990, BIOS 992, and BIOS 994 do not count towards the electives requirement.
| Code | Title | Hours |
|---|---|---|
| Biostatistics Elective Course Options | ||
| BIOS 635 | Introduction to Machine Learning | 3 |
| BIOS 664 | Sample Survey Methodology | 4 |
| BIOS 668 | Design of Public Health Studies | 3 |
| BIOS 665 | Analysis of Categorical Data | 3 |
| BIOS 672 | Topics in Real Analysis, Introduction to Measure Theory | 1 |
| BIOS 673 | Intermediate Statistical Inference | 1 |
| BIOS 669 | Working with Data in a Public Health Research Setting | 3 |
Milestones
The following list of milestones (non-course degree requirements) must be completed; view this list of standard milestone definitions for more information.
- Master's Committee
- Master's Written Exam / Approved Substitute
- Thesis Substitute
- Residence Credit
- Exit Survey
- Master's Written Exam 2
Doctor of Philosophy in Biostatistics (Ph.D.)
The doctor of philosophy (Ph.D.) degree in the Department of Biostatistics provides advanced, research-oriented training in theory and methodology of biostatistics to prepare individuals for careers in academia, government, and industry.
Course Requirements
| Code | Title | Hours |
|---|---|---|
| Public Health Foundation Courses | ||
| SPHG 600 | Foundations of Public Health 1 | 3 |
| EPID 600 | Principles of Epidemiology for Public Health | 3 |
| or EPID 710 | Fundamentals of Epidemiology | |
| Core Courses | ||
| BIOS 611 | Introduction to Data Science | 4 |
| BIOS 660 | Probability and Statistical Inference I | 3 |
| BIOS 661 | Probability and Statistical Inference II | 3 |
| BIOS 662 | Intermediate Statistical Methods | 4 |
| BIOS 663 | Intermediate Linear Models | 4 |
| BIOS 672 | Topics in Real Analysis, Introduction to Measure Theory | 1 |
| BIOS 673 | Intermediate Statistical Inference | 1 |
| BIOS 735 | Statistical Computing - Basic Principles and Applications | 4 |
| BIOS 760 | Advanced Probability and Statistical Inference I | 4 |
| BIOS 761 | Advanced Probability and Statistical Inference II | 4 |
| BIOS 762 | Theory and Applications of Linear and Generalized Linear Models | 4 |
| BIOS 841 | Principles of Statistical Collaboration and Leadership | 3 |
| BIOS 843 | Seminar in Biostatistics 2 | 4 |
| Electives | ||
| 700-level Biostatistics or (Mathematical) Statistics course from the list below, or approval of the DGS | 9 | |
| Thesis/Substitute or Dissertation Course | ||
| BIOS 994 | Doctoral Research and Dissertation | 6 |
| Minimum Hours | 64 | |
- 1
Students with a prior public health degree are not required to take SPHG 600; exemptions are available for those with non-public health degrees from accredited SPHs. Students should discuss with their Academic Coordinator.
- 2
Four hours of BIOS 843 Seminar taken individually as 1 credit hour.
| Code | Title | Hours |
|---|---|---|
| Biostatistics Elective Course Options | ||
| BIOS 740 | Specialized Methods in Health Statistics | 3-4 |
| BIOS 752 | Design and Analysis of Clinical Trials | 3 |
| BIOS 764 | Advanced Survey Sampling Methods | 3 |
| BIOS 765 | Models and Methodology in Categorical Data | 3 |
| BIOS 767 | Longitudinal Data Analysis | 4 |
| BIOS 772 | Statistical Analysis of MRI Images | 3 |
| BIOS 773 | Statistical Analysis with Missing Data | 3 |
| BIOS 774 | Advanced Machine Learning | 3 |
| BIOS 782 | Statistical Methods in Genetic Association Studies | 3 |
| BIOS 784 | Introduction to Computational Biology | 3 |
| BIOS 785 | Statistical Methods for Gene Expression Analysis | 3 |
| BIOS 775 | Statistical Methods in Diagnostic Medicine | 3 |
| BIOS 776 | Causal Inference in Biomedical Research | 3 |
| BIOS 777 | Precision Medicine and Machine Learning | 3 |
| BIOS 779 | Bayesian Statistics | 4 |
| BIOS 780 | Theory and Methods for Survival Analysis | 3 |
| BIOS 781 | Statistical Methods in Human Genetics | 4 |
| STOR 701 | Statistics and Operations Research Colloquium | 1 |
| STOR 712 | Optimization for Machine Learning and Data Science | 3 |
| STOR 713 | Mathematical Programming II | 3 |
| STOR 722 | Integer Programming | 3 |
| STOR 734 | Stochastic Processes | 3 |
| STOR 743 | Reinforcement Learning and Markov Decision Processes | 3 |
| STOR 754 | Time Series and Multivariate Analysis | 3 |
| STOR 757 | Bayesian Statistics and Generalized Linear Models | 3 |
| STOR 767 | Advanced Statistical Machine Learning | 3 |
Milestones
The following list of milestones (non-course degree requirements) must be completed; view this list of standard milestone definitions for more information.
- Doctoral Committee
- Doctoral Oral Comprehensive Exam
- Doctoral Written Exam
- Prospectus Oral Exam
- Advanced to Candidacy
- Dissertation Defense
- Doctoral Dissertation Approved/Format Accepted
- Residence Credit
- Exit Survey
- Doctoral Teaching Experience
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
Kevin Anstrom (70), Clinical Trials, Statistical Consulting, Causal Inference, Data Safety Monitoring, Pragmatic Clinical Trials, and Coordinating Center Operations
Jianwen Cai (93), Survival Analysis and Regression Models, Clinical Trials, Analysis of Correlated Responses
David J. Couper (77), Epidemiological Methods, Longitudinal Data, Data Quality
Michael Hudgens (42), Nonparametric Estimation, Group Testing, Causal Inference, Infectious Diseases
Joseph G. Ibrahim (11), Bayesian Inference, Missing Data Problems, Bayesian Survival Analysis, Generalized Linear Models, Genomics
Anastasia Ivanova (83), Clinical Trials Design, Sequential Design of Binary Response Experiments, Statistical Methodology in Biostatistics
Gary G. Koch (14), Categorical Data Analysis, Nonparametric Methods
Michael R. Kosorok (88), Biostatistics, Bioinformatics, Empirical Processes, Statistical Learning, Data Mining, Semiparametric Inference, Monte Carlo Methods, Survival Analysis, Clinical Trials, Personalized Medicine, Cancer, Cystic Fibrosis
Yun Li (59), (Joint with the Department of Genetics), Statistical Genetics
Danyu Lin (31), Survival Analysis, Semiparametric Statistical Methods, Clinical Trials
Feng-Chang Lin (71), Survival Analysis, Generalized Linear Models, Longitudinal Analysis, Hearth Disease and Stroke, Infectious Disease, Neuroscience
Yufeng Liu (73), (Joint with the Department of Statistics and Operations Research), Statistical Machine Learning and Data Mining, High-Dimensional Data Analysis, Nonparametric Statistics and Functional Estimation, Bioinformatics, Design and Analysis of Experiments
James Stephen Marron (82), (Joint with the Department of Statistics and Operations Research), High Dimension Low Sample Size (HDLSS), Data and/or Data, Exotic Data Types such as Manifold and Tree-Structural Data
Jane Monaco (43), Survival Analysis, Correlated Failure Time Data
Andrew Nobel, (Joint with the Department of Statistics and Operations Research), Data Mining, Statistical Data of Genomic Data, Machine Learning
John S. Preisser Jr. (89), Categorical Data, Longitudinal Data Analysis
Todd A. Schwartz (13), Categorical Data, Clinical Trials
Richard Smith, (Joint with the Department of Statistics and Operations Research), Spatial Statistics, Time Series Analysis, Extreme Value Theory, Bayesian Statistics
Daniela T. Sotres-Alvarez (74), Linear Mixed Models, Latent Variable Models, Dietary and Physical Activity Patterns
Xianming Tan (50), Finite Mixture Models, Design of Clinical Studies, Variable Selection for Zero-Inflated Models, Non-Parametric Regression
Haibo Zhou (40), Missing/Auxiliary Data, Survival Analysis, Human Fertility
Hongtu Zhu (48), Neuroimaging Statistics, Structural Equation Models, Statistical Computing, Diagnostic Methods
Fei Zou (4), Statistical Genetics
Associate Professors
Jamie B. Crandell (64), (Joint with the School of Nursing,) Bayesian Methods, Longitudinal Analysis and Measurement Error Modeling
Tanya P. Garcia (67), Survival Analysis, Semiparametric Theory, Longitudinal Data Analysis
Annie Green Howard (75), Cardiovascular Disease, Global Health
Quefeng Li (81), High Dimensional Data Analysis, Integrative Analysis of Omics Data, Robust Statistics, Factor Models
Michael I. Love (39), (Joint with the Department of Genetics,) Statistical Modeling of Genetics Data, High-Throughput Sequencing, RNA Sequencing (RNA-seq), Empirical Bayes Methods
Naim Rashid (79), Cancer, Genomics, High Throughput Sequencing, High Dimensional Data Analysis, Variable Selection
Di Wu (51), (Joint with the School of Dentistry,) Statistical Bioinformatics and Biostatistics for Preprocess and Integration of High-Dimensional Biomedical Data
Baiming Zou (97), Robust Modeling of Data with Complex Structures, Machine Learning Methods for Large Scale Electronic Health Record Data Analysis
Assistant Professors
Didong Li (80), Geometric Data Analysis, Information Geometry, Nonparametric Bayes, Spatial Statistics
Xihao Li (16), Statistical Genetics and Genomics, Integrative Analysis of WGS/WES and Multi-Omics Data, Functional Genomics and Annotations, Data Integration and Meta-Analysis, Multivariate Analysis, Machine Learning
Yusha Liu (54), Cancer, Single-Cell Modeling, Multi-Omics Data Integration, Bayesian Inference, Functional Data Analysis, and Quantile Regression
Ivana Malenica, Casual Inference, Machine Learning, Non-/Semiparametric Inference
Kara McCormack (85), Statistical Pedagogy, Classroom Accessibility and Inclusivity
Lina Montoya, Causal Inference, Precision Health/Policy, (Optimal) Dynamic Treatment Regimes, Sequential Multiple Assignment Randomized Trials, Semiparametric Efficient Estimation and Adaptive Designs
Bryce Rowland (46), Precision Medicine, Clinical Trial Design and Execution, Applied Biostatistics
David Zhang, Methodology: Computer Vision, Language Models, Generative AI; Applications: Spatial Omics, Computational Pathology, Medical Imaging
Beibo Zhao, Subgroup Analysis in Clinical Trials and Observational Studies, Survey Sampling
Instructors
Kinsey Helton
Jeff Laux
Vincent Toups (17)
Adjunct Professors
Haoda Fu
Eric Laber
Sean Simpson
Wei Sun
William Valdar
Clarice Weinberg
Xiaojing Zheng
Donglin Zeng
Richard Zink
Adjunct Associate Professors
Matthew Psioda
Shanshan Zhao
Adjunct Assistant Professors
Marcella H. Boynton
Luiz Carvalho
Can Chen
Nikki L.B. Freeman
Haolin Li
Vanessa Miller
Charles Pepe-Ranney
Tarek Zikry
Zhengwu Zhang
Professors Emeriti
Robert Agans
Shrikant I. Bangdiwala
Lloyd E. Chambless
Clarence E. Davis
James E. Grizzle
Ronald W. Helms
William D. Kalsbeek
Lawrence L. Kupper
Lisa M. LaVange
Keith E. Muller
Bahjat Qaqish
Paul W. Stewart
Michael J. Symons
Kinh N. Truong
Department of Biostatistics
