Applied Statistics (MS)
Students in WPI’s PhD in statistics program tackle the deeper problems in statistics and apply them to real-world applications in academic settings and in industries as diverse as banking and healthcare.
As the need for skilled statisticians increases, WPI’s PhD in statistics program offers a rigorous plan for students who enjoy the field’s challenges, who want to forge new paths, and who anticipate solving original problems. The program offers advanced coursework and research in statistics while imparting critical-thinking and problem-solving skills.
Students in this interdisciplinary program work closely with accessible faculty and often collaborate with researchers in data science, bioinformatics and computational biology, engineering, and business. Students demonstrate their competency at gathering vast amounts of numerical facts (data), evaluating it, and translating or presenting the findings and results for applications in various fields.
The knowledge gained in this program provides real-world opportunities for careers in data-driven industries. Companies use enormous amounts of data to help develop more targeted products. The healthcare industry has great need for statisticians who can help develop new drug technologies or conduct clinical trials. Students are supported in research that will solve puzzles and patterns in big data.
Curriculum
Students entering the program with a BS must successfully complete 90 semester hours of graduate work (students with an MS must complete 60 semester hours). At least 30 semester hours must be spent in dissertation research. Full-time residency at WPI is required for at least one continuous academic year of graduate work.
- General Courses (credited for students with MS), 30 credits
- Research Preparation Phase, 24-30 credits
- Research-related Courses or Independent Studies, 9-18 credits
- PhD Project, 1-9 credits
- Extra-departmental Studies, 6 credits
- Dissertation Research, at least 30 credits
- General Comprehensive Exam
- PhD project
- Preliminary Examination
- PhD Dissertation
Statistical Courses
MA540 Probability and Mathematical Statistics I
MA541 Probability and Mathematical Statistics II
MA542 Regression Analysis
MA543 Statistical Methods for Data Science
MA546 Design and Analysis of Experiments
MA547 Design and Analysis of Observational and Sampling Studies
MA548 Quality Control
MA549 Analysis of Lifetime Data
MA550 Time Series Analysis
MA552 Distribution-free and Robust Statistical Methods
MA554 Applied Multivariate Analysis
MA556 Applied Bayesian Statistics
MA584 Statistical Methods in Genetics and Bioinformatics
Mathematical Courses
MA502 Linear Algebra
MA503 Lebesgue Measure and Integration
MA510 Numerical Methods
MA514 Numerical Linear Algebra
MA528 Measure Theoretic Probability Theory
You may consider completing approved independent studies work in advanced mathematical statistics, advanced multivariate statistics, advanced survey sampling, advanced linear model, generalized linear model, advanced Bayesian statistics, advanced time series, advanced measure-theoretical probability, or advanced statistical genetics.
Research
Our PhD program is highly competitive, and it offers an excellent foundation for future scholars and leaders in statistics. Through rigorous and comprehensive training and research experiences in statistics and related areas, you’ll be ready for leadership roles in academia, industry, and government. WPI’s program requires you to sharpen your critical-thinking and problem-solving skills to address statistical challenges in data-related researches and applications.
WPI’s PhD students work alongside faculty and other PhD students and are involved in diverse projects, both fundamental and interdisciplinary, in these areas:
- Bayesian statistics, survey sampling, and small-area estimation with applications to health, economic, and agricultural data
- High-dimensional statistical learning for big data, including financial time series and spatial statistics
- Biostatistics, statistical genetics and genomics, and statistics in biomedical and clinical studies
- Order restricted inference and meta-analysis
The strong interdisciplinary nature of our PhD program also provides students with enormous possibilities to interact with researchers in other disciplines, including basic sciences, business, data science, bioinformatics, and biomedical engineering.
Faculty Profiles
Faculty members include the following:
Zheyang Wu
Professor Wu's research interest lies in applying the power of statistical science to promote biomedical researches. In statistical genetics, he is developing novel statistical theory and methodology to analyze genome-wide association (GWA) data and deep (re)sequencing data to hunt new genetic factors for complex human diseases. In epigenetics, he is studying gene expression regulation mechanisms through chromatin interaction, and RNA silencing pathways in the developmental stages of germ-line cells.
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Jian Zou
Professor Zou's research focuses on financial time series and spatial statistics with applications to epidemiology, public health and climate change. His most recent research on statistical theory and methodology addressed a wide range of challenges including high dimensionality, complex dependencies, and space and time variations. His research in high-frequency financial data tackled problems with high dimensionality, which is currently a hot topic in statistics.
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Buddika Peiris
Teaching is an art. It is something you develop within yourself. It needs hard work, time, creativity and dedication. Giving clear and understandable lectures, facilitating group activities, and interacting with students are all important parts of my teaching. My objectives as a statistics educator are: (1) to teach students about statistical tools and how to use them correctly, (2) to expose students to the structure of statistical analysis, and (3) to teach students how to communicate statistical results and concepts to a variety of audiences.
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Balgobin Nandram
Getting Involved
Affiliated faculty in statistics includes the following:
