Teaching
I teach statistics at every level at WPI, from the first applied statistics course to the graduate course in statistical genetics. My aim is understanding that lasts beyond the exam. For the courses I have redesigned I built topic-based materials, with lecture notes, recorded lectures, R examples and practice problems, and I share them with the colleagues who teach these courses.
Courses
| Course | Title |
|---|---|
| MA 2611 | Applied Statistics I |
| MA 2610 | Applied Statistics for Life Sciences |
| MA 2631 | Probability |
| MA 540 / MA 4631 | Probability and Mathematical Statistics I |
| MA 541 / MA 4632 | Probability and Mathematical Statistics II |
| MA 542 | Regression Analysis |
| MA 546 | Design and Analysis of Experiments |
| BCB 4004 / MA 4603 | Statistical Methods in Genetics and Bioinformatics (undergraduate) |
| BCB 504 / MA 584 | Statistical Methods in Genetics and Bioinformatics (graduate) |
| BCB 510 | BCB Seminar |
Course materials for enrolled students are on Canvas.
Statistical Methods in Genetics and Bioinformatics is a course I created as the foundational statistics course of the Bioinformatics and Computational Biology (BCB) degrees. It was taught as one cross-listed course until 2024, when growing enrollment led to separate undergraduate and graduate versions. The graduate version now runs as a semester-long course with a research-oriented class project, and it integrates AI-assisted learning into the coursework.
Programs
I co-founded WPI’s Bioinformatics and Computational Biology program and helped create its BS, BS/MS, MS and PhD degrees. I also co-created the PhD program in Statistics in the Department of Mathematical Sciences.
Teaching grants
- WPI Summer Sandbox Grant,
- Structured AI Tutoring for Applied Statistics: an AI-assisted practice tool for MA 2611, a course that serves more than 700 students a year. The tool delivers curated problems with adaptive difficulty and AI-generated feedback aligned to the course, through instructor-guided prompts and instructor-curated content rather than unstructured chatbot use.
Student projects
Students work with me on research projects at every level: PhD dissertations, master’s theses and capstone projects, undergraduate Major Qualifying Projects (MQPs) and Interactive Qualifying Projects (IQPs), and independent studies. So far I have advised 5 PhD dissertations, 7 master’s theses, 19 master’s capstone and research projects, 8 MQPs and 1 IQP, and 73 independent-study and project courses. The People page lists the projects.
Typical topics are statistical genetics and bioinformatics; statistical theory and methodology for signal detection, decision-making, prediction and causal inference; computational statistics, including the implementation of methods for large data and the development of R packages; and statistical applications in engineering, experimental design and business. Students are welcome to bring their own ideas that match the group’s interests.
What I expect. Research requires self-motivation, curiosity, patience and effort, and a dedication to excellent work that goes beyond earning the credits. A successful project usually takes 20 hours a week or more.
How a project runs. We meet weekly to discuss progress, challenges and next steps. Students submit weekly reports summarizing their methods and results, with the code organized on GitHub. Each project ends with a final report. For an outstanding project, I help prepare a manuscript for publication.
Undergraduates at WPI can read more about the MQP and the IQP. If you are interested in a project, email me with a short note on your background and what you would like to work on.