Both senior seminar and senior thesis satisfy the Bates W3 writing requirement and highlight mathematical research, presentation, writing, and group collaboration. Senior seminar is a good choice for students wanting to improve all these, with special emphasis on presentation and group collaboration. Senior seminars also involve writing, as well as mathematical research on topics curated by the instructor.

For the 2026-2027 academic year, there will be two senior seminar options: Spatial Statistics and Spatial Data Science, taught by Professor Laurie Baker; and Mathematical Methods in Data Science, taught by Professor Fatou Sanogo.

To ensure the senior seminar is an enriching experience, the math department keeps the class size relatively small. To help the department place students into senior seminar and thesis, each junior math major completes a request form by NOON on the last day of Winter Semester classes of the junior year, that is, by 12:00pm (noon) on Friday, April 17, 2026. Some details:

• The request form seeks background information on the student, the student’s preferences regarding senior seminar and thesis, and the student’s reasoning behind their preferences.
• It is a good idea for juniors to discuss the choice between senior seminar and senior thesis with faculty members before completing the request form.
• The department meets to consider all senior seminar and thesis proposals. The department chair typically notifies students of the results of the meeting during Short Term.
• The course description for the Winter 2027 senior seminars is below.

Spatial Statistics and Spatial Data Science, taught by Professor Laurie Baker

Everything happens somewhere. Spatial statistics is a branch of statistics that focuses on analyzing data with a geographic component, providing essential tools for understanding patterns, dependencies, and processes in space. From modeling disease outbreaks and crime hotspots to estimating air pollution levels at unmeasured locations, spatial statistical methods help answer key scientific and policy-driven questions.

In this seminar, we will explore fundamental spatial statistical concepts, including point processes (e.g., modeling earthquake occurrences or crime hotspots), random fields (e.g., predicting soil contamination levels or temperature variations), and spatial interpolation (e.g., estimating air pollution levels at unmeasured locations). We will emphasize both the mathematical foundations and practical applications of these methods, using real-world datasets to develop proficiency in spatial analysis. 

A key component of this seminar is student leadership in learning. Students will be responsible for leading class discussions, presenting on assigned topics, and guiding peers through critical concepts in spatial analysis. Writing assignments will also incorporate technical documentation using Quarto, Markdown, and LaTeX to develop professional documentation and reproducible research workflows. Through hands-on group projects analyzing real data and writing-intensive assignments, students will develop expertise in accessing and analyzing spatial data, interpreting results, and effectively communicating insights. Coding experience is recommended but not required. There are no prerequisites for the seminar and students who have not taken Math 214 and Math 215 are very welcome.

Mathematical Methods in Data Science, taught by Professor Fatou Sanogo

Mathematical models are increasingly popular to help extract structure, uncertainty, and prediction from complex data. Mathematical data science brings together ideas from probability, statistics, linear algebra, optimization, and machine learning to understand patterns, make predictions, and quantify risk across a wide range of applications, including but not limited to healthcare (predicting disease risk,..) , ecology (modeling animal populations,…), social science (analyzing social networks), climate science (forecasting extreme weather,..), economics (predicting market trends,…), and engineering (detecting equipment failures,…).

In this seminar, we will explore the mathematical methods underneath modern data science such as statistical learning and time-to-event (survival) analysis. Topics may include regression and classification, dimensionality reduction, as well as survival analysis techniques such as machine-learning approaches to risk prediction. 

Students will explore these ideas by leading discussions on research papers or applied case studies. Students will be responsible for identifying, sourcing, and analyzing datasets aligned with their individual or group projects. Students will be responsible for writing reports on their progress using various tools. Prior exposure to calculus and linear algebra is recommended; programming experience is helpful but not required.

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