The Mathematics of Data Science (Math-DS) Group consists of Kyungduk Ko, Michal Kopera, Jodi Mead, Megan Null, Michael Perlmutter, Grady Wright, and Fan Zhang. As an undergraduate, you can work with members of Math-DS group in the Mathematics of Data Science emphasis described in detail below.
Many members of the Math-DS group are also active in graduate education. This includes the Computing Ph.D. program (primarily focused on the emphases in Data Science, Artificial Intelligence, and Computational Math. Science & Engineering), the MS in Mathematics (primarily focused on the emphases in Applied Mathematics and Statistics, and the MS in Data Science.
Seminar
Many members of the Math-DS group are active participants in the Scientific comPUting and Data Science (SPUDS) Seminar, an informal forum for faculty and students, both within the math department and in other related fields, to present their work.
Mathematical Sciences Major – Emphasis in the Mathematics of Data Science
Data science is an intrinsically interdisciplinary field which combines techniques from more traditional subjects such as statistics, computer science, and applied mathematics in order to analyze data, make predictions, and extract insights. Knowledge of data science is increasingly vital in a wide variety of industries including healthcare, e-commerce, sports, finance, energy, transportation, media, retail, manufacturing, telecommunications, government, defense, and many others.
The Math-DS emphasis is new emphasis within the Mathematics Major. In addition to taking all courses required for the mathematics major, students engage in an interdisciplinary curriculum including classes on Python-programming from the computer science and a course on Philosophy in a Datafied World from the philosophy department. (These courses can be used to fulfill other requirements and thus do not increase the total number of courses you will need to take.)
Frequently Asked Questions
How is the Math-DS emphasis different from the other emphases?
Many students are curious how the data science emphasis differs from other emphases within the math major, particular Computational and Applied Mathematics and Statistics. In short, the data science emphasis requires students to take course in both statistics and applied mathematics, combining aspects of both the other emphases. It also differs from these other emphases by requiring coursework in computer science and philosophy. A more detailed comparison of these emphases can be found in this document.
Can I do more than one emphasis?
Yes! Completing multiple emphases would give you an extremely well rounded background which is highly valuable. In addition to natural overlap in course work with Computational and Applied Mathematics and Statistics, other combinations are also may also great idea. For instance, combining the Math-DS emphasis with an emphasis in Theoretical Math may be an excellent choice for students who are interested in emerging methods which combine data science with Topology, Algebra, and Geometry. Combining Math-DS with the Math Education emphasis may be an interesting option for future math teachers who wish to incorporate data-science examples into their courses.
What are the emphasis requirements?
To complete the Mathematics of Data Science emphasis you must:
- Take all courses required for Mathematics Major.
- Take Math 365 – Introduction to Computational Mathematics and Math 475 – Statistical Learning.
- Take PHIL 123 – Philosophy in a Datafied World, CS 133 – Foundations of Data Science, and CS 233 – Essentials of Data Science. Please note that CS 133/233 can be used to fulfill your sequence requirement (in place of CS 121/221) and that PHIL 123 can be used to fulfill your requirement in the Foundations of Humanities.
- Take three elective courses from MATH 403, MATH 414, MATH 426, MATH 427, MATH 436, MATH 462, MATH 471, and MATH 472. Of these three courses:
- At least one must be from MATH 403, MATH 414, MATH 426, MATH 427, and MATH 436, and
- At least one must be from MATH 462, MATH 471, and MATH 472.
Faculty interests
Kyungduk Ko is an Associate Professor whose research focuses on the theory and practice of long memory processes and on the development of wavelet-based statistical models and their application. Early work on long memory processes was on the parameter estimation and change point detection of ARFIMA processes. Contributions on statistical modeling with long memory processes include partial linear regression models, ANOVA, and linear regression models with long memory errors. His research is mainly based on wavelet transforms and Bayesian inference with application to financial time series, climate time series and functional magnetic resonance imaging (fMRI) data.
Michal Kopera is an Associate Professor who is interested in computational and applied mathematics, high-performance scientific computing, computational fluid dynamics, adaptive mesh refinement, and scientific software development, with a growing focus on deep reinforcement learning methods for guiding adaptive grids. Michal is working on developing ocean models using modern numerical methods (spectral elements, discontinuous Galerkin). An important aspect of his work is the ability of a model to represent complex geometries, and dynamically adapt the mesh to an evolving solution, including through data-driven and learning-based approaches.
Jodi Mead is a Professor who conducts research centers on advancing the mathematical foundations and computational methodologies for data assimilation, inverse methods, and uncertainty quantification. She develops algorithms that bridge theory and real-world application, particularly for systems where data and models must be integrated to improve predictive capability. Her work has produced new techniques for regularization parameter estimation, model-error covariance characterization, and joint inversion of multiple data types. These advances have been applied to diverse environmental and geophysical challenges, including wildfire smoke transport and subsurface imaging.
Megan Null is a Teaching Assistant Professor who specializes in teaching statistics including MATH 153 and MATH 254. She has also conducted research in statistical genetics/genomics including a focus on simulating rare variant genetic data.
Michael Perlmutter is an Assistant Professor whose primary area of research is Geometric Deep Learning, i.e., deep learning for graph- and manifold-structured data. This includes both (a) work on the geometric scattering transform, a predesigned, wavelet-based model of neural networks, and (b) work constructing high-performing networks for signed and/or directed graphs. Recently, he has become increasingly interested in using these methods in biomedical applications such as AI-aided drug discovery, analyzing metabolic networks, and predicting patient outcomes from single-cell data.
Grady Wright is a Professor whose research interests are in high-order methods for partial differential equations, approximation theory, low rank methods, scientific computing, and numerical software development. He works on problems in biology (biofluids and biomechanics), geophysics (geophysical fluid flows), and astronomy (cosmic microwave background). He develops methods based on radial basis functions, (compact) finite-differences, and spectral methods for these problems. A common theme of his work is complex geometries, such as spheres and more general surfaces.
Fan Zhang is an Assistant Professor whose research centers on advancing the statistical foundations and computational methodologies for the design and analysis of experiments, Bayesian statistical modeling, and uncertainty quantification. She develops frameworks that bridge theory and real-world application, particularly for complex systems requiring efficient data exploration and surrogate modeling. Her recent work explores Bayesian variable selection in Gaussian process models and decision analysis for multi-fidelity systems. She applies these statistical frameworks to practical problems in computer experiments and manufacturing optimization.