Research Opportunities

Sports Analytics

Are you interested in graduate school, eager to prepare for a research career, or simply want to explore the cool problems math can solve? Develop research skills and contribute to the Mission of the ACMS Department! Feel free to look through the below opportunities in the ACMS Department and contact Director of Undergraduate Studies, Dr. Alan Huebner, to express your interest and receive guidance on how to find a mentor.

In the near future, an info session will be organized for interested students to engage in this program.

Mentor Office/Contact Info Research Topics/Areas of Interest Suggested Background/Experience
Martina Bukač
201F Crowley Hall
Modeling fluid-structure interaction problems with applications to arterial blood flow Scientific Computing and Numerical Analysis
Guosheng Fu 201D Crowley Hall
Numerical simulation of flow and transport in fractured porous media ACMS 40390: Numerical Analysis, and basic experience with python programming
Jonathan Hauenstein 102F Crowley Hall
Design and application of numerical methods for solving nonlinear equations Linear algebra, numerical analysis, scientific computing
Bei Hu 174A Hurley Hall
Partial Differential Equations from Biology and Mathematical Finance ACMS 20210: Computer Programming, ACMS 40390: Numerical Analysis, ACMS 40730: Mathematical Modeling
Alan Huebner

101F  Crowley Hall

Educational and psychological testing, applied statistical modeling ACMS 30600 or equivalent knowledge in statistical inference, regression modeling, and R computing
Lizhen Lin 203A  Crowley Hall
Complex data analysis, High-Dimensional data analysis, Geometry and Statistics, Network analysis Basic or advanced knowledge of statistics
Daniele Schiavazzi 101G Crowley Hall
Stochastic Modeling, Uncertainty Analysis, Time-Frequency Analysis, 2D-3D Image Processing, Machine Learning ACMS 40760 and basic Python programming. I'm looking for self-motivated, resilient and independent students
Giuseppe Vinci 201A Crowley Hall
Statistical theory, graphical models, topological data analysis, machine learning, computational neuroscience, genomics, astrostatistics, forensic science. Basic or advanced knowledge in probability theory and/or statistical inference (e.g. courses ACMS 30600, ACMS 30550).
Yongtao Zhang 176 Hurley Hall
Numerical methods for solving Partial Differential Equations from Computational Biology and Computational Physics Numerical Analysis, Computer Programming, Partial Differential Equations