Research Opportunities

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 mbukac@nd.edu	Modeling fluid-structure interaction problems with applications to arterial blood flow	Scientific Computing and Numerical Analysis
Guosheng Fu	201D Crowley Hall gfu@nd.edu	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 hauenstein@nd.edu	Design and application of numerical methods for solving nonlinear equations	Linear algebra, numerical analysis, scientific computing
Bei Hu	174A Hurley Hall b1hu@nd.edu	Partial Differential Equations from Biology	ACMS 20210: Computer Programming, ACMS 40390: Numerical Analysis, ACMS 40730: Mathematical Modeling
Alan Huebner	101F  Crowley Hall Alan.Huebner.10@nd.edu	Educational and psychological testing, applied statistical modeling	ACMS 30600 or equivalent knowledge in statistical inference, regression modeling, and R computing
Daniele Schiavazzi	101G Crowley Hall dschiavazzi@nd.edu	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 gvinci@nd.edu	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).
Adam Volk	203A Crowley Hall avolk2@nd.edu	Combinatorics, graph theory, and graph algorithms (theory and applications)	Scientific computing and linear algebra
Victoria Woodard	152C Hurley Hall vweber1@nd.edu	Applying data science and statistical analysis for a wide range of fields. Previous topics have included political science, public health, and computer science.	ACMS 30600 and programming experience.
Yongtao Zhang	176 Hurley Hall yzhang10@nd.edu	Numerical methods for solving Partial Differential Equations from Computational Biology and Computational Physics	Numerical Analysis, Computer Programming, Partial Differential Equations