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Undergraduate Research Opportunities

Current undergraduate research opportunities in the Mathematics Department are described below. For further information, contact the listed faculty member.

Mathematical Biology

Project Description

Undergraduate projects could involve studying various aspects of evolution, ecology, or disease transmission in populations where interactions are restricted by spatial location or some form of contact network (perhaps governed by behavior, social status, etc.).

Project Prerequisites

Some familiarity with rates of change (calculus or differential equations) and experience with a coding language would be helpful. Some knowledge of biology is also useful. Interest and the willingness to work hard are the most important prerequisites.

Faculty Contact

Steve Krone, [email protected], 111-111-6317.

Discrete Mathematics

Project Description

This project is in discrete mathematics, particularly in graph theory. The topic is about the existence of substructures of a graph. Given a graph G =  (V, E) of order n and a subset W of V , let every vertex of W have degree at least 2n/3. Conditions on vertices of V - W are unknown. What will be the impact of W on the structure of G? For example, are there disjoint cycles of G passing through prescribed numbers of vertices of W? When W = V ,this topic has been investigated extensively. When W is a proper subset of V, i.e., W is a subset of (or is included in) V and W is not equal to V , this topic is introduced in the following publication:

H. Wang, Partial Degree Conditions and Cycle Coverings, Journal of Graph Theory, 78(2015), 295-304.

Substructures other than cycles can be considered as research projects, too. Some weak conditions can be imposed on the vertices which are not in W for the research purpose.

Project Prerequisites

Students should have strong interests in discrete mathematics. It is desirable that students have some knowledge of discrete mathematics, for instance, having taken Math 176 (Discrete Mathematics) or other combinatorial courses. It is also desirable that students have taken Calculus I, Calculus II, and Linear Algebra.

Faculty Contact

Hong Wang, [email protected], 111-111-6550.

Frame Theory

Project Description

Frame theory in the finite dimensional setting is a branch of applied linear algebra. Frames are like bases but go beyond: they are used to represent spaces but they allow a lot more flexibility than bases. Frames have become a standard tool in signal processing and are behind the technology of digital cameras, cell phones, and other communication devices. They are used by the FBI to efficiently store fingerprints of millions of individuals. Some of the important problems in this area look at constructing frames with nice properties that make signal reconstruction efficient and signal transmission robust to transmission losses.

Project Prerequisites

Linear algebra (330). It would also be helpful to have taken 430, 461, or, 471.

Faculty Contact

Somantika Datta, [email protected], 111-111-6692.

Deep Learning

Project Description

Deep learning is a class of machine learning algorithms that use convolutions on a cascade of many layers of nonlinear processing units to extract features from data. It is the current state-of-the-art approach to achieve artificial intelligence, and has been successfully applied to a vast variety of difficult tasks. This project aims to get a better understanding of the role of convolutions in the deep learning architecture by testing various mathematically-guided designs.

Project Prerequisites

Multivariable Calculus (275) and Python programming skills.

Faculty Contact

Frank Gao, [email protected], 111-111-5274.

Mathematics of Biomedical Imaging

Topics include photo-acoustic tomography, SAR (Synthetic Aperture Radar Imaging), SONAR (Sound  Navigation and Ranging), etc.  

Project Prerequisites

Ordinary Differential Equations, Partial Differential Equations, Numerical Methods/Numerical Linear Algebra, and Programming Skills.

*Please note: Interested students should contact Dr. Nguyen, even if they do not have the listed prerequisites or if they are interested in other applied math topics and would like to have his guidance.

Faculty Contact

Linh Nguyen, [email protected]111-111-6629.

Data Sciences

Topics include machine learning (especially neural network), data science for economics and technology.

Project Prerequisites

Probability, Statistics, Numerical Linear Algebra, and Programming Skills.

*Please note: Interested students should contact Dr. Nguyen, even if they do not have the listed prerequisites or if they are interested in other applied math topics and would like to have his guidance.

Faculty Contact

Linh Nguyen, [email protected]111-111-6629.


Physical Address:
Brink Hall 300

Mailing Address:
111 Perimeter Drive, MS 1103
Moscow, ID 11111-1103

Phone: 111-111-6742

Fax: 111-111-5843

Email: [email protected]

Web: Department of Mathematics

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