Undergraduate Research

Independent research conducted under the guidance of a faculty member.

Introduction to Combinatorial Topology

Special topics course on Combinatorial Topology, offered in Spring 2022 by Marissa Loving.

High-dimensional probability

The goal of this PhD level graduate course is to provide a rigorous introduction to the methods of high-dimensional probability.

Science Based Data Science

The lectures will focus on an introduction of modern data science techniques and the foundational mathematical concepts in linear algebra, probability, and basic optimization related with these techniques. Sufficient case studies with real-world data sets will be provided to illustrate how to use the learned techniques and how to choose an appropriate model.

College Algebra

Study of the properties of algebraic, exponential, and logarithmic functions as needed for pre-calculus and calculus.

Statistical Theory

This course is an introduction to theoretical statistics for students with a background in probability. A mathematical formalism for inference on experimental data will be developed.

Probability Theory

This course is a mathematical introduction to probability theory, covering random variables, moments, multivariate distributions, law of large numbers, central limit theorem, and large deviations.

A Second Course on Linear Algebra

This course will cover important topics in linear algebra not usually discussed in a first-semester course, featuring a mixture of theory and applications.

Probability and Statistics with Applications

Introduction to probability, probability distributions, point estimation, confidence intervals, hypothesis testing, linear regression and analysis of variance.

Introduction to Discrete Mathematics

Mathematical logic and proof, mathematical induction, counting methods, recurrence relations, algorithms and complexity, graph theory and graph algorithms.


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