Special courses

This course provides a survey of, and experience in, applying quantitative and computational techniques in contemporary biomedical research, based on diverse large-scale data. Three major components include: 1) Lectures to introduce biomedical questions, critical datasets, and statistical/machine learning techniques. 2) Real- world case-studies guided by discussions of recent seminal papers of data-driven biomedical research. 3) Group projects to utilize computational methodology to answer open biomedical questions.  The course is taught by Prof. Jianrong Wang in CMSE.

This is an advanced course where students will work side by side with instructors to explore the use of next-generation computer architectures for large scale scientific and engineering applications.  This course is jointly taught by Profs. Andrew Christlieb, Dirk Colbry, and Brian O'Shea in CMSE.

This course provides an overview of computer science topics that are critical to students in computational and data science, including a sample of topics including algorithms, data structures, and computer architectures. The course will also introduce students to software development tools and software engineering techniques that are commonly used in the computational and data science community. This course will use the C programming language (and minimal C++) as a basis, and will include substantial coding assignments.  This course will be taught by Profs. Alexei Bazavov and Tony Gao in CMSE.

This course provides an introduction to mathematical reasoning, including basic logic and the derivation of mathematical proofs. It also provides a review of the areas of linear algebra and differential equations that are used in the CMSE subject exam courses.  This course is recommended for students who wish to prepare more fully for CMSE graduate courses that have a strong applied mathematics component (i.e., CMSE 820, 821, 823, and some special topics courses).  This course will be taught by Profs. Mark Iwen and Jose Perea in CMSE.

The purpose of this course is to introduce the mathematical problems in modern medical and seismic imaging methods. Students will gain experience in modeling various tomography techniques and in formulating imaging problems in mathematical terms. They will be exposed to classical and state-of-the-art algorithms that have been designed to reconstruct the underlying images from observations. Numerical implementation of the algorithms will be discussed as well to complement the theory and give students a better sense of these cutting-edge technologies.  This course will be taught by Profs. Rongrong Wang and Yang Yang in CMSE.

This course offers an introduction to kinetic theory, focusing on classical non-equilibrium many-body theory, the BBGKY hierarchy, the kinetic equations, moment closures, applications of this theory, and numerical methods used to solve these equations.  The course is taught by Michael Murillo in CMSE.

This summer, the Forestry Department is offering an online course for R programming that might be of interest to CMSE undergraduate and graduate students. Please contact Andrew Finley with questions at finleya@msu.edu.

The aim of this course is to introduce the theory of inverse problems and methods for solving them in practice. The student, upon completion of the course, should be able to comprehend the principles underlying ill-posed problems and the imaging of material objects. Particular attention will be paid in the course in designing numerical inversion algorithms.

Course instructor: B. Shanker, bshanker@egr.msu.edu

Date/time: Tuesday/Thursday, 1:00-2:20 p.m. in 004 Urban Plan & Land Arch

Prerequisites: Numerical Linear Algebra

The CMSE Department will be offering a graduate course on Image Processing Techniques taught by faculty member Dirk Colbry. In this course, we intend to develop and explore tools that assist researchers in analyzing their scientific image datasets. To do this we are focusing on the computational representation of images and the types and classes of algorithms that have been developed for science analysis.

This course will cover aspects of modern computational harmonic analysis at the interface of signal processing and data science. A central theme of the course is to find “good” representations of functional data (e.g., time series, images, etc), where the quality of the representation is measured through notions of sparsity, characterization of certain functional classes, and eventually empirical data driven measures.

The aims of this course are to introduce modeling techniques and issues, review successful models, and develop critical thinking about computational models and theories in neuroscience. We will study and experiment with simple models of single cells and small networks. We will then study some models of simple animal behaviors and conclude with more complex models for mammalian cognition. Students will learn and work in MATLAB and will make their own model of a specific dynamic process or behavior for a course project.

Today’s technologies enable neuroscientists to gather data in quantities previously unimagined, and the BRAIN initiative will dramatically expand these capabilities. Many experimental researchers experience a bottleneck when it comes time to analyze their hard-won data. This course is intended to help neuroscience practitioners to meet the challenges posed by the analysis of large functional datasets, and to introduce quantitative students to the wealth of problems to be addressed in big data from neuroscience.

Numerical methods, parameter estimation, and statistical analysis for mathematical models used in engineering. Theory will be illustrated with examples from various engineering disciplines. MATLAB will be taught and used in the class, and is required to be used for homeworks and exams.

This is an introductory course on Linear Algebra with a focus on scientific/engineering applications and solving large problems using computers. This course will be instructed by Faculty Member, Dirk Colbry.