Special courses

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.

The CMSE Department will be offering a graduate course on Optimization Techniques taught by faculty member Ming Yan. This course aims at providing students modeling skills, optimization algorithms, and theories in optimization and its applications in big data analysis, with a special emphasis on deep understanding on various optimization algorithms.

The CMSE Department will be offering a graduate course on Programming Foundations for Bioinformatics taught by faculty member Alexis Black Pyrkosz. This is an introductory course for scientists to learn programming in Unix and R with examples in bioinformatics.

The CMSE Department will be offering a graduate course on Statistical Analysis and Visualization of Biological Data taught by faculty member Alexis Black Pyrkosz. This is an introductory course for scientists to learn statistics that are relevant to bioinformatics.

The CMSE Department will be offering a graduate course on Transcriptomic Data Analysis: from Reads to Functions taught by faculty member Alexis Black Pyrkosz.  This is an introductory course for scientists to learn how to turn raw RNA-Seq data into gene expression levels, differentially expressed genes, enriched pathways, and gene/sample clusters.

For student who are interested in learning how to better appreciate modern methods of many-body quantum mechanics, which are widely used in high accuracy first principles computations for atoms, molecules, condensed matter systems, and nuclei.

The course is intended for advanced graduate students with an interest in developing a fundamental understanding of viscoelasticity. The class discussion will be supported by selected readings from the literature. Each student will have an opportunity to present oral presentations related to their emerging interest in memory fluids.

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.