Lecture Time & Place

2020 Spring semester

T/Th 9:30-10:45am

408 SMI (Service Memorial Institute is a building connected to the Medical Science Center)

Course Webpage: www.stat.wisc.edu/~mchung/teaching/768

Instructor

Moo K. Chung, PhD mkchung@wisc.edu

Associate Professor of Biostatistics and Medical Informatics

Waisman Laboratory for Brain Imaging and Behavior

University of Wiconsin-Madison

Office: Medical Science Center 4725, 1300 University Ave

Office Hours: T/TR 10:45-12:30am. To set up separate appointments, please email the instructor.

Prerequisite

The course is designed for graduate students, postdocs and researchers who wish to learn quantitative mathematical, statistical and computational techniques in processing and analyzing medical images. However, basic understanding of linear algebra, calculus and statistics will be useful to fully understand lectures. The course material is applicable to a wide variety of high dimensional nonstandard data and imaging problems beyond medical images. Senior undergraduate students may take the course after discussion with the instructor.

Lectures

Introduction to Course

MATLAB Programming

Introduction to MATLAB, Imaging data types

Heterogeneous data

Trees, graphs, networks, manifold valued data

Geometric Data Analysis (GDA)

Riemannian and spectral geometry, manifold learning, regression on manifolds

Topological Data Analysis (TDA)

persistent homology, algebraic topology, computational topology

Hilbert Space Methods

Object oriented data analysis (OODA), functional data analysis, differential equations

Big Data Computation

Large-scale computation, scalable computation, online algorithms

Additional Learning Materials

There is no required textbook. Lecture slides and additional notes will be provided before each lecture in https://sites.google.com/view/bmi768. Parts of lectures will be based on the following three text books written by the instructor:

Computational Neuroanatomy: The Methods, 2012 World Scientific Publishing

Statistical and Computational Methods in Brain Image Analsis, 2013 CRC Press

Brain Network Analysis, 2019 Cambridge University Press

Course Evaluation

• Students are required to set their own goals and follow them. The course workload estimator Rice University: Center for Teaching Excellence will help you estimate the amount of work you need to put for the class.
• Course evaluation is based on following options. Students are required submit the final research project report and do the final oral presentation at the end of the semester. The project can be 1) literature review, 2) sequence of homework problems, 3) computer programming project, or 4) a research project. For programming and research project, you can either use your own data for the project (after consultation with the instructor) or the instructor will provide the state-of-art data.
• Sample class projects in previous image analysis course taught by the instructor can be found here. Each semester, the focus of the course change. So the sample project reports may not reflect the current course topics.