Graduate Courses

An introduction to machine learning concepts and methods, including supervised and unsupervised learning, dimensionality reduction and visualization, with an emphasis on special considerations in healthcare and biomedical engineering. Students in this course will use Python to analyze real-world biomedical data and create predictive models. Previous programming experience is required.

Gross anatomy, physiology, and selected pathophysiology of the cardiovascular, respiratory, musculoskeletal, nervous, renal, and integumentary systems. Emphasizes understanding the integrated body systems through an engineering lens, with discussion of relevant medical devices. Laboratory experiments will be used to demonstrate key physiologic aspects of the human body. Knowledge of biology and basic physiology helpful but not required.

Materials science of living structures and biomedical devices; biomaterial testing; mechanical behavior and failure mechanisms of biomaterials; medical device application and design; interactions between biomaterials and biological systems; special topics such as tissue engineering. 

Skeletal and muscle anatomy, and muscle mechanics. Theory and application of electromyography. Motion and force measuring techniques with laboratory practice. Inverse dynamics modeling of the human body. Current topics in musculoskeletal biomechanics research.

This course covers techniques used to process digital signals in applications such as audio filtering and speech recognition. Topics include analog-to-digital and digital-to-analog conversions, aliasing, quantization, discrete-time signals and systems, discrete-time Fourier transform, Z-transform, and digital filter design. MATLAB is used to demonstrate concepts and to process real signals. Course includes an advanced project to explore a digital signal processing system.

Applications of electrical engineering in recording and modifying neural activity of the brain. Topicsinclude basics of brain imaging techniques such as electroencephalography (EEG), magneticresonance imaging (MRI), and functional magnetic resonance imaging (fMRI). Introduction to treatmentmethods utilizing electric and magnetic fields to alter brain activity such as repetitive transcranialmagnetic stimulation (rTMS). Knowledge of frequency domain analysis required.

Advanced applications of solid mechanics principles to understand mechanical response of biological tissues to physiological loading. Determination of appropriate mechanical testing methods to simulate physiological loading experienced by biological tissues in situ. Analysis of experimental load and deformation data to determine mechanical properties of biological tissues.

Introduction to human factors and ergonomics. Topics may include: work-related musculoskeletal disorders, hand tool design, anthropometry, workplace design, low back biomechanics. Students will be responsible for a self-directed project. Fee: $50

Goes into depth on concepts and tools necessary to analyze the value of new technology, create new products, and get products to market. Upon completion, students will have the knowledge to put together a commercialization plan for bringing a new technology or product to market. 

Students work in teams to plan and design a device for the biomedical field. Biomedical engineering design process will be covered including problem identification, medical motivation, background research and prototyping. There will be a focus on innovation, translation, and product development.

A continuation of BME 583. This project-driven course includes bench-scale to bedside perspectives regarding how to move discoveries from basic research into clinical and bedside care. Topics include prototyping, validation testing, and professional development. Course includes discussion of ethical issues related to biomedical engineering. Discussions include invited speakers from the outside community. 

BME students are required to secure, complete and report on an applied internship experience. To meet the requirement, the internship must follow the University’s documentation rules for internships and be approved by the course instructor. Students may receive an IP (In Progress) grade until the completion of their internship. Course is graded Pass/No Pass.