This is a lecture course providing an overview of the broad field of human movement biomechanics and neural control. This develops engineering skillsets for rigid body kinematics and dynamics, solves quantitative problems associated with the study of human movement, and a literature overview. This includes computer modeling of how the central nervous systems controls musculoskeletal movement, including muscle mechanics, gross body dynamics, control theory, and motor learning.

Prerequisites: BIOS 110, MATH 180, CS 109 (or equivalent)

The objective of this course is to provide general aspects of dental science and research for undergraduate students. The course will include (i) introduction to dentistry (ii) basics of clinical procedures (iii) basics of restorative dentistry/implants (iv) basics of endodontics (v) biomaterials used in dentistry (vi) biomechanics aspects in dentistry/TMJ disorder (vii) clinical concerns/challenges (viii) surgery room animations (ix) new drug delivery system. Students will be able to understand the basics of dental science. They will also learn the advancements in dentistry, such as new biomaterials, biosensors, and diagnostic tools. This course is suitable for students who are interested in pre-dental or research related to dental/oral science. It is open to undergraduate and graduate students in biomedical engineering, biological science, applied health science, mechanical engineering, or materials engineering.

Over the past decade, wearables and nearables technologies have revolutionized and quantified multiple aspects of our lives including sports, fitness, entertainment, medicine, and health monitoring. In this course, we will learn how to design and fabricate wearables and nearables devices. The course provides the opportunity to work in cross-disciplinary groups and develop the skill set needed to be part of this emerging field. This new, exciting class combines various sensors, software platforms, and Bluetooth technology to create wearable and nearable devices. An open-ended project module will allow you the option to collaborate with a selected industry partner on a real-world problem, potentially leading to a future internship. This course is open to a total of 32 undergraduate and graduate students in biomedical engineering, computer science, or electrical and computer engineering. Students must have a background in circuit design via ECE 115 and ECE 210.

• View videos of actual projects from the course

Magnetic Resonance Imaging (MRI) is one of the most widely used imaging modalities in hospitals and clinics for medical diagnosis due to its non-invasive, non-ionizing, and multi-planar capability to visualize human body. This course offers in-depth discussions covering all core areas, including: basic principles, image weighting and contrast, spin and gradient echo pulse sequences, spatial encoding, k-space, protocol optimization, artefacts, instrumentation, and MRI safety. We will use the leading MRI reference book MRI in practice as our textbook. This course will prepare students, in the future, for advanced researches on MR imaging and for MRI related occupation, for example, the advanced level examination for MRI offered by the American Registry for Radiologic Technologists (ARRT) in the USA.

This course will teach introductory computer vision with a focus on biomedical applications. The course will go through image processing methods, feature extraction, machine learning, and introductory deep learning for computer vision (i.e. convolutional neural networks for classification). The class will focus on 3-4 problem sets that will be a mix of theory questions and programming assignments, all of which will be in Python. A major portion of the class grade will also be a group project, where students will work in groups of 3-4 and apply the learned methods to a novel research problem on real world data.

Manual palpation represents one of the most traditional diagnostic methods. The sensitivity of manual palpation is rooted in the correlation of pathological changes with the mechanical behavior of biological tissue. While medical doctors can only qualitatively assess the stiffness of tissue by touch near to the body’s surface, some elastographic techniques are capable of determining the mechanical properties deep inside the organs. Therefore, Elastography has diagnostic potential for a variety of diseases, such as hepatic fibrosis and neurodegenerative brain diseases. This course is highly interdisciplinary and covers problems related to continuum mechanics, biorheology, MRI and image processing. We will review the theoretical foundations of elastography, and students will receive an overview of elastographic techniques. The emphasis of this course will be on Magnetic Resonance Elastography (MRE). By the end of this course, the students will understand how mechanical waves are measured using MRI and they will be proficient in processing MRI data for the calculation of viscoelastic material parameters, such as the tissue stiffness.

Theory of acoustics in fluids and solids. Measurement methods. Medical diagnostic and imaging applications: cardiovascular and pulmonary acoustics, ultrasound, dynamic elastography. Students must have graduate standing in an engineering program or permission of the instructor. Must have had differential equations (e.g. Math 220) with grade of B or better.

This course is designed to teach students the fundamentals of key organ systems in the human body from a quantitative perspective such that certain aspects of the systems can be understood at a more intuitive level and be used to predict physiological outcomes for needs in healthcare. Students will also be introduced to cutting edge examples in biomedical engineering research and development in which physiological principles are being used in academic and industrial settings to design model systems for different applications, such as drug development and regenerative medicine (i.e. cell-based therapies for patients suffering from organ failure). The knowledge gained in this course will be invaluable to make students well-rounded biomedical engineers in their current degree program but also beyond in the job market. Both graduate students and undergraduate students are welcome to take this course since the instructor will provide adequate guidance and materials to ensure that students with different backgrounds can succeed in the course.

Interdisciplinary course with first year IMED medical students to refine concept solutions for unmet clinical needs. This class is aimed at biomedical engineering graduate students with an interest to lead in the development of a prototype medical device in collaboration with medical students. IMED students have spent the fall validating clinical problems and have developed several potential concept solutions. We are looking for biomedical engineering students who want to sharpen their interpersonal communication skills, contribute technical expertise to design a prototype using the product development process. Please note the course is an evening, synchronous virtual class on Tuesdays. Class meets approximately every two weeks 5-7 pm. Limited enrollment. Please reach out to Dr. Kotche for additional information and permission to register.

This course will teach the current state of the art for computer vision with a focus on biomedical applications. The course will go through advanced topics in deep learning, such as segmentation, detection, image generation, multi-task learning, feature learning, black box visualizations, and much more. The class will function much like a journal club with students taking turns presenting various papers written in recent years. The class assumes a strong base understanding of computer vision and modern-day deep learning. The main focus of the class, outside of the lectures and assigned readings, will be a final project where students work in groups of three or four to apply the learned methods to a novel research problem on real-world data.

• Course checklist: MS in Biomedical Engineering
• Course checklist: MS in Bioinformatics (thesis track)
• Course checklist: MS in Bioinformatics (non-thesis track)

• Course checklist: PhD in Biomedical Engineering
• Course checklist: PhD in Bioinformatics