BME 595: Special Topics in BME Courses

Fall 2025


BME 595-F01 ST: Value Creation for Graduate Research
Instructor: George Pins / Len Polizzotto
Credit: 1

This interdisciplinary course will help students develop the skills needed to identify and react to the challenge of creating value in research projects and proposals. They will practice communication skills needed to both describe their research work to external people as well as to those both in and outside of their specific discipline. The skills learned and developed by the students will enable them to elevate the purpose and the impact of the research they propose and conduct.


BME 595-F02 ST: Diagnostic Medical Physics
Instructor: William McCarthy
Credit: 3

Students will be introduced to the fields of diagnostic medical imaging with a focus on the fundamental imaging physics. Basic concepts, including: matter and energy, x-ray production, and photon interactions, will lead to topics in x-ray generation, nuclear magnetic resonance, and sound-wave propagation. The course will then focus on the different diagnostic imaging modalities including X-ray radiography, Computed Tomography, Nuclear Magnetic Resonance, Gamma Scintillation, and ultrasound imaging.


BME 595-F04 ST: Wearable/Mobile Sensors and Systems
Instructor: Ted Clancy
Credit: 3

This course explores the design and application of small-size, low-power wearable sensors, with a focus on medical- and health-related uses. Students will begin by learning about sensor technologies and electronic circuits that transduce common physiologic signals, including the electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), photoplethysmogram (PPG; for pulse oximetry), transcutaneous oxygen measurement, temperature, and inertial measurement units (IMUs).  Key topics in signal processing include online or off-line techniques such as heart rate estimation from ECG/PPG data and EMG-based control of prosthetic devices. Through hands-on labs, students will design, build and program functional sensor systems.
Recommended Background: working knowledge of MATLAB (or Python) and C programming and an undergraduate background in analog circuits and computer engineering. Undergraduate background in digital signal processing and/or wireless signals would be helpful, but not necessary.


BME 595-F05 ST: Engineered Models of Human Disease
Instructor: Catherine Whittington
Credit: 3

The class will focus on how researchers think about and design models of human disease. Primary examples will be in vitro models, but in vivo models will also be discussed. Existing model systems will be introduced and evaluated to assess their utility and limitations for certain diseases. Information from those models, combined with disease-specific elements, will be used to determine how we as engineers and physical scientists can use our toolkit to better “engineer” them. Problems may be studied in the context of current advances in science and technology (research, industry, clinic), clinical case studies, etc. from descriptive, qualitative, and quantitative perspectives. NOTE: The focus of the course is not to delve deep into disease history and/or perform an intense review of current disease-specific research. Classes will be a balance of lecture, discussion, and active learning with a mix of individual and group assignments that will include writing and micro-teaching.
Recommended background: Students are expected to have a basic understanding of cell biology, physiology, and basic engineering principles. Background in genetics, molecular biology, biomaterials, and biochemistry, will be helpful.

BME 595-F06 ST: Mechanobiology
Instructor: Solomon Mensah
Credit: 3

Spring 2026

BME 595-S01 ST: Lab Automation
Instructor: Ross Lagoy
Credit: 3

In this cutting-edge special topics course, students will explore the evolving field of automated laboratory systems at the intersection of science, technology, and innovation. Through a variety of interactive lessons, students will develop critical skills for careers in life sciences, drug discovery, research, and robotics. From high-throughput assay development to fully automated cloud laboratories and Al-driven processes, students will engage with advanced technologies that are transforming scientific research. By the end of the course, students will be prepared to excel in roles across biotech, pharma, start-ups, and research institutions, advancing breakthroughs and shaping the future of medicine.