Courses

Multiscale Structural Mechanics

This course covers fundamentals of micromechanics, structural mechanics needed for design and analysis of composite structures capturing microstructural details including fiber/matrix and other constituent materials. This course assumes an introductory background in elasticity and finite element method, and aims to provide students a unified framework for multiscale structural mechanics. This course emphasizes concepts of mechanics through formulating and solving typical problems of anisotropic, heterogeneous structures, and helps foster an in-depth understanding of the subject. Students not only gain knowledge of the fundamental principles needed for multiscale simulation but also gain an integrated and consistent understanding of multiscale structural mechanics based on Continuum Mechanics. Fall 2019 Syllabus

Biostatistics

This course focuses on fundamental principles of multivariate statistical analyses in biostatistics, including multiple linear regression, multiple logistic regression, analysis of variance, and basic epidemiology concepts. The fundamental theories are applied to analyze various biomedical applications ranging from laboratory data to large-scale epidemiological data. In particular, this course focuses on multivariate statistical analyses, which involve more than one variable and take into account several variables on the responses of interest. This course focuses on fundamental principles of multivariate statistical analyses in biostatistics, including multiple linear regression, multiple logistic regression, analysis of variance, and basic epidemiology concepts. The fundamental theories are applied to analyze various biomedical applications ranging from laboratory data to large-scale epidemiological data. In particular, this course focuses on multivariate statistical analyses, which involve more than one variable and take into account several variables on the responses of interest. In addition, although statistical learning and machine learning are different, both learning approaches play a key role in inference and prediction. This course compares statistical learning and deep learning in the context of biostatistics.

Biomedical Signal Processing

This is a biomedical "data-science" course covering the application of signal processing and stochastic methods to biomedical signals and systems. A "hands-on" approach is taken throughout the course (see section on required software). While an orientation to biomedical data is key to this course, the tools and concepts covered here will provide foundational skills that are useful in many domains. Topics include: overview of biomedical signals; Fourier transforms review and filter design, linear-algebraic view of filtering for artifact removal and noise suppression (e.g., frequency filtering, regression, noise-cancellation, PCA, ICA); statistical inference on signals and images; estimation theory with application to inverse imaging and system identification; spectra, spectrograms and wavelet analyses; pattern classification and diagnostic decisions (machine learning approaches and workflow). This course is distinct from other classic offerings in ECE/MA/STAT in at least three ways: relevant theory in signal processing and statistical methods is covered as needed, but a major focus is on implementation/application of the fundamental techniques to real-world biomedical signals. Statistical methods that are typically taught with a "univariate" perspective are expanded ot topologically organized high-dimensional data such as time-series and images, and done so motivated by the needs in biomedical applications (e.g., electrophysiology, neuroimaging). This course uses practical applications to integrate probabilistic methods with classic linear-algebraic tools (such as Fourier transforms). These foundational areas are often introduced in separate courses, but are powerful when brought together.

Biosensors: Fundamentals and Applications

An introduction to the field of biosensors and an in-depth and quantitative view of device design and performance analysis. An overview of the current state of the art to enable continuation into advanced biosensor work and design. Topics emphasize biomedical, bioprocessing, environmental, food safety, and biosecurity applications.

Introduction to Clinical Medicine

The information and intellectual approach offered will help students recognize needs for engineering solutions to current challenges in medicine.

Preclinical and Clinical Study Design

The practice of Biomedical Engineering concerns itself with the design, development, and testing of medical devices that will be commercialized to improve or sustain life. Medical device companies, and the engineers they employ, have an ethical and legal responsibility to robustly examine the safety and performance of these devices through preclinical and clinical testing. This course covers the responsible conduct of preclinical and clinical study research necessary for obtaining marketing approval, with a focus on the US FDA requirements, and using a risk-based approach to ensuring safety and effectiveness of medical devices. Topics will include non-clinical benchtop testing, evaluation of device-tissue interactions and how they may be studied with pre-clinical animal models to predict safety and performance, statistical considerations for study design, and ethics related to responsible conduct of pre-clinical and clinical research.

Regulatory Issues Surrounding Approval of Biomedical Devices

Medical devices are developed, manufactured, and distributed in a highly regulated environment. This course primarily concerns the processes for obtaining FDA marketing approval or clearance for biomedical devices. Prior to marketing a medical device, governmental approval or clearance is required depending on the type of device and the risk associated with the device. This course is part of a three-course series dealing with various aspects of regulatory science of medical devices. Regulatory processes for class II and III devices, including combination devices, are covered with specific focus on 501(k) and PMA requirements; a section on Emergency Use Authorization (EUA) has also been added. Approval requirements in the EU, Japan, China, and other regions will also be considered. Throughout the course, emphasis will be placed on regulatory science, regulatory strategy and principles of interacting with regulatory agencies.

Quality Systems for Regulatory Compliance

Course runs Mod 2 & 3: June 10 - July 30. Medical devices are developed and manufactured in a highly regulated environment. This course will provide a basic introduction, overview, and systematic study of the intent and impact of the major federal laws and regulations governing the development, manufacturing, distribution, and marketing of medical devices. Focus is on understanding the critical elements of quality systems and quality compliance from a risk analysis perspective. Instruction in regulatory science of quality systems and compliance is provided by academics, FDA, and industry representatives with expertise in their fields. This course is part of a three-course series which will be introduced in class. This course will be delivered entirely on-line through learning modules and video conferenced Q&A.

Ethical Engineering of Medical Technologies

The medical device industry may be on the brink of a crisis. While innovations in lifesaving new technologies are transforming medical practice, the rapid pace at which these developments are emerging and the intense pressures of the competitive industry is challenging the ethical training of the engineers involved. In addition, the regulatory environment for medical device development has been changing dramatically, leaving companies with more questions than answers on how to best practice safe and effective medical device development. These changes are creating opportunities for ethical problems to arise--and they have. Recent documentaries, such as The Bleeding Edge and Bleed Out highlights some of the concerns over the state of the industry. In this course we will examine many of these ethical and regulatory concerns from multiple frameworks and perspectives including industry, government, and society. We will practice ethical analysis and develop empathic and decision-making skills designed to prepare engineers to deal productively and ethically with these issues in their professional practice. Guest speakers will include thought leaders from clinical medicine, engineering innovation, and the healthcare products industry who will offer their professional insights. The final project of the course will be a paper analyzing the ethical development of an emerging medical product. This course is designed for graduate students and upper-level students in all engineering disciplines.

Fundamentals of MEMS

Key topics in micro-electro-mechanical systems (MEMS) and biological micro-integrated systems; properties of materials for MEMS; microelectronic process modules for design and fabrication.

Medical Imaging Diagnostic Technologies

This gateway course will provide an introduction to the physics, technologies, and biological considerations associated with modern imaging and diagnostic tools. Specific modalities to be examined will include x-ray, nuclear imaging, ultrasound, MRI, and microscopy techniques such as phase contrast, DIC, confocal microscopy, two-photon microscopy as well as concept of adaptive optics, light-sheet microscopy, 4Pi microscopy, and modern super-resolution microscopy techniques (PALM/STORM, STED and IM). The course also covers principles of optics, contrast generation (including genetically encoded probes and physiological indicators), image formation, detection, and analysis.

Neural Mechanisms of Health and Disease

Major topics include: 1) Single neuron physiology and plasticity 2) Information processing by sensory and motor circuits 3) Modern techniques and devices in neuroscience 4) Neuropathologies, such as epilepsy, aging, and others.

Lighting and Daylighting Design of Buildings

Lighting accounts for at least 30% of the energy consumption in commercial buildings. This course focuses on the design of illumination systems in buildings (electric and natural lighting) in order to achieve energy efficiency and visual comfort. The first part of the course includes analytical lighting calculation techniques, visual perception, radiative transfer, lamp characteristics, electric lighting system design and control for calculation of required indoor illuminance levels. The second part of the course covers daylighting (natural lighting) systems, including state-of-the-art daylighting prediction models as well as design and control of such devices and advanced metrics. The course also has a lab section, in which the students learn how to work with lighting and daylighting tools; and a term project, to build their own computational transient lighting models in open source programming languages, in order to design illumination systems and predict electricity consumption and potential energy savings.

Computer Applications in Construction

Building information modeling (BIM) and advanced computational applications are revolutionizing the construction industry. In this class, students will learn the fundamentals of BIM, its applications in construction, e.g., 4D, 5D, and nD modeling, and the recent trends of computing such as artificial intelligence, machine learning, and natural language processing in the context of e-Construction. A number of carefully crafted hands-on activities targeting at construction problems will help students to not only learn the fundamentals, but also master their applications in construction. Students are expected to be able to create information systems to solve a problem following a systematic approach of problem definition, problem analysis, identification of possible solutions, solution selection, implementation and evaluation. This course may be broken into three 1-credit courses.

Coastal Engineering

This course provides students with an introduction to the discipline of coastal engineering, with a strong focus on coastal processes (waves, water levels, and sediment transport) as well as applied design principles for shoreline protection measures and coastal structures.

Computational Watershed Hydrology

Students in this class will learn how to access and process commonly used geospatial and temporal data such as the digital elevation model (DEM), land use, soil, streamflow and precipitation using geographic information systems (GIS). After processing, students will learn how to use these data to create hydrologic and hydraulic models such as HEC-HMS, HEC-RAS and SWAT. Students will learn how to interpret model results and present information to convey the role of climate and human factors on watershed hydrology. After completing this course, students should be able to perform hydrologic and hydraulic analysis or modeling in research and professional practice to address issues related to water movement and availability in natural settings.

Physico/Chemical Processes in Environmental Engineering

Advanced Structural Mechanics

Design of Prestressed Concrete Structures

Structural Dynamics