Courses

A quantitative, model-oriented investigation of the cardiovascular system. The course will for focus on cardiac electrophysiology, mechanics, and hemodynamics using multi-scale physiology-driven models.

This course introduces the fundamental programming concepts and techniques in Java and is intended for all who plan to use computer programming in their studies and careers. Topics covered include control structures, arrays, functions, recursion, dynamic memory allocation, simple data structures, files, and structured program design. Elements of object-oriented design ...

Students will be introduced to the history of Leadership Theory from the "Great Man" theory of born leaders to Transformational Leadership theory of non-positional learned leadership. Transformational Leadership theory postulates that leadership can be learned and enhanced. The course will explore the knowledge base and skills necessary to be an ...

Analysis and simulation of nonlinear behavior in biological systems: bifurcations (cell-fate decision), limit cycles (cell-cycle, neuronal excitations), chaos, and maps. Matlab will be used to simulate these systems and motivate nonlinear analytic tools and stability analysis.

The goal of artificial intelligence (AI) is to tackle complex real-world problems with rigorous mathematical tools. In this course, you will learn the foundational principles and practice implementing various AI systems. Specific topics include machine learning, search, Markov decision processes, game playing, constraint satisfaction, graphical models, and logic.

Study of three classical formal models of computation--finite state machines, context-free grammars, and Turing machines--and the corresponding families of formal languages. Power and limitations of each model. Parsing. Non-determinism. The Halting Problem and undecidability. The classes P and NP, and NP-completeness.

This combined lecture and laboratory course will delve into the workings of the cell and the interactions between cells. The emphasis in this course is on quantitative analysis of reactions between molecules, including receptor-ligand and antigen-antibody specificity, enzyme catalysis, genetic information, protein processing and secretion, cell physiology and cell functions ...

This course introduces students to modeling and analysis of linear biological systems. Topics include viscoelastic materials, pharmacokinetics, reaction-diffusion-convection equation with applications to molecular transport in tissues. The course also introduces students to the Matlab programming language, which allows them to implement the models discussed in the classroom.

This course provides an introduction to data science and machine learning for biomedical engineering. The lectures cover topics in biomedical data processing (convolution, denoising, filtering, edge detection, template matching), biomedical data reduction (feature extraction, principal component analysis), and biomedical data regression, classification (including deep learning), and clustering.

The BME Undergraduate Design Team program supports more than 15 teams of undergraduates each year as they develop solutions to some of the most challenging and important healthcare needs in the world. Over the past few years, these design teams have completed over 250 medical device projects, upon which the ...

Working in teams with upperclassmen this course (1) introduces biomedical engineering freshmen to an orderly method for analyzing and modeling biological systems, (2) introduces engineering principles to solve design problems that are biological, physiological, and/or medical, and (3) considers the ethical and professional responsibility in developing biomedical engineering solutions to ...

In this hands-on course, students will build an imaging device and learn to apply signals and systems knowledge in imaging system characterization, optimization, and post-processing. The course includes an introduction to two-dimensional signal processing techniques, basic imaging principles, imaging systems modeling, and optimization methods.

The Geometry of Euclidean Space, Differentiation Space, Higher-Order Derivatives: Maxima and Minima, Vector-Valued Functions, Double and Triple Integrals, The Change of Variables Formula and Applications of Integration, Path and Line Integrals, The Integral Theorems of Vector Analysis

This half-semester flipped-content laboratory course will consist of modules that provide students with valuable hands-on experience in cell and tissue engineering. Modules contain experiments including the basics of cell culture techniques, gene transfection, metabolic glycoengineering, and cell encapsulation. Students will collect and analyze their own experimental data, write-up results in ...

This course focuses on computer-based techniques, systems, and applications exploiting quantitative information from medical images and sensors to assist clinicians in all phases of treatment, from diagnosis to preoperative planning, execution, and follow-up. It emphasizes the relationship between problem definition, computer-based technology, and clinical application and includes a number of ...

CIS II (601.456/496/656/356) is a projects course for graduate students and upper-level undergrads, in which students work in teams of 1-3 on semester-long projects broadly related to computer-integrated interventions, AI in medicine, medical image analysis, or related topics. In addition to material covered in lectures/seminars by the instructor and other ...

This course covers how to use databases in applications, first principles on how to scale for large data sets and how to design good data systems. A few key topics: — Introduction to relational data model, relational database engines, and SQL. — How to scale systems for large data sets ...

This course covers the analysis, design, and implementation of data structures including arrays, stacks, queues, linked lists, binary trees, heaps, balanced trees, and graphs. Other topics include sorting, hashing, Java generics, unit testing, and benchmarking. Course work involves both written homework and Java programming assignments.

The CS300 seminar is offered to incoming first-year students in the Autumn quarter. The seminar gives CS faculty the opportunity to speak for 40 minutes about their research. The idea is to allow the new CS PhD students the chance to learn about the professor's areas of research before permanently ...

First Order Differential Equations, Second Order Linear Equations, Higher Order Linear Equations, Systems of First Order Linear Equations, Nonlinear Differential Equations And Stability, Numerical methods, The Laplace Transform, Series Solutions of Second Order Linear Equations