Biomedical Engineering MS - Curriculum

This course is a graduate-level introduction to the biodesign process used for innovating medical technologies. Student teams will apply a needs-based assessment strategy to identify opportunities in a biomedical related field such as assistive technologies and rehabilitation engineering. Incorporating CAD will culminate in a virtual medical device prototype. Concepts of intellectual property, regulatory considerations, and reimbursement and business models will be introduced. (This course is restricted to Graduate students.) Lecture 3 (Fall).

This course will expose student to the basic properties of data collected from biological systems and issues involved in the statistical analysis of such data. Specifically, this course will review the motivations and rationale behind conventional regression models, issues that arise in applying these methods to biological data, and specific extensions of these methods required to obtain meaningful results. Specific examples of these approaches and their application will be given at different levels of biology. The analysis of such problems will require the use of advanced regression techniques directed at resolving the partial confounding that is typical of living (closed loop regulated) systems, applied under statistical software packages (e.g., spreadsheets, graphing, Matlab, SPSS, Simca). (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lab 3 (Biannual).

This course presents an in-depth study of the primary concepts of experimental design. Its applied approach uses theoretical tools acquired in other mathematics and statistics courses. Emphasis is placed on the role of replication and randomization in experimentation. Numerous designs and design strategies are reviewed and implications on data analysis are discussed. Topics include: consideration of type 1 and type 2 errors in experimentation, sample size determination, completely randomized designs, randomized complete block designs, blocking and confounding in experiments, Latin square and Graeco Latin square designs, general factorial designs, the 2k factorial design system, the 3k factorial design system, fractional factorial designs, Taguchi experimentation. (Prerequisites: ISEE-325 or STAT-257 or MATH-252 or MCEE-205 or STAT-205 or equivalent course or students in ISEE-MS, ENGMGT-MS, or MIE-PHD programs.) Lecture 3 (Spring).

This course is an introduction to the probabilistic models and statistical techniques used in the analysis of biological and medical data. Topics include univariate and multivariate summary techniques, one and two sample parametric and nonparametric inference, censoring, one and two way analysis of variance, and multiple and logistic regression analysis. (This class is restricted to graduate students in COS, KGCOE, GCCIS, CHST or CLA.) Lecture 3 (Spring).

Statistical tools for modern data analysis can be used across a range of industries to help you guide organizational, societal and scientific advances. This course is designed to provide an introduction to the tools and techniques to accomplish this. Topics covered will include continuous and discrete distributions, descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one-way ANOVA and Chi-square tests. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall).

How to design and analyze experiments, with an emphasis on applications in engineering and the physical sciences. Topics include the role of statistics in scientific experimentation; general principles of design, including randomization, replication, and blocking; replicated and unreplicated two-level factorial designs; two-level fractional-factorial designs; response surface designs. Lecture 3 (Fall, Spring).

This course provides students with a variety of lab experiences across many specialties of biomedical engineering. Experiments emphasize proper data collection and analysis as well as critical reading and scientific writing. (This course is available to RIT degree-seeking graduate students.) Lab 6, Lecture 2 (Fall).

This course is used by students in the Biomedical Engineering MS degree program as a capstone experience following completion of BIME 607 Graduate Biodesign. Students will learn and apply advanced Biodesign strategies related to intellectual property, regulatory approval, and potential commercialization, completing a series of modules with specific learning goals. The course will include the design and fabrication of product concepts using rapid prototyping tools. Students completing an internship may use that experience as motivation for their project in this course. Students must work with a faculty advisor who will approve their topic and review their progress throughout the completion of this capstone experience. A written paper and presentation of the work as well as a prototype are required. (Prerequisites: BIME-607 or BIME-608 or equivalent course.) Ind Study 6 (Fall, Spring, Summer).

This seminar course presents topics of contemporary interest to graduate students enrolled in the program. Presentations include off campus speakers, and assistance with progressing on your research. Selected students and faculty may make presentations on current research under way in the department. (This course is available to RIT degree-seeking graduate students.) Lecture 1 (Fall, Spring).

The course begins with a pertinent review of linear and nonlinear ordinary differential equations and Laplace transforms and their applications to solving engineering problems. It then continues with an in-depth study of vector calculus, complex analysis/integration, and partial differential equations; and their applications in analyzing and solving a variety of engineering problems. Topics include: ordinary and partial differential equations, Laplace transforms, vector calculus, complex functions/analysis, complex integration. Chemical engineering applications will be discussed throughout the course. (Prerequisites: Graduate standing in Chemical Engineering.) Lecture 3 (Fall).