Covers applications to applied mechanics, thermofluids, and dynamics/control problems relevant to engineering. Topics include differential equations applied to modeling and characterization of processes, linear algebra used for multidimensional and complex system computations and modeling, and statistics and probability used for controls and signal analysis, among other applications. Introduces the foundational basis for approximate methods of engineering analysis, including its application to finite element analysis.
Designed to introduce new graduate bioengineering students to the fundamentals of bioengineering research topics and methodology. Includes outside speakers to discuss general topics in bioengineering. Examples of course topics include the medical device qualification and regulatory environment, tissue engineering, cell engineering, mechanobiology, drug delivery, bioimaging, neuromotor control, effective design of experiments, writing research proposals for the National Institutes of Health (NIH) and how to evaluate and write a peer-reviewed journal article, etc. Expects students to read, critically evaluate, and present the research in a bioengineering journal article. Students are then expected to extend their article into a hypothesis-driven proposal in NIH format with an oral defense of the proposal.
This course is based on reading and discussing primary literature that
exemplifies approaches that have revealed new concepts and principles
governing biological systems. Special emphasis will be placed on
difficulties that lie at the interface of theory and experiment in
bioengineering and systems biology. Each week, the students will read
and discuss two important papers, and the majority of the class time
will be devoted to student discussions rather than to formal lectures.
Topics include: cooperativity, robust adaptation, kinetic
proofreading, sequence analysis, clustering, network analysis,
dynamical systems, and biological design principles.
A module on mass-spectromery analysis from a survey course of advanced and emerging research methods in bioengineering. Broad range of topics include nucleic acid imaging, genetic engineering, mass-spectrometry proteomics, non-linear systems and parameter estimation. Lectures are led weekly by different department faculty members, with particular emphasis on development of methods for quantitative analysis of experimental data. Students are expected to have some familiarity and proficiency with widely used analysis tools such as Matlab, Python or R.
The course is a collection of short lectures. Each lecture introduces one topic in 5-10 min max. Topics include: Non-ignorable missing data, GSEA, Reproducibility versus accuracy, Batch effects, Data biases. TouTube playlist:Statistics for Proteomics