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Blog Post number 4

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Blog Post number 1

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teaching

Biosimulation I

Graduate course, BMES-672, 2018

Biomedical engineering is at the interface of mathematics, engineering, and biology / medicine. This course will be on the mathematical analysis of biomedical engineering systems. As the first course in the Biosimulation sequence, it will emphasize analytical methods and the development of models. This course will provide the foundations for the modeling and mathematical analysis of biomedical engineering systems, with an emphasis on differential equations and an introduction to probabilistic methods. In general, the focus will be on analytical techniques, setting the stage for BMES673, which introduces numerical methods and other advanced computational techniques for solving applied problems. Modeling and specific biological and biomedical applications will be introduced through examples and experience.

Biosimulation II

Graduate course, BMES-673, 2018

Biomedical engineering is at the interface of mathematics, engineering, and biology / medicine. This course is the second in the Biosimulation sequence on the mathematical analysis of biomedical engineering systems. It will emphasize numerical methods for simulation to analyze models and generate predictions. As a whole, this course will provide the foundations for computer modeling and mathematical analysis of biomedical engineering systems. The numerical methods that are presented in this course are not only useful for general problems throughout science and engineering, but also helpful for complex computational problems involved in biological and biomedical domains. By studying numerical methods students can become more informed users of the underlying numerical algorithms used in bioengineering environments, and be better prepared to evaluate and judge the accuracy of the model results. Much more complex and applicable models with simulations will be introduced in this course than BMES672, since we will have access to computer simulation.

Neural Networks

Graduate course, BMES-725, 2019

The emergence of big data, machine learning algorithms, and increased computing power has enabled significant breakthrough in neural networks and related applications. This class will discuss recent progress and findings in the exciting field of deep learning. While mathematical aspects will be covered, the primary goal is to provide students with the deep learning tools and technical aspects needed to solve the data science problems in biomedicine. Besides the construction by students of computer simulations of important deep neural networks, the class will also show how to develop deep learning applications in the Cloud. Selected applications in computational neuroscience, computational biology, medical imaging, and electronic medical records will be discussed.