Biomedical Informatics M.S.

LEARNING OBJECTIVES

1. This program provides a general overview of fundamental concepts and applications in Biomedical Informatics. This course introduces students to the major components of healthcare data analytics, such as healthcare information infrastructure, data standards, computational approaches, and data science methodology. Invited speakers with expertise in clinical, imaging, and translational informatics will discuss the development of novel applications in Genomics, Radiomics, Pathomics, Public Health, and Precision Medicine.

2. This program presents a comprehensive overview of human biology, anatomy, and physiology. Students will start by learning about the fundamentals of cell biology, biochemistry, and genetics, followed by a transition into learning about human tissues, organs, and organ systems. The pathophysiology of common diseases is also highlighted to contextualize a working knowledge of the subject matter with the intention of serving as an appropriate starting point for the development of healthcare analytics applications.

3. Upon completion of the program, students are expected to learn basic programming skills and be prepared for future data analytics courses. They are expected to understand how to use various tools for data processing and analysis. They are expected to learn basic skills to implement simple programs to achieve different goals. They are expected to learn to debug the code. They are expected to learn to analyze efficiency of the program and to optimize the time and memory consumption.

4. Upon completion of the program, students should be able to access data from various public molecular data repositories. They should be able to prepare summaries by performing data processing of high-dimensional datasets that are associated with a range of biological signals, spanning from next generation sequencing to clinical data.

5. Upon completion of the program, students are expected to gain the general understanding of the analytical aspects of the Biomedical Imaging Informatics. It covers a broad spectrums of biomedical image analysis techniques: image enhancement, segmentation, registration, object detection, object tracking, event detection, and image classification. The course will also cover a wide range of image modalities: Magnetic resonance imaging, Computed tomography, Ultrasound, Positron emission tomography, Microscopy imaging, etc. The computation/analysis will be carried out using languages such as Matlab and Python.

6. Upon completion of the degree, students should be able to participate in groundbreaking research in cutting-edge fields like artificial intelligence and machine learning through completion of a capstone project.