MMSc-BMI Curriculum

This introductory course surveys methods in biomedical informatics, including methods and approaches used in clinical informatics, bioinformatics, imaging and population health informatics. Basic concepts, trends, and best practices in biomedical informatics and biomedical research, including research ethics, the conduct of research in medical science, and broad issues relative to the application of research to human health are covered. Students will become familiar with core biomedical informatics methodologies.

BMI 701 Course and Schedule Details

This course will teach students advanced coding and statistics needed to extract, transform and prepare data to work with various biomedical informatics pipelines and analyze the results. Topics covered include:

• Foundational statistical theory for biomedical informatics
• Advanced R with focus on implementing statistical analyses and data visualizations
• Advanced Python with focus on running machine learning pipelines

BMI 714 Course and Schedule Details

This course will teach graduate students both important concepts and practical skills needed to perform high-quality biomedical informatics research. The course examines core principles related to the conduct of biomedical research and some of its practical applications in the field of biomedical informatics. Students will also learn to synthesize and communicate these concepts through written work and a poster presentation.

BMI 717 Course and Schedule Details

Outside of the set of required courses, students will have the opportunity to choose from a wide range of available courses to meet the program's elective requirements. In the first semester, students must complete a minimum of 6 credits to fulfill this requirement. 

Possible elective options in the fall semester include:

• BMI 706: Data Visualization for Biomedical Applications (2 credits)
• BMI 708: Precision Medicine (2 credits)
• BMI 709: Creating Biomedical Dashboards with R Shiny (2 credits)
• Any of the pre-approved courses listed on the Program Electives Webpage. 

Students will also have the opportunity to petition program approval to take courses outside of these options.  

Artificial intelligence (AI) continues to transform medicine, offering cutting-edge approaches to address challenges in medical research and practice. This course covers the foundations of modern AI, including self-supervised learning, generative models, and multimodal techniques with applications to natural language processing, medical image analysis, patients’ medical records, and longitudinal data. The course aims to equip students with both a technical understanding of AI techniques and the implications of these technologies, especially in terms of model and data interpretability, integration into clinical and research workflows, human-AI interaction, and ethical considerations. Materials will be presented through lectures by faculty, readings of contemporary literature, small group research projects, and multiple practical tutorials with hands-on components. 

BMI 702 Course and Schedule Details

This course will teach biomedical informatics graduate students the practical skills needed to use scientific literature in their biomedical informatics research. At the beginning of the course, students will participate in interactive lessons about literature reviews, the peer review process, and evaluating scientific literature. Then, students will read, evaluate, and present papers to their peers in an informal journal club format and in a formal presentation. In the final weeks of the course, students will learn how to write their own scientific proposals and papers that build on existing scientific literature.

BMI 718 Course and Schedule Details

This course will teach graduate students the practical skills to plan and execute a biomedical informatics research project, including: presentations, figure design, rigor and reproducibility, setting timelines, adapting to obstacles. This course is designed for students in the MMSc in Biomedical Informatics program and will prepare them for their thesis research year and thesis milestones, such as TAC meetings and progress report.

BMI 719 Course and Schedule Details

Outside of the set of required courses, students will have the opportunity to choose from a wide range of available courses to meet the program's elective requirements. In the second semester, students must complete a minimum of 8 credits to fulfill this requirement. 

Possible elective options in the spring semester include:

• Deep Learning for Biomedical Data (2 credits)
• Single-cell analysis for Functional Genomics of Disease (2 credits)
• Creating Biomedical Dashboards Using R Shiny (2 credits)
• Health Information Technology: From Ideation to Innovation (4 credits)
• Any of the pre-approved courses listed on the Program Electives Webpage. 

Students will also have the opportunity to petition program approval to take courses outside of these options.