MMSc-BMI Curriculum

This course will introduce students with an interest in biomedical informatics to a wide variety of biomedical data from the macro (e.g. population health) to the micro level (e.g. an individual's DNA). Students will learn about both clinical and translational data used in current biomedical research by exploring the ways these data are generated, stored, managed, and protected. In-depth understanding of the intricacies of biomedical data will prepare students to carry out better analyses in the future, whether this is building biomedical AI models, advancing understanding of the human genome, or improving patients’ health.

This course is aimed towards students with an interest in working with biomedical data and thus a basic understanding of molecular biology is recommended. In addition, students will learn to interact with these data through database queries or via the Linux command line, so a basic computational background is desirable.

Students will be evaluated using a combination of weekly assignments and two exams, both of which will test students’ understanding of different biomedical data formats, important concepts, and hands-on data processing skills.

BMI 701 Course and Schedule Details

This course covers the programming, data analysis, and statistics skills required for advanced graduate-level courses in biomedical informatics. During the first half of the course, students develop coding skills to reproducibly analyze complex biomedical data. Topics include visualization, clustering, dimensionality reduction, and high-performance computing. During the second half of the course, students develop their statistical toolkit for robust hypothesis testing. Topics include hypothesis testing, parametric and non-parametric tests, simulations and resampling, regression models, classifiers, and statistical significance. 

This course is highly interactive and applied. In-class activities and problem sets provide opportunities for students to practice analyzing real-world clinical, molecular, and epidemiological data to answer biological questions - from designing the analysis all the way through interpreting and communicating the results. All coding will be in the R programming language, so students should have basic R programming skills prior to enrolling in this course.

BMI 714 Course and Schedule Details

This course prepares graduate students to perform high-quality biomedical informatics research. The course starts with an overview of foundational ethics principles for biomedical research and examines their practical applications in the field of biomedical informatics. The course also addresses other key ethics considerations for research, such as equity and emerging technologies. Throughout the course, students will use ethics analysis skills to analyze real-world research case studies at Harvard and beyond. They will also learn to communicate about research through written work and a 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. At least 2 credits of electives taken during the first year must be in an approved computational subject area. 

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)
• BMIF 204: Foundations of Clinical Data and its Applications (4 credits)
• Any of the pre-approved courses listed on the Program Electives Webpage. 

Students will also have the opportunity to petition for 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. At least 2 credits of electives taken during the first year must be in an approved computational subject area. 

In the second semester, students complete 6-8 credits to fulfill this requirement. Students must complete a minimum of 14 elective credits total by the end of the first year. 

Possible elective options in the spring semester include:

• AI In Medical Imaging (2 credits)
• Single-cell analysis for Functional Genomics of Disease (2 credits)
• Integrative Analyses for Rare Genetic Disease Diagnostics (2 credits)
• Biomedical Web Applications with R/Python 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 for program approval to take courses outside of these options.