Below are the lists of courses that are pre-approved for elective credit for the MMSc-BMI program in the Fall 2024 and Spring 2025 semesters
These courses do not conflict with the required core courses for the 2024 - 2025 academic year. Students can petition to take courses that are not already on this list through the New Elective Request Form beginning August 1st for the Fall 2024 semester, and January 2, 2025, for the Spring 2025 Semester.
Spring 2025 Pre-Approved Electives
Harvard Medical School
Includes all BMI CoursesBMI 707 - Deep Learning for Biomedical Data (HMS: 2 credits)
Spring Semester Course
Deep learning is a branch of machine learning that leverages multiple layers of data representations to capture complex data patterns at various levels of abstraction. Inspired by the organization of neurons in biological systems, deep learning has demonstrated exceptional performance in many tasks, including image classification, natural language processing, and protein structure prediction. This course will introduce the fundamental principles of deep neural networks and GPU computing, discuss convolutional neural networks and transformer architectures, and examine key biomedical applications. Students are expected to be familiar with linear algebra and machine learning and will participate in a group project.
BMI 709 - Creating Biomedical Dashboards Using R Shiny (HMS: 2 credits)
Fall and Spring Semester Course
Biomedical research projects and pipelines often generate output that requires additional interpretation or benefit from interactive and customizable summarization. Web-based dashboards are a convenient way of providing an easy interface to interact with this data, eliminating the need for direct interaction with the underlying data processing.
This course will teach you Shiny, a powerful dashboard framework on top of R that lets you build custom, web-based applications that can serve as a front-end to any R code. You will learn how to build apps from scratch or convert existing R scripts into a Shiny application.
Students will need to enter the class with foundational R programming skills to start writing Shiny applications. Familiarity with Git and GitHub is highly recommended as this is how course materials and assignments in this class will be distributed and evaluated.
BMI 710 - Single-Cell Analysis for Functional Genomics of Disease (HMS: 2 credits)
Spring Semester Course
Single-cell technologies promise unparalleled insights into human biology, but first, these data require a new computational and statistical toolkit. In this course, we will compare single-cell and bulk data analysis paradigms, and explore new methods to quality control, analyze, and interpret single-cell-RNA-sequencing data. Students will learn about single-cell technologies and experimental design, build pipelines to process sequencing data, and use R packages for quality control and analysis. Students will learn about frameworks for interpreting single-cell data - eg., trajectories, differential abundance - and use them to answer biological questions with single-cell datasets in diverse domains. We will conclude by exploring the application of single-cell technologies to disease cohorts with spatial profiling, reference mapping, and multimodal approaches. Class activities will include lectures, coding activities, and paper discussions.
BMI 741 - Health Information Technology Innovation: From Ideation to Implementation (HMS: 4 credits)
Spring Semester Course
As the US healthcare system moves from a fee-for-service to value-based reimbursement system and seeks to deliver higher quality care more efficiently, there is increased need and opportunity for innovation. Clinical Informatics analyzes, designs, implements, and evaluates information and communication systems to enhance health outcomes, to improve patient care, and to enable healthcare transformation. This class applies health information technology (HIT) to solve health care problems, teaching the skills to identify health care needs and pain points, design technology-based solutions (new solution, optimize existing system, or purchase vendor solution), and lead successful implementations. Course activities will include lectures, panel discussions, and laboratory sessions. Expert speakers from hospitals, technology vendors, and start-ups will present real-world examples and share lessons learned. Course participants will complete a longitudinal group project proposing an HIT innovation project; this project may serve as the basis for start-ups, fellowship projects, and research theses.
NCE 512 - Big Data and Machine Learning in Healthcare Applications (HMS: 2 credits)
Spring Semester Course
This course is designed as a critical, hands-on exploration of major healthcare data sources and current state-of-the-art algorithms. The students will learn how to use data for solving the most challenging healthcare problems, to achieve real implementation gains, and to avoid common pitfalls. We will study a wide range of real problems (in clinical workflows, patient records, medical images), to see how their data patterns can be found and used in the most meaningful way. The course will be based on real data, including healthcare operations records, patient demographics, clinical records (vital signs, lab results), and medical imaging. Note that all data will come from public and anonymized sources, so no special medical records access/clearance will be required.
Harvard Faculty of Arts and Sciences
APCOMP 209B - Advanced Topics in Data Science (FAS: 4 credits, HMS: 4 credits)
Spring Semester Course
Note: MBI students have successfully enrolled in this course without taking part 1 in the fall. Please make sure to mention your degree program and programming preparation undergone in BMI 714 when submitting petitions for this course.
Building upon the material in Data Science 1, the course introduces advanced methods for statistical modeling, representation, and prediction. Topics include multiple deep learning architectures such as CNNs, RNNs, transformers, language models, autoencoders, and generative models as well as basic Bayesian methods, and unsupervised learning.
COMPSCI 1810 - Machine Learning (FAS: 4 credits, HMS: 4 credits)
Spring Semester Course
Introduction to machine learning, providing a probabilistic view on artificial intelligence and reasoning under uncertainty. Topics include: supervised learning, ensemble methods and boosting, neural networks, support vector machines, kernel methods, clustering and unsupervised learning, maximum likelihood, graphical models, hidden Markov models, inference methods, and computational learning theory. Students should feel comfortable with multivariate calculus, linear algebra, probability theory, and complexity theory. Students will be required to produce non-trivial programs in Python.
GENETICS 228 - Genetics in Medicine: From Bench to Bedside (FAS: 4 credits, HMS: 4 credits)
Spring Semester Course
Focus on translational medicine: the application of basic genetic discoveries to human disease. Each three-hour class will focus on a specific genetic disorder and the approaches currently used to speed the transfer of knowledge from the laboratory to the clinic. Each class will include a clinical discussion, a patient presentation if appropriate, followed by lectures, a detailed discussion of recent laboratory findings and a student-led journal club. Lecturers will highlight current molecular, technological, bioinformatics and statistical approaches that are being used to advance the study of human disease. There is no exam. Students will present one paper per session in a journal club style.
Attendance and active participation for the duration of all class meetings is required. If you are unable to attend class or cannot be present for the entire session, you are expected to contact the course instructor. Two incomplete or missed sessions will result in a failing grade. Please do not sign up if you know you will have to miss 2 or more sessions. For more information visit https://ecor.mgh.harvard.edu/Default.aspx?node_id=375
MICROBI 210 - Microbial Sciences: Chemistry, Ecology and Evolution (FAS: 4 credits, HMS: 4 credits)
Spring Semester Course
This is an interdisciplinary graduate-level and advanced undergraduate-level course in which students explore topics in molecular microbiology, microbial diversity, host-microbe associations in health and disease, and microbially-mediated geochemistry in depth. This course will be taught by faculty from the Microbial Sciences Initiative. Topics include the origins of life, biogeochemical cycles, microbial diversity, and ecology. Course will limit enrollment to 20 students.
Harvard T.H. Chan School of Public Health
BST 210 - Applied Regression Analysis (HSPH: 5 credits, HMS: 4 credits)
Spring Semester Course
Topics include model interpretation, model building, and model assessment for linear regression with continuous outcomes, logistic regression with binary outcomes, and proportional hazards regression with survival time outcomes. Specific topics include regression diagnostics, confounding and effect modification, goodness of fit, data transformations, splines and additive models, ordinal, multinomial, and conditional logistic regression, generalized linear models, overdispersion, Poisson regression for rate outcomes, hazard functions, and missing data. The course will provide students with the skills necessary to perform regression analyses and to critically interpret statistical issues related to regression applications in the public health literature.
BST 223 - Applied Survival Analysis (HSPH: 5 credits, HMS: 4 credits)
Spring Semester Course
BST 223 is a course on survival analysis, or more generally time-to-event analysis, with the primary audience being graduate students pursuing a Masters degree in biostatistics or a PhD in one of the other departments at the Harvard Chan School. Covered in the course will be: an introduction to various types of censoring and truncation that commonly arise; the mathematical representations of time-to-event distributions, such as via the hazard and survivor functions; nonparametric methods such as Kaplan-Meier estimation of the survivor function and log-rank test for hypothesis testing; semi-parametric and parametric regression modeling techniques, such as the Cox model, the accelerated failure time model, the additive hazards model and cure fraction models; survival analysis within the causal inference paradigm; the analysis of competing and semi-competing risks; outcome-dependent sampling schemes, such as nested case-control and case-cohort designs; and, power/sample size calculations for studies with time-to-event endpoints. Throughout, equal emphasis will be given to the theoretical/technical underpinnings of survival analysis and to the use of real world data examples.
BST 231 - Statistical Inference I (HSPH: 5 credits, HMS: 4 credits)
Spring Semester Course
A fundamental course in statistical inference. Discusses general principles of data reduction: exponential families, sufficiency, ancillarity and completeness. Describes general methods of point and interval parameter estimation and the small and large sample properties of estimators: method of moments, maximum likelihood, unbiased estimation, Rao-Blackwell and Lehmann-Scheffe theorems, information inequality, asymptotic relative efficiency of estimators. Describes general methods of hypothesis testing and optimality properties of tests: Neyman-Pearson theory, likelihood ratio tests, score and Wald tests, uniformly and locally most powerful tests, asymptotic relative efficiency of tests.
BST 261 - Data Science II (HSPH: 2.5 credits, HMS: 2 credits)
Spring Semester Course
This course is an introduction to deep learning, a branch of machine learning concerned with the construction, development, and application of neural networks. Deep learning algorithms extract layered high-level representations of data in a way that maximizes performance on a given task. We will cover a range of topics including basic neural networks, convolutional networks, and recurrent networks, and applications to problem domains like computer vision and speech recognition. Programming (Python) and case studies will be used throughout the course to provide hands-on training in these concepts.
EPI 293 - Analysis of Genetic Association Studies (HSPH: 2.5 credits, HMS: 2 credits)
January Term Course
At the end of this course students will grasp Concept and Theory, Methods and Software Tools needed to critically evaluate and conduct genetic and genomic association studies in unrelated individuals and family samples, including: basic molecular and population genetics, marker selection algorithms, haplotyping, multiple comparisons issues, population stratification, genome-wide association studies, genotype imputation, gene-gene and gene-environment interaction, analysis of microarray data (including gene expression, methylation data analysis, eQTL mapping), next-generation sequencing data analysis and genetics simulation studies. Useful software tools will be introduced and practiced in labs and projects. Students interested in methodology development will find interesting research topics to pursue further. Students interested in application will learn cutting-edge methods and tools for their ongoing projects. Course materials will be updated according to the fast-growing areas of genomics and other omics studies.
Harvard Kennedy School
DPI 617 - Law, Order, and Algorithms (HKS: 4 credits, HMS: 4 credits)
Spring Semester Course
Data and algorithms are rapidly transforming law enforcement and the criminal legal system, including how police officers are deployed, how discrimination is detected, and how sentencing, probation, and parole terms are set. Modern computational and statistical methods offer the promise of greater efficiency, equity, and transparency, but their use also raises complex legal, social, and ethical questions. In this course, we examine the often subtle relationship between law, public policy, and technology, drawing on recent court decisions, and applying methods from economics, statistics, and machine learning. We survey the legal and ethical principles for assessing the equity of algorithms, describe computational techniques for designing fairer systems, and consider how anti-discrimination law and the design of algorithms may need to evolve to account for machine bias. Concepts will be developed in part through guided in-class coding exercises, though prior programming experience is not necessary.
Massachusetts Institute of Technology
6.3900 - Introduction to Machine Learning (MIT: 12 units, HMS: 4 credits)
Fall and Spring Semesters
Introduction to the principles and algorithms of machine learning from an optimization perspective. Topics include linear and non-linear models for supervised, unsupervised, and reinforcement learning, with a focus on gradient-based methods and neural-network architectures. Previous experience with algorithms may be helpful.
7.75 - Human Genetics and Genomics (MIT: 12 units, HMS: 4 credits)
Spring Semester Course
Upper level seminar offering in-depth analysis and engaged discussion of primary literature on the dimensions and phenotypic consequences of variation in human genes, chromosomes, and genomes. Topics include the human genome project; pedigree analysis; mutation and selection; linkage and association studies; medical genetics and disease; sex chromosomes and sex differences; the biology of the germ line; epigenetics, imprinting, and transgenerational inheritance; human origins; and evolutionary and population genetics. Students taking graduate version complete additional assignments. Limited to 20 total for versions meeting together.
HST 971 - Strategic Decision Making in Life Science Ventures (MIT: 9 units, HMS: 4 credits) Same course as 15.363, students are recommended to enroll in HST 971 listing.
Spring Semester Course
Surveys key strategic decisions faced by managers, investors and scientists at each stage in the value chain of the life science industry. Aims to develop students' ability to understand and effectively assess these strategic challenges. Focuses on the biotech sector, with additional examples from the digital health and precision medicine industries. Includes case studies, analytical models, and detailed quantitative analysis. Intended for students interested in building a life science company or working in the sector as a manager, consultant, analyst, or investor. Provides analytical background to the industry for biological and biomedical scientists, engineers and physicians with an interest in understanding the commercial dynamics of the life sciences or the commercial potential of their research.
HST 978 - Healthcare Ventures (MIT: 12 units, HMS: 4 credits) Same course as 15.367, students are recommended to enroll in HST 978 listing.
Spring Semester Course
Addresses healthcare entrepreneurship with an emphasis on startups bridging care re-design, digital health, medical devices, and new healthcare business models. Includes prominent speakers and experts from key domains across venture capital, medicine, pharma, med devices, regulatory, insurance, software, design thinking, entrepreneurship, including many alumni from the class sharing their journeys. Provides practical experiences in venture validation/creation through team-based work around themes. Illustrates best practices in identifying and validating health venture opportunities amid challenges of navigating healthcare complexity, team dynamics, and venture capital raising process. Intended for students from engineering, medicine, public health, and MBA programs.