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 Courses

    BMI 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

    Updated by late November 2024

    MIT will not finalize their course schedules for spring 2025 until mid-late November 2024. Pre-approved courses will be added here once the schedule is published. 

Fall 2024 Pre-Approved Electives

  • Harvard Medical School

    Includes all BMI courses

    BETH 706 - Health, Law, Policy and Bioethics (HMS: 4 credits)

    Fall Semester Course

    This course is a survey-style introduction to legal topics in health policy and bioethics. It requires no experience in law, and topics covered could include legal aspects of the doctor-patient relationship, medical malpractice, privacy issues, health care finance, end-of-life issues, organ donation, disability, mental health, medical product regulation, and intellectual property. The course will not cover issues in reproductive ethics or human subjects research regulation as those are covered in other Master of Bioethics core classes. Students will be evaluated via class participation and written work. Sessions will be a mix of lecture and seminar-style, with occasional guest speakers.

    BETH 730 - Ethics and Governance of Digital Privacy and AI (HMS: 2 credits)

    Fall Semester Course

    Enabled by big data, Artificial Intelligence ("AI") technology has become ubiquitous developing at breakneck speed, challenging our ability to reflect meaningfully as a society on its ethical implications and to build policy guardrails that ensure its ethical and responsible development and use. This seminar adopts a multidisciplinary approach, focusing on the ethical and regulatory environments in the United States (US) and the European Union (EU). It aims to equip students with a robust understanding of the ethical, legal, and societal impacts of big data, digital privacy, and AI broadly with a special focus on healthcare. By exploring various normative bioethics perspectives and rights-based frameworks, the course will delve into a wide range of topics through lectures, assigned readings, and class discussions. Students will engage with guest speakers to explore the complexities of big data, automated processing, AI technology, and their associated challenges primarily in the healthcare context. The curriculum will cover the historical context of digital privacy and AI, the US and EU policy strategies, the harms of AI, and the concept of ethical data spaces. It will also examine AI applications in healthcare, providing students with the tools to address the pressing ethical questions relevant to medicine and bioscience.

    BMI 706 - Data Visualization for Biomedical Applications (HMS: 2 credits)

    Fall Semester Course

    Data visualization is an essential component of the analysis toolkit in any data-driven research endeavor. As the primary interface through which analysts are consuming data, data visualization can facilitate new discoveries but also mislead, bias, and slow down progress if done poorly. This visualization course will focus on the role of data visualization in biomedical data analysis applications and also cover the principles of perception and cognition relevant for data visualization. It will also introduce the data visualization design process, and visualization tools and techniques used in biomedical informatics. Major topics include interaction techniques and implementations, high-dimensional data, networks, genomes, time and event sequences, and common generic visualization systems. Students are expected to complete class readings, a final project, and make a final presentation.

    BMI 708 - Precision Medicine (HMS: 2 credits)

    Fall Semester Course

    The real value in biomedical research lies not in the scale of any single data source, but in the ability to integrate and interrogate multiple complementary datasets simultaneously. This course will dive into methods combining clinical and genomics data across different scales and resolutions to enable new perspectives for essential biomedical questions. It will focus on the development of novel statistical methods and techniques for the integration of multiple heterogeneous clinical and epidemiological cohorts, including Electronic Health Records (EHRs). At the HMS Department of Biomedical Informatics, we have built open-source technologies to integrate vast and multiple types of high-throughput phenotypic and genotypic data in these cohorts. The students will use those tools during the problem sets with real patient data to learn the process of making new biomedical discoveries.

    BMI 709 - Creating Biomedical Dashboards Using R Shiny (HMS: 2 credits)

    Fall Semester and Spring Semester

    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.

  • Harvard Faculty of Arts and Sciences

    APCOMP 215 - Advanced Practical Data Science (FAS: 4 credits, HMS: 4 credits)

    Fall Semester Course

    This course aims to review existing Deep Learning flow while applying it to a real-world problem. Then we will build and deploy an application that uses the deep learning model to understand how to productionize models. This course follows the CS109 model of balancing between concept, theory, and implementation. Split into three parts; the course starts with the review of Deep Learning concepts for data and modeling and how to apply them to different tasks, including vision and language tasks. The next part will be Development, where you use the models you trained in part 1 and incorporate them into real-world applications. Finally, you will Deploy the application in Google Cloud Platform (GCP). The three parts will cover in detail topics such as Transfer learning, Containerization using Docker, and Scaling deployments using Kubernetes. At the end of this module, you will build efficient deep learning models and design, build and deploy applications that scale.

    BCMP 230  - Principles and Practice of Drug Development (FAS: 4 credits, HMS: 4 credits)

    Fall Semester Course

    This course is cross-listed and is co-taught with MIT '15.136'. MMSc-BMI students should register via 'BCMP 230'

    Introduction to and critical assessment of the concepts, technologies and practical challenges of developing new medicines and bringing them to market. Pharmacology fundamentals, preclinical drug discovery, clinical trials, manufacturing and regulatory issues, as well as financing and marketing are discussed for small molecule, biologic and cellular therapies. Suitable for individuals with a wide variety of backgrounds and interests from biology to engineering, business and medicine (undergraduate, graduates in MBA, MD and PhD programs). Taught by MIT and HMS faculty and by industry experts. Emphasizes a high level of student engagement via weekly news updates and projects involving collaboration across interdisciplinary teams. 

    BMIF 201 - Concepts in genome analysis (FAS: 4, HMS: 4 credits)

    Fall Semester Course

    This course focuses on quantitative aspects of genetics and genomics, including computational and statistical methods of genomic analysis. We will introduce basic concepts and discuss recent progress in population and evolutionary genetics and cover principles of statistical genetics of Mendelian and complex traits. We will then introduce current genomic technologies and key algorithms in computational biology and bioinformatics. We will discuss applications of these algorithms to genome annotation and analysis of epigenomics, cancer genomics and metagenomics data. Proficiency in programming and basic knowledge of genetics and statistics will be assumed.  

    COMPSCI 1070 - Systems Development for Computational Science (FAS: 4, HMS: 4 credits)

    Fall Semester Course

    This is a project-based course emphasizing designing, building, testing, maintaining and modifying software for scientific computing and data sciences. Students will work in groups on a number of projects, ranging from small data-transformation utilities to large-scale systems. Students will learn to use a variety of tools and languages, as well as various techniques for organizing teams. Most important, students will learn to adapt basic tools and approaches to solve computational problems in academic or industrial environments.

    COMPSCI 2243 - Algorithms for Data Science (FAS: 4 credits, HMS: 4 credits)

    Fall Semester Course

    This is a graduate topics class on algorithmic challenges in modern machine learning and data science. We will touch upon a number of domains (generative modeling, deep learning theory, robust statistics, Bayesian inference) and frameworks for algorithm design (spectral/tensor methods, moment methods, message passing, diffusions), focusing on provable guarantees. The theory draws upon a range of techniques from stochastic calculus, harmonic analysis, statistical physics, algebra, and beyond. We will also explore the myriad modeling challenges in building this theory and prominent paradigms (semi-random models, smoothed complexity, oracles) for going beyond traditional worst-case analysis.

    COMPSCI 2420 - Computing at Scale (FAS: 4 credits, HMS: 4 credits)

    Fall Semester Course

    Machine learning accelerators have enabled a new era of special general computing. This course addresses computation scaling techniques required in implementing these accelerators over parallel, distributed and embedded computing platforms. Students will learn principled methods for mapping prototypical computations onto compute nodes of various hardware capabilities and interconnect patterns, as well as about the close interactions between computational algorithms, machine learning models, and computer organizations. After successfully taking this course, students will have acquired an integrated understanding of these issues, and can take on the challenging tasks of designing and using energy-efficient high-performance accelerators.

    COMPSCI 2790R - Research Topics in Human-Computer Interaction (FAS: 4 credits, HMS: 4 credits)

    Fall Semester Course

    Students will read, write about, prepare presentations about, and discuss human-computer interaction (HCI) and HCI-relevant work with a focus on papers about interfaces and automation that work especially well with (or clash against) human cognitive capabilities. Papers will primarily be on the building and evaluation of novel systems, as well as theories of and studies characterizing human cognition relevant to human-AI interaction scenarios. As a semester-long final project, students will pursue a research project of their own design in self-organized groups and present their findings in writing and orally in a conference-style format, as means to understand more deeply the processes behind HCI research.

    MCB 112 - Biological Sequence Analysis (FAS: 4 credits, HMS: 4 credits)

    Fall Semester Course

    Biology has become a computational science, requiring analysis of large data sets from genome sequencing and other technologies. This course teaches computational methods in biological sequence analysis, using an empirical and experimental framework suited to the complexities of biological data, emphasizing computational control experiments. The course is primarily aimed at biologists learning computational methods, but is also suited for computational and statistical scientists learning about biological sequence data.

    STAT 110 - Introduction to Probability (FAS: 4 credits, HMS: 4 credits)

    Fall Semester Course 

    A comprehensive introduction to probability. Basics: sample spaces and events, conditional probability, and Bayes' Theorem. Univariate distributions: density functions, expectation and variance, Normal, t, Binomial, Negative Binomial, Poisson, Beta, and Gamma distributions. Multivariate distributions: joint and conditional distributions, independence, transformations, and Multivariate Normal. Limit laws: law of large numbers, central limit theorem. Markov chains: transition probabilities, stationary distributions, convergence.  

    STAT 139 - Linear Models (FAS: 4 credits, HMS: 4 credits) 

    Fall Semester Course 

    An in-depth introduction to statistical methods with linear models and related methods. Topics include group comparisons (t-based methods, non-parametric methods, bootstrapping, analysis of variance), linear regression models and their extensions (ordinary least squares, ridge, LASSO, weighted least squares, multi-level models), model checking and refinement, model selection, cross-validation. The probabilistic basis of all methods will be emphasized.

    STAT 236 - Sparse Inference, and Network and Text Analysis (FAS: 4 credits, HMS: 4 credits)

    Fall Semester Course

    High dimensional data analysis is a recent interdisciplinary research area of Statistics, Genetics and Genomics, Engineering, and several other scientific areas. It addresses an array of challenging problems that of contemporary interest, and research in this area has been very active in the past decade.

    This course aims to provide a systematic introduction to various topics in high dimensional data analysis, focusing on large-scale sparse learning, and network and text data analysis. Large-scale sparse learning: Sparsity is a universal phenomenon in modern high dimensional data. Sparse structures are observed in many application settings and have many different forms, such as parameter sparsity, graph sparsity, eigenvalue sparsity, and so on. Exploring sparsity has become a common strategy in data analysis and has largely reshaped classical multivariate statistics problems. This course will investigate classical problems such as multiple testing, linear regression, classification and clustering, under the modern sparse settings. For each problem, the course discusses recent statistical methods for taking advantage of sparsity, and introduces the
    theoretical framework for analyzing these methods.

    Network and text data analysis: Social networks and text documents are unconventional data types. This course introduces statistical models and methods for analyzing such type of data. Topics for network data analysis include community detection, mixed membership estimation, link prediction, and dynamic network modeling. Topics for text data analysis include topic modeling, word embedding, information retrieval, and sentiment analysis.

  • Harvard T.H. Chan School of Public Health

    BST 213 - Applied Regression for Clinical Research (HSPH: 5 credits, HMS: 4 credits)

    Fall Semester Course

    Program Elective

    This course will introduce students involved with clinical research to the practical application of multiple regression analysis. Linear regression, logistic regression and proportional hazards survival models will be covered, as well as general concepts in model selection, goodness-of-fit, and testing procedures. Each lecture will be accompanied by a data analysis using SAS and a classroom discussion of the results. The course will introduce, but will not attempt to develop the underlying likelihood theory. Background in SAS programming ability required.

    BST 227 - Introduction to Statistical Genetics (HSPH: 2.5 credits, HMS: 2 credits)

    Fall Semester Course

    This course introduces students to the diverse statistical methods used throughout the process of statistical genetics, from familial aggregation and segregation studies to linkage scans and association studies. Topics covered include basic principles from population genetics, multipoint and model-free linkage analysis, family-based and population-based association testing, and Genome Wide Association analysis. Instructors use ongoing research into the genetics of respiratory disease, psychiatric disorders and cancer to illustrate basic principles. Weekly homework supplements reading, course lectures, discussion and section. Relevant concepts in genetics and molecular genetics will be reviewed in lectures and labs. The emphasis of the course is fundamental principles and concepts. Course Prerequisites: BST210 (concurrent enrollment allowed)Course Note: There will be a weekly lab section; the time will be scheduled at first meeting. Formerly BIO227

    BST 262 - Computing for Big Data (HSPH: 2.5 credits, HMS: 2 credits)

    Fall Semester Course

    Program Elective

    Big data is everywhere, from Omics and Health Policy to Environmental Health. Every single aspect of the Health Sciences is being transformed. However, it is hard to navigate and critically assess tools and techniques in such a fast-moving big data panorama. In this course, we are going to give a critical presentation of theoretical approaches and software implementations of tools to collect, store and process data at scale. The goal is not just to learn recipes to manipulate big data but learn how to reason in terms of big data, from software design and tool selection to implementation, optimization and maintenance. 

    BST 267 - Introduction to Social and Biological Networks (HSPH: 2.5 credits, HMS: 2 credits)

    Fall Semester Course

    Program Elective

    Many systems of scientific and societal interest consist of a large number of interacting components. The structure of these systems can be represented as networks where network nodes represent the components and network edges the interactions between the components. Network analysis can be used to study how pathogens, behaviors and information spread in social networks, having important implications for our understanding of epidemics and the planning of effective interventions. In a biological context, at a molecular level, network analysis can be applied to gene regulation networks, signal transduction networks, protein interaction networks, and more. This introductory course covers some basic network measures, models, and processes that unfold on networks. The covered material applies to a wide range of networks, but we will focus on social and biological networks. To analyze and model networks, we will learn the basics of the Python programming language and its Network X module. The course contains a number of hands-on computer lab sessions. There are five homework assignments and four reading assignments that will be discussed in class. In addition, each student will complete a final project that applies network analysis techniques to study a public health problem. Course Prerequisites: BST201 or ID200 or ID201 or ID207 or BST202&203 or BST206&207 or BST206&208. Formerly BIO521

    EPI 201 - Introduction to Epidemiology Methods I (HSPH: 2.5 credits, HMS: 2 credits)

    Fall Semester Course

    Program Elective

    EPI201 introduces the principles and methods used in epidemiologic research. The course discusses the conceptual and practical issues encountered in the design and analysis of epidemiologic studies for description and causal inference. EPI201 is the first course in the series of methods courses designed for students majoring in Epidemiology, Biostatistics and related fields, and those interested in a detailed introduction to the design and conduct of epidemiologic studies. Students who take EPI201 are expected to take EPI202 (Methods II). Course Note: Thursday or Friday lab required. 

    EPI 202 - Epidemiologic Methods 2: Elements of Epidemiologic Research (HSPH: 2.5 credits, HMS: 2 credits) 

    Fall Semester Course 

    Program Elective

    EPI202 is a seamless continuation of EPI201. This course builds on the material from EPI201 and extends to concepts of statistical inference, data analysis methods and causal inference in epidemiologic research. Principles and methods are illustrated with examples, and reviewed through homework and in-class exercises. This course is designed primarily for doctoral students majoring in Epidemiology and related fields, and those interested in a research career requiring a rigorous foundation in the design, conduct and analysis of epidemiologic studies.

    EPI 519 - Evolutionary Epidemiology of Infectious Disease (HSPH: 2.5 credits, HMS: 2 credits)

    Fall Semester Course

    Program Elective

    Like all living things, pathogens have evolved by natural selection. The application of evolutionary principles to infectious disease epidemiology is crucial to such diverse subjects as outbreak analysis, the understanding of how different genomic combinations of virulence and drug resistance determinants emerge, and how selection acts to produce successful pathogens that balance the costs and benefits of virulence and transmission. The goal of this course is to introduce basic evolutionary concepts, highlighting the importance of transmission to the fitness as illustrated by comparisons of the adaptive process among different sorts of pathogenic microorganisms. Students will also learn the basics of phylogenetic sequence analysis for the study of outbreaks and transmission, and the construction of simple mathematical models that probe the adaptive process. Students outside of HSPH must request instructor permission to enroll in this course.

    GHP 220 - Intro to Demographic Methods (HSPH: 2.5 credits, HMS: 2 credits)

    Fall Semester Course 

    Program Elective

    This is an introductory level class on the analysis of mortality, fertility and population change. It is required for all master’s and doctoral students in the department of Global Health and Population. Students are introduced to the core literature in this field through lectures, and assigned readings selected from peer-reviewed journals and textbooks. Together, these provide a graduate-level introduction to the principle sources and characteristics of population data and to the essential methods used for the analysis of population problems. The emphasis throughout is on understanding the key processes, models and assumptions used primarily for the analysis of demographic components. Practical training will be given through a required weekly laboratory session, assignments, and a final examination. Examples presented in class and used in assignments are drawn from several countries, combining both developed and developing in assignments are drawn from several countries, combining both developed and developing world realities.

    HPM 206 - Economic Analysis (HSPH: 5 credits, HMS: 4 credits)

    Fall Semester Course

    Program Elective

    Designed to bring students to an intermediate-level understanding of microeconomic theory. Emphasizes the uses and limitations of the economic approach, with applications to public health.

    HPM 282 - Innovative Problem Solving & Design Thinking in Healthcare (HSPH: 2.5 credits, HMS: 2 credits)

    Fall Semester Course

    Program Elective

    Innovative problem solving is a critical skill for healthcare leaders confronting disruptive change and operating in increasingly complex and fast-paced environments. The capacity to innovate is essential to effectively serving patients, improving outcomes and developing sustainable organizations. Design thinking is a disciplined approach to innovation that focuses intensely on the intersection of human needs and values, technical feasibility and strategic viability to create end-user value and pursue market opportunity. Healthcare organizations such as the Mayo Clinic, Kaiser Permanente and the UK's National Health Service (NHS) as well as Ministries of Health in many countries are turning to design thinking to improve patient care and citizen health.

    In contrast to a traditional approach to problem solving that focuses on deciding among known solutions, an innovative approach seeks the best solution possible given available resources, time, and team competencies. Innovative problem solving maximizes learning to reduce uncertainty by focusing on generation of new alternatives, experimentation, and exploration of multiple solutions.

    Learning will occur through a mix of individual and group exercises in class as well as a series of graded and ungraded assignments that enable you to effectively use innovation tools, acquire skills, and adopt mindsets that complement the analytical approaches you have developed in other courses.

    RDS 280 - Decision Analysis For Health/Medical Practice (HSPH: 2.5 credits, HMS: 2 credits)

    Fall Semester Course

    Program Elective

    This course is designed to introduce the student to the methods and growing range of applications of decision analysis and cost-effectiveness analysis in health technology assessment, medical and public health decision making, and health resource allocation. The objectives of the course are: (1) to provide a basic technical understanding of the methods used, (2) to give the student an appreciation of the practical problems in applying these methods to the evaluation of clinical interventions and public health policies, and (3) to give the student an appreciation of the uses and limitations of these methods in decision making at the individual, organizational, and policy level both in developed and developing countries.

    SBS 288 - Qualitative Research Methods in Public Health (HSPH: 2.5 credits, HMS: 2 credits) 

    Fall Semester Course 

    Program Elective

     What students can expect from this course: Qualitative research can be used alone or in combination with quantitative research to investigate public health questions. This introductory course will provide students with an overview of the range of important conceptual and practical issues associated with qualitative research, including providing general familiarity with the design of qualitative studies and conduct of commonly-used qualitative methods. The course begins by examining the variety of potential uses of qualitative methods in public health research and diverse qualitative research approaches.  The course then explores specific topics, including: developing research questions; ethics in qualitative research; “entering” the community to conduct qualitative research; role of theory; ensuring study rigor; selecting and implementing qualitative data collection methods (participant observation, different types of semi-structured interviews and focus groups); writing open-ended questions; sampling; data management and analysis; publishing results; writing research proposals; and considerations for choosing qualitative methods for mixed-methods qualitative or mixed-methods qualitative/quantitative studies. 

    Students should come to class prepared to apply concepts from readings and lectures through participation in class discussions and small group activities that will occur during every class period.  In addition, students will demonstrate application of concepts through completion of written assignments. 

    What this course is not: As this is an introductory course on qualitative research that provides an overview of all pertinent topics to foster familiarity with this research approach as a whole, the course cannot dwell deeply on any one topic.  Students who are looking for in-depth training on a particular step in qualitative research, such as how to analyze their own qualitative dataset, or how to use qualitative coding software, are advised to select a different course.  

  • Harvard Business School

    HBSMBA 1875 - A Manager's Guide to Leveraging Technology (HBS: 1.5 credits, HMS: 2 credits)

    Fall Semester Course

    As future managers, it is crucial to develop a comprehensive understanding of modern IT, gain practical experience in its application, and cultivate a strategic perspective on how IT is leveraged within organizations. This course is designed to provide students with a solid foundation in the key technologies shaping the business landscape, including cloud computing, mobile technologies, social media, data analytics, AI, machine learning, and generative AI. Students will learn to navigate the complex technology ecosystem, assess the business value of IT investments, and develop strategies for harnessing the power of emerging technologies. The course will also emphasize the importance of digital transformation and how students can develop the skills needed to lead such initiatives. 

    By the end of this course, students will be well-equipped to tackle the challenges and opportunities presented by the rapidly evolving technology landscape. They will have the knowledge and skills necessary to make informed decisions about technology adoption, implementation, and management, ensuring that their organizations remain competitive in an increasingly digital world. As the role of IT continues to expand and evolve, it is imperative that future managers across all business functions are prepared to leverage technology effectively and strategically. Classwork will include case studies, coding exercises, and projects.

    HBSMBA 6756 - Life Sciences Venture Creation (HBS: 3 credits, HMS: 4 credits)

    Fall Semester Course

    Creating, launching and funding Life Sciences ventures is a difficult and daunting task, especially given recent market conditions. This course is a practical, hands-on field course primarily designed for students who are very serious about pursuing entrepreneurship within the life sciences, including therapeutics, medical devices, diagnostics, tools, software & data, contract research/manufacturing and more). The course will feature workshops with practitioners and domain experts providing guidance and exploring how to successfully navigate critical tasks when launching a life sciences venture. These tasks include negotiating licenses for intellectual property, developing clinical budgets and timelines, using these tools to develop a business model and resource the venture, exploring the process of building a syndicate and establishing a board of advisors, finding human talent to accomplish scientific goals, and more. 

  • Harvard Law School

    HLS 2497 - Public Health Law and Policy (HLS: 2 credits, HMS: 2 credits)

    Fall Semester Course

    This seminar provides an overview of the historical law and policy decisions that have shaped the U.S. health care system and are informing current debates about health reform. Incorporating varying perspectives, the seminar discusses federal and state policy options to address current health and public challenges.

    This seminar begins with an analysis of health systems in other countries. Next, we discuss the key policy decisions that have shaped the current patchwork of public and private insurance coverage options in this country. After providing this international and historical context, we analyze in detail the key elements of the current U.S. health and public health care systems through the lens of its impact on vulnerable populations. We look at the components of the federal approach to reform, including the national health care reform law the Patient Protection and Affordable Care Act. We also consider several state initiatives that highlight how states are acting as laboratories of innovation to implement sweeping health and public health reforms. Finally, we discuss the current health law and policy climate in this country and explore both the opportunities and challenges for health policy solutions focused on increasing access to care and addressing public health concerns.

    This seminar is open to students interested in health and public health law and policy; no background or prerequisites are required. The reading materials include various book chapters, cases, news reports, and scholarly articles that present diverse viewpoints on the topics presented. The course employs experiential learning techniques, such as role plays, simulations, and discussion posts to spark debate between different sides of often controversial issues. Over the course of a semester, students gain a wealth of hands-on experience in current and emerging health law and policy issues, produce a written policy paper, and develop a range of problem-solving, policy analysis, research and writing, oral communication, advocacy and leadership skills.

    HLS 3307 - Health Law and Access to Medicines (HLS: 2 credits, HMS: 2 credits)

    Fall Semester Course

    This seminar explores the frameworks within health law and policy that relate to access to investigational and prescription drugs. The seminar will focus on a number of foundational topics within health law, including the United States’ fragmented structure of Medicare, Medicaid, and private insurance and how insurance relates to prescription drug affordability; the distribution of authority between legislators and regulators at the state and federal level as they make new law in this area; and how drug candidates are evaluated and, in the case of successful candidates, approved by federal regulators. The seminar will also apply these frameworks to a number of current issues in the area, including but not limited to drug pricing reform, expedited approval pathways, and the COVID-19 pandemic and its impact on drug and vaccine development.

  • Massachusetts Institute of Technology

    1.010B - Causal Inference for Data Analysis (MIT: 6 units, HMS: 2 credits)

    Fall Semester Course

    Introduces causal inference with an emphasis on probabilistic systems analysis. Readings about conceptual and mathematical background are given in advanced of each class. Class is focused on understanding theory based on real-world applications. The course is project-based and focused on cause-effect relationships, understanding why probabilistic outcomes happen. Topics include correlation analysis, Reichenbach's principle, Simpson's paradox, structural causal models and graphs, interventions, do-calculus, average causal effects, dealing with missing information, mediation, and hypothesis testing.

    6.1040 - Software Design (MIT: 18 units, HMS: 6 credits)

    Fall Semester Course

    Provides design-focused instruction on how to build complex software applications. Design topics include classic human-computer interaction (HCI) design tactics (need finding, heuristic evaluation, prototyping, user testing), conceptual design (inventing, modeling and evaluating constituent concepts), social and ethical implications, abstract data modeling, and visual design. Implementation topics include reactive front-ends, web services, and databases. Students work both on individual projects and a larger team project in which they design and build full-stack web applications.

    6 .7900 - Machine Learning (MIT: 12 units, HMS: 4 credits)

    Fall Semester Course

    Principles, techniques, and algorithms in machine learning from the point of view of statistical inference; representation, generalization, and model selection; and methods such as linear/additive models, active learning, boosting, support vector machines, non-parametric Bayesian methods, hidden Markov models, Bayesian networks, and convolutional and recurrent neural networks. Recommended prerequisite: 6.036 or other previous experience in machine learning. Enrollment may be limited.

    6.7960 - Deep Learning (MIT: 12 units, HMS: 4 credits)

    Fall Semester Course

    Fundamentals of deep learning, including both theory and applications. Topics include neural net architectures (MLPs, CNNs, RNNs, graph nets, transformers), geometry and invariances in deep learning, backpropagation and automatic differentiation, learning theory and generalization in high-dimensions, and applications to computer vision, natural language processing, and robotics.

    9.660 - Computational Cognitive Science (MIT: 12 units, HMS: 4 credits)

    Fall Semester Course

    Introduction to computational theories of human cognition. Focuses on principles of inductive learning and inference, and the representation of knowledge. Computational frameworks include Bayesian and hierarchical Bayesian models, probabilistic graphical models, nonparametric statistical models and the Bayesian Occam's razor, sampling algorithms for approximate learning and inference, and probabilistic models defined over structured representations such as first-order logic, grammars, or relational schemas. Applications to understanding core aspects of cognition, such as concept learning and categorization, causal reasoning, theory formation, language acquisition, and social inference. Graduate students complete a final project.

    15 .085 - Fundamentals of Probability (MIT: 12 units, HMS: 4 credits)

    Fall Semester Course 

    Introduction to probability theory. Probability spaces and measures. Discrete and continuous random variables. Conditioning and independence. Multivariate normal distribution. Abstract integration, expectation, and related convergence results. Moment generating and characteristic functions. Bernoulli and Poisson process. Finite-state Markov chains. Convergence notions and their relations. Limit theorems. Familiarity with elementary probability and real analysis is desirable.

    15.128 - Revolutionary Ventures: How to Invent and Deploy Transformative Technologies (MIT: 9 units, HMS: 4 credits)

    Fall Semester Course

    Seminar on envisioning and building ideas and organizations to accelerate engineering revolutions. Focuses on emerging technology domains, such as neurotechnology, imaging, cryotechnology, gerontechnology, and bio-and-nano fabrication. Draws on historical examples as well as live case studies of existing or emerging organizations, including labs, institutes, startups, and companies. Goals range from accelerating basic science to developing transformative products or therapeutics. Each class is devoted to a specific area, often with invited speakers, exploring issues from the deeply technical through the strategic. Individually or in small groups, students prototype new ventures aimed at inventing and deploying revolutionary technologies.

    15.136 - Principles and Practice of Drug Development (MIT: 9 units, HMS: 4 credits)

    Fall Semester Course

    Cross-listed with BCMP 230. MMSc-BMI students should register via 'BCMP 230'

    Description and critical assessment of the major issues and stages of developing a pharmaceutical or biopharmaceutical. Drug discovery, preclinical development, clinical investigation, manufacturing and regulatory issues considered for small and large molecules. Economic and financial considerations of the drug development process. Multidisciplinary perspective from faculty in clinical; life; and management sciences; as well as industry guests.

    15.871 - Introduction to System Dynamics (MIT: 6 units, HMS: 2 credits)

    Fall Semester Course

    Introduction to systems thinking and system dynamics modeling applied to strategy, organizational change, and policy design. Students use simulation models, management flight simulators, and case studies to develop conceptual and modeling skills for the design and management of high-performance organizations in a dynamic world. Case studies of successful applications of system dynamics in growth strategy, management of technology, operations, public policy, product development, and others. Principles for effective use of modeling in the real world. 

    20.201 - Fundamentals of Drug Development (MIT: 12 units, HMS: 4 credits)

    Fall Semester Course

    Team-based exploration of the scientific basis for developing new drugs. First portion of term covers fundamentals of target identification, drug discovery, pharmacokinetics, pharmacodynamics, regulatory policy, and intellectual property. Industry experts and academic entrepreneurs then present case studies of specific drugs, drug classes, and therapeutic targets. In a term-long project, student teams develop novel therapeutics to solve major unmet medical needs, with a trajectory to a "start-up" company. Culminates with team presentations to a panel of industry and scientific leaders.

    CSB .100 - Topics in Computational and Systems Biology (MIT: 12 units, HMS: 4 credits)

    Fall Semester Course

    Seminar based on research literature. Papers covered are selected to illustrate important problems and varied approaches in the field of computational and systems biology, and to provide students a framework from which to evaluate new developments. 

    HST 507 - Advanced Computational Biology: Genomes, Networks, Evolution (MIT: 12 units, HMS: 4 credits)

    Fall Semester Course

    Covers the algorithmic and machine learning foundations of computational biology, combining theory with practice. Principles of algorithm design, influential problems and techniques, and analysis of large-scale biological datasets. Topics include (a) genomes: sequence analysis, gene finding, RNA folding, genome alignment and assembly, database search; (b) networks: gene expression analysis, regulatory motifs, biological network analysis; (c) evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory. These are coupled with fundamental algorithmic techniques including: dynamic programming, hashing, Gibbs sampling, expectation maximization, hidden Markov models, stochastic context-free grammars, graph clustering, dimensionality reduction, Bayesian networks. Additionally examines recent publications in the areas covered, with research-style assignments. A more substantial final project is expected, which can lead to a thesis and publication.

    HST 525 - Tumor Microenvironment and Immuno-Oncology: A Systems Biology Approach (MIT: 6 units, HMS: 2 credits)

    Fall Semester Course

    Provides theoretical background to analyze and synthesize the most up-to-date findings from both laboratory and clinical investigations into solid tumor pathophysiology. Covers different topics centered on the critical role that the tumor microenvironment plays in the growth, invasion, metastasis and treatment of solid tumors. Develops a systems-level, quantitative understanding of angiogenesis, extracellular matrix, metastatic process, delivery of drugs and immune cells, and response to conventional and novel therapies, including immunotherapies. Discussions provide critical comments on the challenges and the future opportunities in research on cancer and in establishment of novel therapeutic approaches and biomarkers to guide treatment.

    HST .953 - Clinical Data Learning, Visualization, and Deployments (MIT: 12 units, HMS: 4 credits)

    Fall Semester Course

    Examines the practical considerations for operationalizing machine learning in healthcare settings, with a focus on robust, private, and fair modeling using real retrospective healthcare data. Explores the pre-modeling creation of dataset pipeline to the post-modeling "implementation science," which addresses how models are incorporated at the point of care. Students complete three homework assignments (one each in machine learning, visualization, and implementation), followed by a project proposal and presentation. Students gain experience in dataset creation and curation, machine learning training, visualization, and deployment considerations that target utility and clinical value. Students partner with computer scientists, engineers, social scientists, and clinicians to better appreciate the multidisciplinary nature of data science.