Curriculum

Along with attending and participating in didactic weekly seminars and a research retreat, students will be encouraged to pursue computational or diabetes-related coursework and tap the variety of offerings in computer science, bioinformatics, statistics, math, and biomedicine at Harvard Medical School (HMS), Harvard School of Public Health (HSPH), and Harvard Faculty of Arts and Sciences (FAS). Funding will be available to subsidize tuition for one course per year. An non-exhaustive list is offered below:”

APCOMP 109A
Instructor: Protopapas
Department: Applied Computation
Offered in: Fall

Data Science 1

Data Science 1 is the first half of a one-year introduction to data science. The course will focus on the analysis of messy, real life data to perform predictions using statistical and machine learning methods. Material covered will integrate the five key facets of an investigation using data: (1) data collection – data wrangling, cleaning, and sampling to get a suitable data set; (2) data management – accessing data quickly and reliably; (3) exploratory data analysis ? generating hypotheses and building intuition; (4) prediction or statistical learning; and (5) communication ? summarizing results through visualization, stories, and interpretable summaries. Part one of a two part series. The curriculum for this course builds throughout the academic year. Students are strongly encouraged to enroll in both the fall and spring course within the same academic year. Part one of a two part series.

APMTH 120
Instructor: Tziperman
Department: Applied Mathematics
Offered in: Spring

Applied Linear Algebra and Big Data

Topics in linear algebra which arise frequently in applications, especially in the analysis of large data sets: linear equations, eigenvalue problems, linear differential equations, principal component analysis, singular value decomposition, data mining methods including frequent pattern analysis, clustering, outlier detection, classification, machine learning, modeling and prediction. Examples will be given from physical sciences, biology, climate, commerce, internet, image processing, economics and more. Recommended Prep: Applied Mathematics 21a,b or equivalent, Computer Science 50 or programming experience.

BCMP 218
Instructor: Irving London, George Daly, Vijay Sankaran
Department: Medical Sciences
Offered in: Fall

Molecular Medicine

Faculty mentors will guide student-led discussions of the papers. A seminar on various human diseases and their underlying genetic or biochemical bases. Primary scientific papers discussed. Lectures by faculty and seminars conducted by students, faculty supervision.

BCMP 234
Instructor: Michel
Department: Medical Sciences
Offered in: Spring

Cellular Metabolism and Human Disease

Cellular and organismal metabolism, with focus on interrelationships between key metabolic pathways and human disease states. Genetic and acquired metabolic diseases and functional consequences. Interactive lectures and critical reading conferences are integrated with clinical encounters. Course Notes: Enrollment may be limited. Recommended Prep: For Undergraduate students only: Knowledge of introductory biochemistry, genetics, and cell biology required (MCB 52 and 54 or equivalent); one year of organic chemistry.

BCMP 310QC/GENETIC 216
Instructor: Winston
Department: Medical Sciences
Offered in: Spring

Advanced Topics in Gene Expression

This course covers different topics in gene regulation, covering genetic, genomic, biochemical, and molecular approaches. A small number of topics are discussed in depth, using the primary literature. Topics range from prokaryotic transcription to eukaryotic development. Course Notes:
BCMP 310QC has merged with Genetics 216. Offered jointly with the Medical School as GN 703.0.
Recommended Prep: BCMP 200 and Genetics 201. All students taking Genetics 216 should read and be prepared to discuss the papers for the first meeting on January 24. The readings can be downloaded from the course web site.

Biophys 205
Instructor: Martha Bulyk
Department: Biophysics
Offered in: Spring

Computational and Functional Genomics

Experimental functional genomics, computational prediction of gene function, and properties and models of complex biological systems. The course will primarily involve critical reading and discussion rather than lectures.

Biophysics 170
Instructor: Leonid Mirny, Shamil Sunyaev, Cheng-Zhong Zhang
Department: Biophysics
Offered in: Fall

Quantitative Genomics

1. Evolutionary and Population Genetics; 2. Comparative Genomics; 3. Structural Genomics and Proteomics; 4. Functional Genomics and Regulation. Each module consists of 4 lectures providing key background material, 1 lecture providing clinical correlates and 1 guest lecture from leaders in the field.

BMI 701
Instructor: Adam Wright
Department: Biomedical Informatics
Offered in: Fall

Foundations of Biomedical Informatics

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 702
Instructor: Adam Wright
Department: Biomedical Informatics
Offered in: Spring

Foundations of Biomedical Informatics II

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 704
Instructor: Chirag Patel
Department: Biomedical Informatics
Offered in: Spring

Data Science I: Integrating Clinical, Genomic & Environmental Data

There is a need to prepare translational informatics researchers to repurpose large and observational biomedical population-based datasets for data-driven discovery. These datasets may include, but are not limited to observational epidemiological cohorts, medical health claims, and electronic medical records, for biomedical discovery. This course will survey the current data and methodological approaches to conduct integrative high-throughput investigation merging genomic, exposomic, and phenomic modalities to find new associations in disease and prevention. Students will be encouraged to find publicly available population data, such as those available from the Databases of Genotypes and Phenotypes, US Centers for Disease Control and Prevention, and USAID. Students may be given sample de-identified extracts from medical claims or electronic health record datasets. Students will also be encouraged to formulate a research paper for submission to a journal or as a proceedings article.

BMI 705
Instructor: Paul Avillach
Department: Biomedical Informatics
Offered in: Fall

Precision Medicine II: Integrating Clinical and Genomic Data

The real value in biomedical research lies not in the scale of any single source of data, but in the ability to integrate and interrogate multiple, complementary datasets simultaneously. This course will dive into methods about 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 in these cohorts vast and multiple types of high-throughput phenotypic and genotypic data. The students will use those tools during the problem sets with real patient data to learn the process towards making new biomedical discoveries.

BMI 706
Instructor: Nils Gehlenborg
Department: Biomedical Informatics
Offered in: Spring

Data Science II: Data Visualization

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 713
Instructor: Peter Park
Department: Biomedical Informatics
Offered in: Fall

Computational Statistics for Biomedical Sciences

This course will provide a practical introduction to analysis of biological and biomedical data. Basic statistical techniques will be covered, including descriptive statistics, elements of probability, hypothesis testing, nonparametric methods, correlation analysis, and linear regression. Emphasis will be on how to choose appropriate statistical tests and how to assess statistical significance. To visualize data and carry out statistical testing, students will learn R, a powerful programming language for statistical computing and graphics. No previous knowledge in statistics or programming is required, although those with no programming experience will be expected to devote a significant amount of extra time. The class will be a combination of lecture and computer lab. This course is geared toward graduate students, but postdoctoral fellows and others are welcomed as space allows.

BMI 726
Instructor: John Brownstein, Kenneth Mandl, Ben Reis
Department: Biomedical Informatics
Offered in: Spring

Big Data Innovations in Population Health

Can we predict epidemics? Do doctors and public health officials need to know what is trending on Google or Twitter? Can computers warn us about dangerous drugs? What insights will emerge from the nearly one trillion dollar investment in electronic health records? This course introduces the cutting-edge ?Big Data? approaches that are transforming medicine at the population level. Learn from internationally recognized innovators who are developing intelligent public health systems to power the healthcare of tomorrow. Core faculty and guest experts will cover topics including the use of big data in precision medicine, designing a learning healthcare system, biosurveillance, pharmacovigilance and patient-centered epidemiological methods. The course will focus both on the needs of public health departments as well as health delivery systems for real time characterization of populations. This class is appropriate for those seeking an overview or planning a research career in informatics, epidemiology, health services research, disease modeling or public health. The course is geared toward graduate students, but interested undergraduates and postdoctoral fellows are welcomed as space allows.

BMI 728
Instructor: Leo Anthony Gutierrez Celi
Department: DBMI
Offered in: Spring

Global Health Informatics

This course is taught by speakers who are internationally recognized experts in the field of global health informatics and who, with their operational experiences, will outline the challenges they faced and describe how they were addressed. This offering is a continuation of our overall objective to study the development of innovations in information systems to accelerate improvements in health outcomes for developing countries. The main themes of the course are: (1) identifying key challenges and real problems, (2) design paradigms and approaches, and (3) evaluation and the challenges in measuring impact. This course is structured as a project-based course, with students working in small teams collaborating with international partners and universities on actual informatics projects and implementations. Students will work in multidisciplinary international teams and gain hands-on experience facing the challenges of implementing information technologies and working in resource poor settings. Weekly lectures led by internationally recognized experts in the field will be supplemented by a practicum of intensive lab hours focused on the team projects guided by mentors from a diversity of backgrounds including public health, engineering, informatics, and business.

BMI 741
Instructor: Adam Landman
Department: Biomedical Informatics
Offered in: Spring

Health Information Technology: From Ideation to Implementation

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.

BST 227
Instructor: Aryee
Department: Biostatistics
Offered in: Fall

Introduction to Statistical Genetics

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 homeworks supplement 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) or PHS 2000A (concurrent enrollment allowed)Course Note:There will be a weekly lab section; the time will be scheduled at first meeting.Formerly BIO227

BST 228
Instructor: Zigler
Department: Biostatistics
Offered in: Fall

Applied Bayesian Analysis

This course is a practical introduction to the Bayesian analysis of biomedical data. It is anintermediate Master’s level course in the philosophy, analytic strategies, implementation, and interpretation of Bayesian data analysis. Specific topics that will be covered include: the Bayesian paradigm; Bayesian analysis of basic models; Bayesian computing: Markov Chain Monte Carlo; STAN R software package for Bayesian data analysis; linear regression; hierarchical regression models; generalized linear models; meta-analysis; models for missing data.Programming and case studies will be used throughout the course to provide hands-on training in these concepts.Prerequisites: BST210 and BST222, or permission of the instructor

BST 235
Instructor: Coull
Department: Biostatistics
Offered in: Fall

Advanced Regression & Stat. Learning

An advanced course in linear models, including both classical theory and methods for high dimensional data. Topics include theory of estimation and hypothesis testing, multiple testing problems and false discovery rates, cross validation and model selection, regularization and the LASSO, principal components and dimensional reduction, and classification methods. Background in matrix algebra and linear regression required.Prerequisite: BST 231 and BST 233, or permission of instructor required.Formerly BIO235

BST 245
Instructor: Haneuse
Department: Biostatistics
Offered in: Spring

Multivariate and Longitudinal Data

The multivariate normal distribution, Hotelling’s T2, MANOVA, repeated measures, the multivariate linear model, random effects and growth curve models, generalized estimating equations, multivariate categorical outcomes, missing data, computational issues for traditional and new methodologies.

BST 249
Instructor: Trippa
Department: Biostatistics
Offered in: Fall

Bayesian Methodology in Biostatistics

General principles of the Bayesian approach, prior distributions, hierarchical models and modeling techniques, approximate inference, Markov chain Monte Carlo methods, model assessment and comparison. Bayesian approaches to GLMMs, multiple testing, nonparametrics, clinical trials, survival analysis.

BST 260
Instructor: Irazarry
Department: Biostatistics
Offered in: Fall

Introduction to Data Science

This class focuses on methods for learning from data, in order to gain useful predictions and insights. Separating signal from noise presents many computational and inferential challenges, which we approach from a perspective at the interface of computer science and statistics. Through real-world examples of wide interest, we introduce methods for five key facets of an investigation:1) data munging/scraping/sampling/cleaning in order to construct an informative, manageable data set;2) software engineering skills for accessing data as well as organizing data analyses and making these analyses sharable and reproducible and3) exploratory data analysis to generate hypotheses and intuition about the data;4) inference and prediction based on statistical tools such as modeling,regression, and classification;5) communication of results through visualization, stories, and interpretable summaries. Formerly BIO260

BST 261
Instructor: Mattie
Department: Biostatistics
Offered in: Spring

Data Science II

This course is the second course in the foundational sequence of the School?s newly approved Master?s Degree in Health Data Science. The course will build upon our existing course, BST260 ?Introduction to Data Science?, in presenting a set of tools for modeling and understanding complex datasets. Specifically, the course will provide practical regression and tree-based techniques for big data. Specific topics that will be covered include: linear model selection and regularization: LASSO and regularization; principal component regression and partial least squares; tree-based methods: decision trees; bagging, random forests, and boosting; unsupervised learning: principal components analysis, cluster analysis.Programming (Python and R) and case studies will be used throughout the course to provide hands-on training in these concepts.Prerequisites: BST260 or permission of instructor

BST 262
Instructor: Choirat
Department: Biostatistics
Offered in: Fall

Computing for Big Data

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 263
Instructor: Miller
Department: Biostatistics
Offered in: Spring

Applied Machine Learning

Statistical machine learning is a collection of flexible tools and techniques for using data to construct prediction algorithms for decision problerns and exploratory analysis. The central theme of the course will be to ground the material in practical real-world data examples, in order to motivate the concepts and illustrate why and how the methods work. Some mathematical foundations will be covered, but the primary emphasis of the course will be on learning how to implement and use the methods, while gaining an intuitive understanding of them. Programming (in R) and case studies will be used throughout the course to provide hands-on training. Prerequisites: BST 260 or BST 210 or BST 232.

BST 267
Instructor: Onnela
Department: Biostatistics
Offered in: Fall

Intro. to Social and Biological Networks

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 NetworkX 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 ID201 or (BST202 & 203) or [BST206 & (BST207 or 208)]Formerly BIO521

BST 270
Instructor: Mattie
Department: Biostatistics
Offered in: Fall

Reproducible Data Science

The central theme of the course will be to meet these scientific needs of reproducible science through training in reproducible research. The topics covered in this course include the fundamentals of reproducible science, case studies in reproducible research, data provenance, statistical methods for reproducible science, and computational tools for reproducible science. This is a blended course where students are introduced to course content online through videos and reading assignments, and then discuss the content in lecture. Each student will submit a completely reproducible research project and give a short presentation at the end of the course.

BST 280
Instructor: Quackenbush
Department: Biostatistics
Offered in: Fall

Intro. Genomics/Bioinformatics

This survey course is intended for a wide audience and will provide an introduction to genomics-inspired techniques and bioinformatics tools, including genome sequencing, DNA microarrays, proteomics, and publicly available databases and software tools.Course Note: Lab or section times to be announced at first meeting.Course Prerequisites: ID201 or [BST201 or (BST202 & 203) or (BST206 & (BST207 or 208)) and (EPI201 or EPI500)], or permission of instructor. Courses may be taken concurrently.Formerly BIO292

BST 281
Instructor: Huttenhower
Department: Biostatistics
Offered in: Spring

Genomic Data Manipulation

Introduction to genomic data, computational methods for interpreting these data, and survey of current functional genomics research. Covers biological data processing, programming for large datasets, high-throughput data (sequencing, proteomics, expression, etc.), and related publications.

BST 282
Instructor: Liu
Department: Biostatistics
Offered in: Spring

Intro. to Comp. Bio. and Bioinformatics

Basic biological problems, genomics technology platforms, algorithms and data analysis approaches in computational biology. There will be three major components of the course: microarray and RNA-seq analysis, transcription and epigenetic gene regulation, cancer genomics.This course is targeted at both biostatistics and biological science graduate students with some statistics and computer programming background who have an interest in exploring genomic data analysis and algorithm development as a potential future direction.

BST 283
Instructor: Carter
Department: Biostatistics
Offered in: Spring

Cancer Genome Analysis

This course is an introduction to modern statistical computing techniques used to characterize and interpret cancer genome sequencing datasets. This Master’s level course will begin with a basic introduction to DNA, genes, and genomes for students with no biology background. It will then introduce cancer as an evolutionary process and review landmarks in the history of cancer genetics, and discuss the basics of sequencing technology and modern Next Generation Sequencing. The course will cover the main steps involved in turning billions of short sequencing reads into a representation of the somatic genetic alterations characterizing an individual patient?s cancer, and will build on this foundation to study topics related to identifying mutations under positive selection from multiple tumors sampled in a population.By the end of the course, students will be able to apply state-of-the art analysis to cancer genome datasets and to critically evaluate papers employing cancer genome data.

CELLBIO 307QC
Instructor: Danesh Moazed, Raul Mostoslavsky
Department: Medical Sciences
Offered in: Spring

Molecular Aspects of Chromatin Dynamics

This course will discuss the role of chromatin dynamics in modulating molecular and cellular processes. The genetic information encoded in our DNA is organized in a defined set of chromosomes, which are condensed about 10.000 fold in order to fit in the cell nucleus. This compaction occurs through packaging of the DNA around histone proteins, a structure known as chromatin. In what was thought to be a rigid structure, today we know that chromatin is an amazingly dynamic folding that plays a crucial role in controlling accessibility of factors to the DNA, and as such, it regulates a vast number of critical biological functions, including gene transcription, DNA replication, DNA repair and cellular identity. In this course we will attempt to cover some of the basic molecular mechanisms that play a role in regulating chromatin dynamics, and in turn how chromatin itself modulate biological processes, including basic mechanisms of inheritance. We will specifically discuss the role of DNA methylation, histone modifications, nucleosome dynamics and novel epigenetic modulators in the context of different biological processes for which chromatin accessibility appears to play a crucial role.

COMPSCI 181
Instructor:
Department: Computer Science
Offered in: Spring

Machine Learning

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.

COMPSCI 222
Instructor: TBA
Department: Computer Science
Offered in: Fall

Algorithms at Ends of the Wire

Covers topics related to algorithms for big data, especially related to networks. Themes include compression, cryptography, coding, and information retrieval related to the World Wide Web. Requires a major final project.

COMPSCI 281
Instructor: Rush
Department: Computer Science
Offered in: Fall

Advanced Machine Learning

Advanced statistical machine learning and probabilistic data analysis. Topics include: Variational inference, graphical models, deep learning, text modeling, unsupervised learning, dimensionality reduction and visualization. Requires a major final project. Recommended Prep: Students should feel comfortable with linear algebra and probability theory. Students will be expected to implement algorithms in Python.

COMPSCI 289
Instructor: Nagpal
Department: Computer Science
Offered in: Fall

Biologically-inspired Multi-agent Systems

Surveys biologically-inspired approaches to designing distributed systems. Focus is on algorithms, analysis, and programming paradigms. Topics: swarm intelligence, amorphous computing, immune-inspired systems, synthetic biology. Discussion of research papers and a research project required. Course Notes: Geared toward graduate students of all levels as well as advanced undergraduates. Preference given to graduate students or upper-level concentrators. Recommended Prep: Experience with algorithms (e.g. Computer Science 124) and programming (e.g. Computer Science 51).

ENG-SCI 150
Instructor: Yue Lu
Department: Engineering Sciences
Offered in: Spring

Introduction to Probability with Engineering Applications

This course introduces students to probability theory and statistics, and their applications to physical, biological and information systems. Topics include: random variables, distributions and densities, conditional expectations, Bayes’ rules, laws of large numbers, central limit theorems, Markov chains, Bayesian statistical inferences and parameter estimations. The goal of this course is to prepare students with adequate knowledge of probability theory and statistical methods, which will be useful in the study of several advanced undergraduate/graduate courses and in formulating and solving practical engineering problems.

EPI 215
Instructor: Chibnik
Department: Epidemiology
Offered in: Fall

Adv. Topics in Case-Control and Cohort Studies

This course primarily extends the applications of parametric regression models covered in EPI204 to address additional and related analytic issues encountered in epidemiologic research. Topics include techniques for modeling continuous and polytomous exposures, methods to account for missing data, doubly-robust modeling, and issues involved in high dimensional data analysis, building and assessing, risk prediction models, and sample size calculations. Emphasis is on applications of interpretations of results with limited introduction to theory that underlies these techniques. Familiarity with SAS is desirable.Course Prerequisites: EPI204 required

EPI 240
Instructor: Tworoger
Department: Epidemiology
Offered in: Spring

Biomarkers in Epidemiology Research

None Available

EPI 249
Instructor: De Vivo
Department: Epidemiology
Offered in: Fall

Molecular Biology for Epidemiologists

Molecular Biology for Epidemiologists, taught by Dr. Immaculata De Vivo, offers an overview of fundamental molecular biology concepts and techniques commonly used in the laboratory and in epidemiological research. During the term, we will cover a broad range of topics including, but not limited to, the mechanisms and regulatory processes involved in different steps of the central dogma of molecular biology, how cellular mechanisms go awry and how these cells can be repaired, Mendelian and non-Mendelian genetics, meiosis, mitosis, and both novel and classical molecular biology tools. This course will be of most interest to those who have not taken a recent college-level course in molecular biology, or equivalent.

EPI 293
Instructor: Liang
Department: Epidemiology
Offered in: Winter

Analysis of Genetic Association Studies

At the end of this course students will grasp Concept and Theory, Methods and Software Tools needed to critically evaluate and conduct genetic 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 genetics/genomics and epigenetics/epigenomics.Course note: Familiarity with SAS or S-PLUS/R and UNIX computing environment are highly recommended. Students are encouraged to discuss course prerequisites with the instructor.Course Prerequisite(s): BST201 and (BST210 or BST213 or EPI204) and (ID200 or EPI200 or EPI201 or EPI505 or EPI500 or ID201); may not be taken concurrently. Students outside of HSPH must request instructor permission to enroll in this course

EPI 507
Instructor: Kraft
Department: Epidemiology
Offered in: Fall

Genetic Epidemiology

Introduces the basic principles and methods of genetic epidemiology. After a brief review of history of genetic epidemiology, methods for the study of both high penetrance and low penetrance alleles, as well as other high throughput genomic data will be described and discussed. Methods of analysis of genome-wide association studies are a particular focus. Examples of contribution of genetic analysis to major diseases will be reviewed.

GENETIC 228
Instructor: Christopher Newton-Cheh
Department: Medical Sciences
Offered in: Spring

Genetics in Medicine – From Bench to Bedside

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, bioinformatic 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.

HarvardX
Instructor: Huttenhower, Quackenbush, Trippa, Choirat
Department:
Offered in: TBA

Principles, Statistical and Computational Tools for Reproducible Science

Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and communicate them to others.

ID 271
Instructor: Schwartz
Department: Environmental Health
Offered in: Spring

Adv. Regression for Environmental Epi

This course covers applied advanced regression analysis. Its focus is on relaxing classical assumptions in regression analysis to better match what epidemiological data really looks like. Specifically, the course will cover nonlinear exposure-response relationships and repeated measure designs, including non-parametric and semi-parametric smoothing techniques, generalized additive models, and time series models. In addition to the theoretical material, students will apply these techniques using R to actual datasets including modeling the effects of environmental exposures on health outcomes. These techniques also are widely applicable to problems in infectious disease, psychiatric, nutritional, occupational, and cancer epidemiology. Basic biostatistics and a course in regression analysis recommended.

IMMUN 201
Instructor: Mempel
Department: Medical Sciences
Offered in: Fall

Principles of Immunology

Comprehensive core course in basic immunology, providing an intensive and in-depth examination of the cells and molecules of the immune system. Special attention is given to the experimental approaches that led to the discovery of the general principles of immunology. Intended for students who have had prior exposure to immunology on the undergraduate level. In the absence of such exposure, students must obtain the permission of the Course Director. Offered jointly with the Medical School as IM 702.0.

IMMUN 324
Instructor: Benoist
Department: Medical Sciences
Offered in: Fall/Spring

Systems Immunology of Tolerance and Autoimmunity

None Available

MATHEMATICS 243
Instructor: Nowak
Department: Mathematics
Offered in: Spring

Evolutionary Dynamics

Advanced topics of evolutionary dynamics. Seminars and research projects.

MCB 169
Instructor: Pallai
Department: Molecular and Cellular Biology
Offered in: Fall

Molecular and Cellular Immunology

The immune system is frontier at which molecular biology, cell biology, and genetics intersect with the pathogenesis of disease. The course examines in depth the cellular and molecular mechanisms involved in the development and function of the immune system and also analyzes the immunological basis of human disease including AIDS and other infectious diseases, autoimmune disorders, allergic disorders, primary immunodeficiency syndromes, transplantation, and cancer. Recommended Prep: Genetics and cell biology strongly recommended. Course Requirements: Prerequisite: LPS A OR LS 1a

OEB 230
Instructor: Mallet
Department: Organismic and Evolutionary Biology
Offered in: Spring

Comparative Genomics

This discussion-based course will survey modern ideas about evolution and speciation, and how they have changed as a result of genomic approaches. As well as readings and discussions in class, the course will utilize some live online video sessions with major players in the field of evolutionary and comparative genomics.

STAT 110
Instructor: Blitzstein
Department: Statistics
Offered in: Fall

Introduction to Probability

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 111
Instructor: Kevin A. Rader
Department: Statistics
Offered in: Spring

Intro to Theoretical Stats

Basic concepts of statistical inference from frequentist and Bayesian perspectives. Topics include maximum likelihood methods, confidence and Bayesian interval estimation, hypothesis testing, least squares methods and categorical data analysis.

STAT 120
Instructor: Jun Liu
Department: Statistics
Offered in: Fall

Introduction to Bayesian Inference and Applications

An introduction to statistical inference under the Bayesian paradigm. Applications include a variety of classic and modern models for high-dimensional, time series and spatial data. Evaluation techniques for modeling assumptions and inference strategies. Hands-on implementation of estimation and inference procedures in R. Knowledge of R programming is assumed.

STAT 171
Instructor: Pillai
Department: Statistics
Offered in: Spring

Introduction to Stochastic Processes

An introductory course in stochastic processes. Topics include Markov chains, branching processes, Poisson processes, birth and death processes, Brownian motion, martingales, introduction to stochastic integrals, and their applications.

STAT 210
Instructor: Blitzstein
Department: Statistics
Offered in: Fall

Probability 1

Random variables, measure theory, reasoning by representation. Families of distributions: Multivariate Normal, conjugate, marginals, mixtures. Conditional distributions and expectation. Convergence, laws of large numbers, central limit theorems, and martingales.

STAT 211
Instructor: Dasgupta
Department: Statistics
Offered in: Fall

Statistical Inference

Inference: frequency, Bayes, decision analysis, foundations. Likelihood, sufficiency, and information measures. Models: Normal, exponential families, multilevel, and non-parametric. Point, interval and set estimation; hypothesis tests. Computational strategies, large and moderate sample approximations.

STAT 215
Instructor: Shirley Liu
Department: Statistics
Offered in: Spring

Introduction to Computational Biology and Bioinformatics

Meets with Statistics 115, but graduate students are required to do more coding, complete a research project and submit a written report during reading period in addition to completing all work assigned for Statistics 115.

STAT 220
Instructor: Jun Liu
Department: Statistics
Offered in: Spring

Bayesian Data Analysis

Basic Bayesian models, followed by more complicated hierarchical and mixture models with nonstandard solutions. Includes methods for monitoring adequacy of models and examining sensitivity of models. Note: Emphasis throughout term on drawing inferences via computer simulation rather than mathematical analysis.

STAT 330
Instructor: N/A
Department: Statistics
Offered in: N/A

High Dimensional Analysis

None Available

STAT 365R
Instructor: Blitzstein
Department: Statistics
Offered in: Spring

Philosophical Foundations of Statistics

Controversies, paradoxes, fallacies, and philosophical issues in the foundations of probability and statistics. Bayesian vs. frequentist vs. fiducial; objective vs. subjective; design-based vs. model-based; low assumption vs. high assumption; robustness vs. relevance; population inference vs. individualized prediction.

SYSBIO 305QC
Instructor: Silver
Department: Systems Biology
Offered in: TBD

Practical Synthetic Biology

Synthetic biology is a new discipline that seeks to enable the predictable engineering of biological systems. According to one conception of synthetic biology, proteins and genetic regulatory elements are modular and can be combined in a predictable manner. In practice however, assembled genetic devices do not function as expected. The purpose of the course is to go beyond the textbook, first-pass description of molecular mechanisms and focus on details that are specifically relevant to engineering biological systems.

SYSBIO204
Instructor: Yin
Department: Systems Biology
Offered in: Fall

Biomolecular Engineering and Synthetic Biology

A course focusing on the rational design, construction, and applications of nucleic acid- and protein-based synthetic molecular and cellular machinery and systems. Students are mentored to produce substantial term projects.

XMIT 15.136J
Instructor: T.J. Allen
Department: Management
Offered in: Fall

Principles and Practice of Drug Development

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.

XMIT 18.05
Instructor: D.A. Vogan
Department: Mathematics
Offered in: Spring

Introduction to Probability and Statistics

Elementary introduction with applications. Basic probability models. Combinatorics. Random variables. Discrete and continuous probability distributions. Statistical estimation and testing. Confidence intervals. Introduction to linear regression.

XMIT 18.337
Instructor: N/A
Department: Mathematics
Offered in: N/A

Parallel Computing

N/A

XMIT 18.418
Instructor: Bonnie Berger
Department: Mathematics
Offered in: Spring

Topics in Computational Molecular Biology

Covers current research topics in computational molecular biology. Recent research papers presented from leading conferences such as the SIGACT International Conference on Computational Molecular Biology (RECOMB). Topics include original research (both theoretical and experimental) in comparative genomics, sequence and structure analysis, molecular evolution, proteomics, gene expression, transcriptional regulation, and biological networks. Recent research by course participants also covered. Participants will be expected to present either group or individual projects to the class.

XMIT 20.201
Instructor: P. C. Dedon, M. A. Murcko, R. Sasisekharan
Department: Biological Engineering
Offered in: Fall

Fundamentals of Drug Development

Addresses the scientific basis for the development of new drugs. First half of term begins with an overview of the drug discovery process, followed by fundamental principles of pharmacokinetics, pharmacodynamics, metabolism, and the mechanisms by which drugs cause therapeutic and toxic responses. Second half applies principles to case studies and literature discussions of current problems with specific drugs, drug classes, and therapeutic targets.

XMIT 7.548
Instructor: J. C. Love, A. Sinskey, S. Springs
Department: Biology
Offered in: Spring

Advances in Biomanufacturing

Seminar examines how biopharmaceuticals, an increasingly important class of pharmaceuticals, are manufactured. Topics range from fundamental bioprocesses to new technologies to the economics of biomanufacturing. Also covers the impact of globalization on regulation and quality approaches as well as supply chain integrity. Students taking graduate version complete additional assignments.

XMIT HST.507
Instructor: M. Kellis
Department: Health Sciences and Technology
Offered in: Fall

Advanced Computational Biology: Genomes, Networks and Evolution

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.

XPBH BIO222
Instructor: Wypij
Department:
Offered in: Fall

Basics Statistical Inference

This course will provide a basic, yet thorough introduction to the probability theory and mathematical statistics that underlie many of the commonly used techniques in public health research. Topics to be covered include probability distributions (normal, binomial, Poisson), means, variances and expected values, finite sampling distributions, parameter estimation (method of moments, maximum likelihood), confidence intervals, hypothesis testing (likelihood ratio, Wald and score tests). All theoretical material will be motivated with problems from epidemiology, biostatistics, environmental health and other public health areas. This course is aimed towards second year doctoral students in fields other than Biostatistics. Background in algebra and calculus required.

XPBH EPI293
Instructor: Liang
Department:
Offered in: Spring

Analysis of Genetic of Association Studies

At the end of this course students will grasp Concept and Theory, Methods and Software Tools needed to critically evaluate and conduct genetic 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 genetics/genomics and epigenetics/epigenomics.