Course Descriptions

EPID 617: Social Epidemiology II: Social and Economic Determinants of Population Health - The objective of this course is to examine, in depth, some of the key social determinants of health in populations. The course is organized around substantive topic areas (e.g. obesity, disability, mental health, youth and substance abuse, stress and social support, neighborhoods and environments), with a focus on understanding the role of social factors in shaping health. The course draws heavily on epidemiologic perspectives and methods as tools to improve our understanding of population health, and is designed to expose students to different methodological approaches and their strengths/limitations in defining population health, understanding its determinants, and assessing the mechanisms by which these determinants influence population health. The course is a combination of lectures and student discussions, with an emphasis on class participation. 3 credits. H

HBHE 669: Genetics, Health Behavior, & Health Education - This course addresses the following topics: genetics and risk communication; ethical issues in genetics research; the psychological and behavioral impact of genetic testing; public and professional knowledge and attitudes about genetics; health education needs in genetics; and emerging issues in the field (e.g., computerized delivery of genetic counseling services). 3 credits. T, H

HBHE 715 Ethical, Legal, & Social Issues in Genomics and Health - This weekly seminar will address a wide range of ELSI issues involved in the following areas: implementation of genetic screening and testing in medical, public health and direct-to-consumer contexts; ethics of genetics research, including challenges around informed consent, data privacy, and return of individual research results; and legal and policy options for the regulation of genetic testing, genomic research, and precision medicine. This seminar is a requirement for fellows in the NIH-funded University of Michigan ELSI Research Training Program. 1.5 credits. D, H

LHS 671: Ethics and Policy Issues for Learning Health Systems - Policy and Ethics of Learning Health Systems II --- Policy and ethics shape the landscape and growth of health infrastructures. This course is designed to engage students in policy and ethical inquiry as they learn the theory, technology, and methods integral to health infrastructures and learning systems. Students in this seminar-style course will consider the policy and ethics of research methods, data science, and health infrastructures. 3 credits. D, H

BIOMEDE 458: Biomedical Instrumentation and Design – Students design and construct functioning biomedical instruments. Hardware includes instrumentation amplifiers and active filters constructed using operational amplifiers. Signal acquisition, processing analysis and display are performed. Project modules include measurement or respiratory volume and flow rates, biopotentials (electrocardiogram) and optical analysis of arterial blood oxygen saturation (pulse-oximetry). 4, Fall. D,H

BIOMEDE 499.060: AI in BME – The goal of this course is to introduce and apply Artificial Intelligence (AI) tools to problems in Biomedical Engineering. AI algorithms can learn patterns from biomedical data sets to provide actionable insights on disease diagnosis or treatment. This course will focus on practical applications of AI in BME with hands-on tutorials. This course will provide an overview of a wide range of AI and machine-learning tools (e.g. clustering, regression, decision trees, random forests and neural networks), biomedical data sets (imaging, omics, health records) and diseases (cancer, cardiovascular-, infectious- and brain diseases). 2, Winter. D,T,H

BIOSTAT 521: Applied Biostatistics - Fundamental statistical concepts related to the practice of public health: descriptive statistics; probability; sampling; statistical distributions; estimation; hypothesis testing; chi-square tests; simple and multiple linear regression; one-way ANOVA. Taught at a more advanced mathematical level than Biostat 503. Use of computer in statistical analysis. 4 credits. D

BIOSTAT 522: Biostatistical Analysis for Health-Related Studies - A second course in applied biostatistical methods and data analysis. Concepts of data analysis and experimental design for health-related studies. Emphasis on categorical data analysis, multiple regression, analysis of variance and covariance. 3 credits. D

BIOSTAT 650: Applied Statistics I: Linear Regression - Graphical methods, simple and multiple linear regression; simple, partial, and multiple correlation, estimation, hypothesis testing; model building and diagnosis; introduction to nonparametric regression; introduction to smoothing methods (e.g., lowess). The course will include applications to real data. 4 credits. D

BIOSTAT 699: Analysis of Biostatistical Investigations - Identifying and solving design and data analysis problems using a wide range of biostatistical methods. Written and oral reports on intermediate and final results of case studies required. D

BIOSTAT 881: Topics in Advanced Causal Inference - This course covers statistical theory and methodology for drawing causal conclusions from observational and experimental data. We will cover theoretical foundations including DAGs and SEMs, followed by special topics, which may include instrumental variable analysis, causal inference in high dimensions, and causal inference with longitudinal data. D

EECS 545: Machine Learning - This course will give a graduate-level introduction of machine learning and provide foundations of machine learning, mathematical derivation and implementation of the algorithms, and their applications. 3 credits. D

EPID 636: Cancer Risk and Epidemiology Modeling -This course will introduce 1) the concepts of multistage carcinogensis and the analysis of cancer epidemiology using mathematical models of carcinogenesis; 2) the analysis of cancer prevention strategies using Markov cancer natural history models.  Students will learn how to develop and fit multistage and cancer natural history models in R. D

HBEHED 662: Risk Communication: Theory, Techniques, and Applications in Health - This course will provide students with a theoretical and practical understanding of when and why people feel their health is "at risk." We focus on building students' ability to use evidence based techniques that can increase understanding and use of health data by patients, communities, the media, and policy makers. 3 credits. D, H

HS 650: Data Science and Predictive Analytics - This course aims to build computational abilities, inferential thinking, and practical skills for tackling core data scientific challenges. It explores foundational concepts in data management, processing, statistical computing, and dynamic visualization using modern programming tools and agile web-services. Concept, ideas, and protocols are illustrated through examples of real observational, simulated and research-derived datasets. Some prior quantitative experience in programming, calculus, statistics, mathematical models, or linear algebra will be necessary. 4 credits. D

HS 807: Management and Analysis of Large US Health Data - This course will provide students with a general overview of the principles, concepts, and methods of data management and analysis of large sources of national health data. This is a "doing" course and students will be guided through a collection of exercises designed to provide hands-on experience with a focus on leveraging publicly available data to answer health-related questions. Specific data sources covered in this course include: the National Health and Nutrition Examination Survey (NHANES); the Area Health Resource File; the Surveillance, Epidemiology, and End Results Program (SEER) Cancer Registry; the National Ambulatory, Medical Care Survey (NAMCS and NHAMCS); and the Medical Expenditure Panel Survey (MEPS). Students will become proficient at data management (importing/exporting, cleaning, and combining data files), analysis (basic descriptive measures and applied regression), and designing an analytic workflow in order to examine a relationship between an exposure and health outcome. Students will also be introduced to complex survey design methods, common approaches to building statistical models, and methods of risk adjustment. 4 credits, Fall. D,H

HS 852: Linear Modeling - This research methods course provides a foundation for using statistical methods in order to investigate health-related research questions. The course builds on concepts learned in prerequisite course HS 851 including univariate statistics; study design; data acquisition and management; and conceptual modeling. Modeling topics covered in the course include simple linear regressions, generalized linear models, and linear mixed models. Teaching methods include lectures, laboratory sessions, assigned readings, assignments, and critique of scientific papers. Assessment of the student's knowledge and understanding of the material will culminate in an empirical research project that addresses a research question using linear models. This is an applied graduate-level course and emphasizes the practical aspects of statistical methods. Prerequisite (Advisory): HS 851 or an equivalent course, or instructor permission. 4, Winter. D

IOE 574: Simulation Design & Analysis - Discrete event simulation for modeling and analysis. Development of simulations using a high-level programming language. Probabilistic and statistical aspects of simulation, including variate and process generation, variance reduction, and output analysis. Connections to stochastic models and queueing. Applications in services, healthcare, and manufacturing. 3 credits. D

IOE 691: Predictive Analytics for Interdisciplinary Research - The course provides a foundation in how to integrate multiple, diverse data sources to gain strong predictive accuracy and strong insights into challenging, interdisciplinary research problems. The course provides a foundation in semi-parametric and non-parametric predictive modeling, including ensemble-based methods. The focus is on how these models work and how to effectively leverage them for strong data analysis across multiple domains of application. There is a strong focus on model testing and validation, regularization, and implementation of models for prediction. Applications are taken from a variety of fields including risk analysis, operations research, civil engineering, climate science, public health, and disaster science. A major portion of the work in the course is a research-based term project in which students conduct and analysis, aiming for a conference or journal submission. D

LHS 610: Exploratory Data Analysis for Health - Students will learn foundational topics in data science and health information through hands-on work with real health datasets. Students will learn R, one of the most widely used languages for data science. The course contains two large themes: understanding health data and making inferences based on data. 3 credits. D

LHS 665: Applied Biostatistics for Health Researchers - This is a PhD-level biostatistics course that covers fundamental statistical concepts and methods for researchers who need to analyze health and/or healthcare data and interpret research. Major topics include descriptive statistics, probability theory, statistical inference, hypothesis testing, correlation, regression (linear and logistic), survival analysis, reliability/validity of diagnostic tests, and epidemiological study designs. 4 credits. D

LHS 668/SI 542/HMP 668: Introduction to Health Informatics - Introduction to concepts and practices of health informatics. Topics include: a) major applications and commercial vendors; b) decision support methods and technologies; c)analysis, design, implementation, and evaluation of healthcare information systems; and d) new opportunities and emerging trends. D, T, H

LHS 712: Natural Language Processing on Health Data - Students will learn advanced methods and techniques in text mining and natural language processing of health-related data, including electronic health records, published literature, and social media. Students will develop computational techniques to analyze different genres of health data, and build resources to search and extract relevant information from free text. 3 credits. D

MECHENG 555/MFG 555 – Design Optimization. Prerequisite: Math 451 and Math 217 or equivalent. Mathematical modeling of engineering design problems for optimization. Boundedness and monotonicity analysis of models. Differential optimization theory and selected numerical algorithms for continuous nonlinear models. Emphasis on the interaction between proper modeling and computation. Students propose design term projects from various disciplines and apply course methodology to optimize designs. 3 credits. D

SI 618: Data Manipulation and Analysis - This course aims to help students get started with their own data harvesting, processing, aggregation, and analysis. Data analysis is crucial to evaluating and designing solutions and applications, as well as understanding user's information needs and use. In many cases the data we need to access is distributed online among many webpages, stored in a database, or available in a large text file. Often these data (e.g. web server logs) are too large to obtain and/or process manually. Instead, we need an automated way of gathering the data, parsing it, and summarizing it, before we can do more advanced analysis. 3 credits. D

SI 670: Applied Machine Learning - Students will learn how to correctly apply, interpret results, and iteratively refine and tune supervised and unsupervised machine learning models to solve a diverse set of problems on real-world datasets. Application is emphasized over theoretical content. 3 credits. D

SI 671: Data Mining: Methods and Applications - This is a seminar course of advanced topics in data mining, the state-of-the-art methods to analyze different genres of information, and the applications to many real world problems. The course will highlight the practical applications of data mining instead of the theoretical foundations of machine learning and statistical computing. The course materials will focus on how the information in different real world problems can be represented as particular genres, or formats of data, and how the basic mining tasks of each genre of data can be accomplished using the state-of-the-art techniques. To this end, the course is suitable for those who are consumers of data mining techniques in their own disciplines, such as natural language processing, networks science, human computer interaction, economics, social computing, sociology, business intelligence, and biomedical informatics, etc. 3 credits. D

STATS 415: Data Mining and Statistical Learning - This course covers the principles of data mining, exploratory analysis and visualization of complex data sets, and predictive modeling. The presentation balances statistical concepts (such as over-fitting data, and interpreting results) and computational issues. Students are exposed to algorithms, computations, and hands-on data analysis in the weekly discussion sessions. 4 credits. D

STATS 500: Statistical Learning I: Regression - The course covers concepts and methods for regression analysis and applications. Topics include estimation, inference, interpretation of results, diagnostics, lack of fit, robust procedures, weighting and transformations, and model selection. The response variable could be continuous, binary or counts. More advanced techniques (splines, principal components analysis, and shrinkage estimators including ridge regression and Lasso) will also be covered. While there will be some theory, the emphasis will be on applications and data analysis. 3 credits, Fall. D

STATS 600 Regression Analysis - This is an advanced introduction to regression modeling and prediction, including traditional and modern computationally-intensive methods. It includes a comprehensive treatment of linear models for independent observations using least squares estimation; non least-squares approaches including penalization methods for variable selection; regression methods for dependent data, including generalized least squares, estimating equations, and mixed models; generalized linear models and generalized estimating equations; quantile regression, dimension reduction regression, and smoothing based methods. It also covers issues related to data collection, study design, and interpretation of findings, including missing data, non-representative samples, causality, and designed experiments. 4, Fall. D

SW864: Multilevel and Longitudinal Data Analysis - Longitudinal and Content Multilevel models have become a standard statistical tool for quantitative research on neighborhoods, communities and schools. Perhaps surprisingly, the multilevel model for cross-sectional data can easily accommodate longitudinal data where study participants are observed repeatedly. While this is sometimes not recognized, multilevel models for longitudinal data are closely related to other important longitudinal data models, such as fixed effects regression, an important technique for controlling for unobserved variables. This course focuses on the use of multilevel and longitudinal data analysis for social work research. 3 credits. D

HMP 615: Introduction to Public Health Policy - Introduction to the PH systems and policy issues PH practitioners face. Overview of public health policy interventions, theoretical motivations, influence of the political, bureaucratic, and social environments in which policy decisions are made, and population health consequences of such decisions. 3 credits. T, H

HMP 630: Business of Biology - The objective in this interdisciplinary graduate course is to explore the intersections between science, technology, commerce and social policy as they come together to advance (and in some cases retard) progress toward more-personalized health care. The course is intended for graduate students in medicine, biomedical and health-related science, public health, law, engineering, and business interested in the future of health care. 2 credits. H

PUBHLTH 513: Public Health Systems, Policy and Management - This course will introduce students to the public health system, public health policy development, and fundamental management concepts for managing public health organizations. Topics covered include organization, financing and history of public health, public health policy-making, advocacy, and basic principles of finance and human resource management in public health organizations. 3 credits. T, H

PUBHLTH 516: Leadership Skills for Interprofessional Practice - This course highlights foundational leadership skills needed by public health professionals to effectively work in interprofessional teams. Course themes include self-reflection on leadership style, growth mindset, fostering collaboration, motivating teams to accomplish goals, leading change, and guiding decision making. 1 credit. H

PUBHLTH 626: Understanding and Improving the US Healthcare System - Provides as asynchronous, engaging, and interactive way to understand the U.S. healthcare system and gain insight about the system. 1 credit. T, H

BIOMEDE 499.002: Clinical Observation and Needs Finding - In this course, students will observe nurses, technicians, surgeons, and physicians at the UM or VA Hospital, observing clinical practices in various medical specialties and settings. From these observations, students will identify important clinical problems and generate need statements based on their understanding. By the end of the term, students will assess the impact, marketability, and feasibility of solving these needs. 2 credits. D

BIOMEDE 561: Biological Micro-and Nanotechnology - Many life processes occur at small size-scales. This course covers scaling laws, biological solutions to coping with or taking advantage of small size, micro- and nanofabrication techniques, biochemistry, and biomedical applications (genomics, proteomics, cell biology, diagnostics, etc.). There is an emphasis on microfluidics, surface science, and non-traditional fabrication techniques. 3 credits. D

BIOMEDE 584/CHE 584/MSE 584: Advances in Tissue Engineering - Fundamentals engineering and biological principles underlying field of tissue engineering are studied, along with specific examples and strategies to engineering specific tissues for clinical use (e.g., skin). Student design teams propose new approaches to tissue engineering challenges. 3 credits. D 

BIOMEDE 588/CHE 588: Global Quality Systems and Regulatory Innovation - This course is for scientists, engineers, and clinicians to understand and interpret various relevant global and regional quality systems for traditional and cutting edge global health technologies, solutions and their implementation. Speakers from academia, the FDA, and biomedical related industries will be invited to participate in teaching this course. 2 credits. D

BIOMEDE 599.99: Systems Biology of Human Diseases - This course uses genetic and metabolic design principles to analyze healthy and diseased biological states by working to uncover the metabolic interactions between cancer cells and cells in neighboring tissue that support cancer growth and metastasis. 1-6 credits. D

BIOMEDE 599.011: Engineering Approaches to Cancer Biology - This course aims at designing, building and utilizing new experimental and computational tools to analyze and interpret multi-scale processes that regulate the behavior of human cells and tissues in response to perturbations such as cytokines, stress, cytotoxic and targeted drugs. 1-6 credits. D

CPTS 820: Clinical Translation in Pharmacokinetics - This course reviews the fundamental and practical aspects of absorption, distribution, metabolism, and excretion (ADME) for therapeutics and helps students strategize, plan and design translational research for drug dose design. 1 credit. D

CPTS 822: Research and Clinical Translation in Pharmacogenomics- This course focuses on methods for research and clinical translation of DNA (genetics and epigenetics) and RNA (transcriptomics) in precision pharmacotherapy, which we globally refer to as "pharmacogenomics". Students will learn research methods such as genomic data generation, analysis, and experimental models. Students will also learn methods for clinical translation such as genomics-driven clinical trials and how pharmacogenetics is currently used in clinical practice. 3 credits. D

CPTS 824: Clinical Pharmacy Translational Science - Pharmacoproteomics and pharmacometabolomics are critical to understanding pharmacokinetics and drug response and are important for the discovery and validation of biomarkers for precision pharmacotherapy. This course will introduce students to the basic theories, analytical methods, and data analysis approaches in pharmacoproteomics and pharmacometabolomics. 3 credits. D, T

EPID 515: Genetics in Public Health - This course is designed for students with biology or genetics background, that are interested in understanding genetics in public health. This course will provide an in depth examination of genetics in public health including newborn screening diseases and practices, fundamentals of population genetics, and the genetics of common chronic diseases. 3 credits. D, H

EPID 516: Genetic Epidemiology - This course relates genomics to the core public health discipline of epidemiology emphasizing the use of genomics to help describe disease frequency and distribution and to gain insights into biological etiologies. Topics include genetic material in disease, in families and in populations; the investigation of multifactorial traits; model-based linkage analysis; model-free linkage analysis; segregation analysis; allele association and linkage disequilibrium; and gene-gene interactions and gene-environment interactions. Issues related to implementing studies are considered. 4 credits. D, H

EPID 719 Methods in Genetic and Epigenetic Epidemiology - This course familiarizes students with general methods and principles of genetic and epigenetic epidemiology. The course seeks to integrate concepts in human genetics, population genetics, epidemiology, and biostatistics. The course will emphasize the practical applications of existing methods, which requires a critical evaluation of the scientific literature. Students are expected to be active participants in the course. Some of the topics to be included are population genetics, genetics of common diseases, gene-environment interaction, genetic and epigenetic association studies, and social epigenomics. Prerequisites: Introductory level course in epidemiology that covers study designs and measures of disease frequency and association.  Introductory level course in biostatistics that covers correlation and basic regression (e.g. linear and logistic regression) 1 credit. T, D, H

HUMGEN 541: Molecular Genetics - This course explores how the information content of the DNA genome is (i) organized, propagated, and altered, and (ii) functionally expressed by regulated transcription into RNA - the core molecular properties and processes of genetic systems that underlie all further investigations of organismal, clinical, and population genetics. 3 credits. D

HUMGEN 542: Molecular Basis of Human Genetics in Disease - This course will emphasize the principles and methods of genetics and molecular genetics as they relate to human disease. The course covers the topics of monogenic traits, cytogenetics, non-Mendelian inheritance, cancer genetics, and complex genetic disease. In each section, principles of genetics are presented by way of illustration of particular human genetic diseases or conditions. 3 credits. D

NUTR 630 Principles of Nutritional Sciences - This course presents foundational knowledge on nutritional metabolism of macronutrients. The digestion, absorption, transport, utilization and storage of macronutrients in humans are the focus. This course integrates biochemical and physiological aspects of nutrient utilization, interactions and metabolic regulation in humans. 3 credits. Fall. D

PHAR 647: Clinical Trials for Translational Scientists - In this multidisciplinary course students will design their own clinical trial by being part of a TO-T3 translational research team. Topics covered include trial design, ethical issues, managing the study team, study conduct, IRB and regulatory practice, protecting and respecting participants, managing data and data safety, and communicating findings. 2 credits. D, H

PHAR 640: Intro to Translational Science, Learning to Talk the Talk - This course introduces students to the full T1-T4 translational research spectrum, combining the expert presentation, clinical case study analysis and interactive small group discussion. Taught by a diverse team of UM faculty, this course provides a strong foundation for students who want to become leaders and innovators in translational research. 2 credits. D, H

PHRMACOL 503/MEDCHEM 503 Real-World Drug Discovery - U-M Department of Pharmacology, Life Sciences Institute, and Michigan Drug Discovery have developed a new course/program to provide relevant experience to trainees who are potentially interested in pursuing careers in pharma. Students will participate in “New Target Strategy Teams,” with each team researching and presenting their evaluation of a potential novel drug discovery project of interest to Michigan Drug Discovery.  These teams will be organized and run very similarly to how these same kinds of teams operate in “big pharma;” and trainees will be making an important contribution to real-world decisions regarding which potential projects Michigan Drug Discovery and its collaborators will invest resources into. 2 credits. D, T, H

BIOINF 524/525: Foundations in Bioinformatics - This course provides an introduction to the principles and practical approaches of bioinformatics as applied to genes and proteins. The overall course content is broken down into 3 sections focusing on foundational information, statistics, and systems biology, respectively. 3 credits. D

BIOINF 527: Introduction to Bioinformatics & Computational Biology - This course introduces students to the fundamental theories and practices of Bioinformatics and Computational Biology via a series of integrated lectures and labs. These lectures and labs will focus on the basic knowledge required in this field, methods of high-throughput data generation, accessing public genome-related information and data, and tools for data mining and analysis. The course is divided into four areas: Basics of Bioinformatics, Computational Phylogeny (includes sequence analysis), Systems Biology and Modeling. 4 credits. D

BIOINF 580: Introduction to Signal Processing and Machine Learning in Biomedical Sciences - The course covers signal processing and machine learning methods with an emphasis on their application in healthcare. Students will need a basic understanding in linear algebra for this course. 3 credits. D

BIOINF 585: Deep Learning in Bioinformatics - This project-based course is focused on deep learning and advanced machine learning in bioinformatics. The course will be comprised of deep learning and some other traditional machine learning in applications including regulatory genomics, health records, and biomedical images, and computation labs. 4 credits. D

BIOSTAT 626: Machine Learning for Health Sciences - This is a course introducing modern machine learning algorithms and data analytics for prediction, classification and data pattern recognition, with an emphasis on their applications in health data sciences. 3 credits. D

BIOSTATS 646/ BIOINF 545: High Throughput Molecular Genetic and Epigenetic Data Analysis - This course will cover basic analysis of microarrays, RNA-Seq, and ChIP-Seq data including hands-on lab sessions. The class also covers an introduction to the underlying biology and the technologies used for measuring RNA levels, transcription factor binding an epigenetic modifications, and quality control of microarray and deep sequencing data. 3 credits. D

EPID 708 Machine Learning for Epidemiologic Analysis in the Era of Big Data - The course focuses on advances in machine learning and its application to causal inference and prediction via a so-called Targeted Learning approach. These techniques allow the use of machine learning algorithms not just for prediction, but for estimating so-called causal parameters, such as average treatment effects, direct and indirect effects, dynamic treatments, optimal treatment regimes, etc. Targeted Learning provides the theoretical framework for deriving substitution estimators and rigorous statistical inference. Such techniques will become increasingly important in the era of Big Data, and the course will focus on the implementation of these techniques on existing data. The course will have a computer lab portion based on the R programming. Time permitting, we will discuss the implementation of approaches via cloud computing. This course is targeted towards more methods oriented epidemiologists and students. Prerequisites: Some background in R (can be obtained through EPID798 in this summer session) and working knowledge of regression and other standard statistical methodology common in basic epidemiological analysis. 1 credit. D, H

PHYSIOL/ BIOINF 520: Computational Systems Biology for Physiologists - This course provides an introduction to mathematical and computational modeling for both experimentally and theoretically inclined students, as well the currently employed strategies to investigate physiological problems with computational modeling. The course will focus on three major topics: Part 1: Introduction to Methods and Tools for Computational Modeling, Part 2: Cellular Electrophysiology, and Part 3: Biochemical Systems. 3, Winter. D

SI/HMP 661: Managing Health Informatics Managing Health Informatics - The course will prepare students to take on management challenges faced in health informatics leadership roles within a variety of organizational settings. It will be a highly interactive course in which students will have the opportunity to apply theory when discussing real-world health informatics scenarios from a variety of perspectives. 3 credits. H

IOE 513: Healthcare Operations Research: Theory and Applications - This course provides an overview of the role of operations research in healthcare. It surveys and evaluates research done in this field and addresses some of the key technical issues encountered when developing healthcare operations research models. Insights will be shared about carrying out collaborative research with healthcare professionals. 3 credits. T, H

IOE 813: Seminars in Healthcare Systems Engineering - Healthcare is critical to society and has a major impact on our economy. In this course, focused around weekly seminars by leading scholars in this important area, we provide a broad overview to ways systems engineering can improve the delivery of healthcare: decreasing costs, reducing error and developing innovations. 2 credits. T, H

LHS 611: Knowledge Representation and Management in Health - Important lessons about how to improve the health of individuals and populations are being learned everyday by a growing number of communities of interest. To apply these lessons broadly, communities of interest need efficient, effective means to represent and manage the new knowledge they generate. Considering the community of interest as a diverse convening, governing, discoursing, learning community made of knowledge generators and users, this course provides an intensive introduction to select social and technical methods to support community needs for representing and managing knowledge. 3 credits. T, H

LHS 621: Implementation Science in Health 1 - Dissemination and implementation sciences are important emerging disciplines. In this course, students learn and apply principles of dissemination and implementation sciences to problems related to healthcare practice and policy, including preparation of an implementation project. This course emphasized the fit between dissemination and implementation sciences and learning health cycles. 3 credits. D, T, H

LHS 650: Health Infrastructures Pro Seminar 1 - Health infrastructures connect networks, of people, organizations, and technologies at multiple levels of scale in physical and virtual spaces to improve the health of individuals and populations. This seminar examines theory and applied case studies to explore infrastructural thinking in the context of learning health system. 3 credits. T, H

LHS 660/SI 648/HMP 648: Research Methods for Learning Systems - Foundational introduction to empirical methods, both quantitative and qualitative, applicable to the study of health infrastructures and learning systems. Offers a broad overview that will enable students in the PhD program to begin formulating their interests into researchable problems, and make informed choices of the more advanced research methods courses they will need to pursue their research agenda. 3 credits. D

LHS 721: Implementation Science in Health 2 - Students will apply concepts learned in LHS 621 about how dissemination and implementation sciences fit into the LHS learning cycle, and apply practical skills to implement and evaluate complex interventions to improve health care. Students will complete a project implementing evidence-based practice and prepare reports for multiple audiences. 3 credits. D, T, H 

LHS 750: Health Infrastructures Pro Seminar 2 - Health infrastructures connect networks, of people, organizations, and technologies at multiple levels of scale in physical and virtual spaces to improve the health of individuals and populations. This seminar examines theory and applied case studies to explore infrastructural thinking in the context of learning health system. 2 credits. D, T, H

Path 862: Translational Pathology – A graduate-level course designed to help meet the growing need for scientists and medical professionals who can bridge the gap between basic science and clinical practice. This multi-disciplinary course trains both graduate students and clinical residents/fellows in the methods and principles involved in translating basic science findings into clinically useful interventions to improve human disease outcomes. The central objective is to illustrate how basic science applied to human disease can lead to the discovery of its pathophysiology, which in turn can be used to develop therapeutics and diagnostic tests. The course is taught from the perspective of the pathologist, wherein faculty experienced with successful translational research offer insight spanning: the nature and manifestation of human disease, the mechanisms of disease pathogenesis, chemical pathology and drug discovery/development, laboratory diagnostics, clinical trials, personalized medicine, and the newest technologies in these arenas. The target mixture of research and clinical trainees enriches the educational experience. 1 credit. D, T

SI 554/HBHE 654: Consumer Health Informatics - In this course, students will become familiar with a range of consumer health informatics (CHI) applications, including the needs/problems that the applications address, their theoretical bases, and their designs. Building on this prior CHI work, students will acquire an ability to evaluate existing applications, to use design techniques and skills for ideation, and to generate theory-informed design and implementation strategies for CHI applications. Students will also learn to assess the needs and technological practices of potential users, with a particular focus on groups that experience health and information access disparities. 3 credits. D, T, H

SI/HBHE 684: Designing Consumer Health Technologies - This course focuses on the design processes, theories, and evaluation methods that can help you construct and iteratively test high-quality consumer health technologies. The course covers prototyping techniques (creation of low and medium-fidelity prototypes, wizard of oz prototyping, and physical prototyping), psychological theories and constructs with direct applicability to consumer health technologies (e.g., behavioral economics, dual process models, operant conditioning), and evaluation techniques (e.g., single-case designs, micro-randomized trials) that can be used to do formative evaluation and optimization of consumer-health technologies. 3 credits. 

SI 669: Developing Mobile Experiences - Develop mobile applications using state of the art tools and platforms. Learn how to use standard testing, monitoring, and debugging tools to find and fix software bugs. Gain familiarity with other mobile app development approaches, UX principles and methods, and emerging mobile technologies such as wearables and Augmented Reality. 3 credits. D, T, H