Explore Our Workshops Hands-on workshops to enhance your research skills.
Our workshops offer practical, interactive learning led by experts in epidemiologic research. Each session is designed to provide actionable insights, hands-on experience, and tools you can apply immediately in your work. Whether you’re looking to strengthen core skills or explore emerging methods, these workshops deliver focused training to help you advance your research.
2026 - In Person Workshops
Join us onsite for interactive workshops that bring learning to life. These sessions are held in person during the SER Annual Meeting, offering hands-on training and direct engagement with experts and peers.
Workshop Description:
This workshop will introduce participants to the Causal Roadmap for generating real-world evidence of cause-and-effect in health studies:
1) clear statement of the research question;
2) specification of the causal model and causal effect of interest;
3) assessment of identifiability to link the causal effect to a parameter estimable from the observed data;
Workshop Description:
Estimating equations are a powerful tool set for epidemiologists, with many familiar estimators being special cases. Importantly for epidemiologists, estimating equations allow for the simultaneous estimation of parameters. This enables a streamlined approach to estimate the variance and compute confidence intervals.
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In many Schools of Public Health, the art and science of getting your first grant is part of the course sequence. What’s less well taught is what comes next how to launch, sustain, and ultimately wrap up your first major research grant.
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Causal mediation analysis can provide a mechanistic understanding of how an exposure impacts an outcome, a central goal in epidemiology and health and social sciences. However, rapid methodologic developments coupled with few formal courses presents challenges to implementation.
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Writing a K-series NIH proposal is exciting but daunting. We will go over what a K is (and is not), and walk through the entire process, from generating feasible and fundable ideas to navigating the grant submission process. We will distinguish different K mechanisms, focusing on the K01 and K99-R00, and tradeoffs between them.
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This hands-on workshop addresses the essential R skills often missing from introductory courses, targeting researchers with some R experience who want to improve their coding practices and troubleshooting abilities. Participants will learn workflows that make R projects more reproducible, maintainable, and shareable, which is critical for epidemiologic research.
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What do top biomedical journal editors look for in submitted articles? How can we ensure high-quality methods and reliable results in the published literature that inform guidelines and clinical decision-making? Join us for a 4-hour workshop that will explore these questions and more! We have specially crafted this session for students and researchers with little or no prior peer review experience.
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Increasingly data mined from the electronic health record (EHR) are being used for secondary analysis in epidemiological research. But more data do not equate to better quality research. In this workshop, we will cover the fundamentals of working with EHR data and designing and conducting valid epidemiological analyses. The workshop will be half didactic lecture and half interactive group exercise.
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Exposure measurement error is a pervasive challenge in epidemiologic and public health research. Ignoring exposure measurement error leads to biased estimates of associations between exposures and health outcomes, usually attenuating effects toward the null and increasing the risk of false negatives, leading to misleading findings. This workshop will equip participants with both the conceptual understanding and practical skills to recognize, assess, and correct for exposure measurement error in epidemiologic research, featuring case studies in nutritional and environmental epidemiology.
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Propensity scores, defined as the probability of treatment conditional on covariates, have gained widespread use in biomedical and public health research. Propensity scores are intuitively appealing, and estimation using standard software packages is straightforward. Two common approaches using propensity scores, matching and weighting, allow researchers to target various causal estimands such as the average treatment effect (ATE), the average treatment effect in the treated (ATT), and the average treatment effect in the untreated (ATU).
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Version control, the practice of tracking and managing changes to statistical code, is essential for reducing errors in a statistical analysis. However, many epidemiologists are not trained to do this and are unsure how it fits with institutional review board (IRB) protocols and privacy standards. In this workshop, we will provide an introduction to git and GitHub, to equip epidemiologists with version control tools that also meet ethical standards.
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Measurements of neighborhood ‘social determinants of health’ are increasingly urgent in modern public health thinking, and are thought to drive and/or reinforce social and spatial inequities. Sometimes this necessitates an investigation of neighborhood health patterns, like premature mortality at the census tract scale.
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Sequence analysis, originally developed in biology to analyze DNA strings, is a data-driven technique that has been adapted to the social sciences in the 1980s to compare and group life course trajectories accounting for the order, timing, and duration of events. In epidemiology, it has been used to operationalize social exposures such education, family or work trajectories across the life-course, or healthcare trajectories such as chronic diseases progression.
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This workshop will apply the Causal Roadmap to evaluate causal effects with multiple intervention variables, such as the cumulative effects of an exposure over time and the effects on time-to-event outcomes subject to right-censoring.
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In Epi, we tend to be most familiar with estimating the effects of binary treatments or exposures. The classic average treatment effect (ATE), risk difference, risk ratio, and odds ratio are all examples of this. However, sometimes, exposures exist as a set, and it is most relevant to consider intervening on them jointly.
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This workshop will introduce attendees to Agent-Based Modeling (ABM) as a methodology for studying complex, dynamic, and heterogeneous systems in epidemiologic surveillance. Through a combination of lecture and hands-on exercises. attendees will explore the theoretical underpinnings of ABMs, implementation strategies using a free and operating system-independent platform (NetLogo), and the application of ABMs to real-world surveillance problems
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A wealth of unrealized health information is stored in unstructured fields and datasets, including text found on social media, and clinical note narratives within electronic health records (EHRs). Natural language processing and text analysis techniques allow researchers to operationalize and analyze information beyond what is collected in structured datasets in an efficient manner.
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This talk will briefly review the history of machine learning (ML) and artificial intelligence (AI), introduce the concepts and jargon, and demonstrate how to use these tools in Stata.
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Directed acyclic graphs (DAGs) are now standard in epidemiology, used to clarify assumptions, guide covariate adjustment, and diagnose bias. Their common use has been as static maps for identifying adjustment sets, yet DAGs can offer more if we recognize their structure as something worthy of analysis.
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Questions about dynamic (i.e., ‘personalized’) treatment strategies that account for changing individual characteristics increasingly outnumber the randomized trials available to answer them. When trials do not exist, real-world data, e.g., electronic health records and health insurance claims, can be used to estimate the observational analogues of the per-protocol effects of dynamic strategies, though, sufficient data and appropriate methods are required.
More2026 - Virtual Workshops
Learn from anywhere with our interactive online workshops. These sessions bring expert-led training and practical insights to your screen, offering flexibility without sacrificing engagement. Connect with peers and deepen your skills from the comfort of your home or office.
Workshop Description:
Directed acyclic graphs (DAGs) are now standard in epidemiology, used to clarify assumptions, guide covariate adjustment, and diagnose bias. Their common use has been as static maps for identifying adjustment sets, yet DAGs can offer more if we recognize their structure as something worthy of analysis.
MoreWorkshop Description:
This talk will briefly review the history of machine learning (ML) and artificial intelligence (AI), introduce the concepts and jargon, and demonstrate how to use these tools in Stata.
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Systems science modeling methods have been used for a long time in various fields, including infectious disease epidemiology, but have only been relatively recently introduced to the broader fields of public health and epidemiology, particularly non-communicable diseases.
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Missing data are a common problem in epidemiological studies that can introduce bias if handled inappropriately. The appropriate method for handling missing data in a study depends, amongst other aspects, on the causal mechanism that led to the missing data. Therefore, careful consideration of the assumptions about this mechanism is crucial for developing an appropriate analysis plan.
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