Upcoming Events

Summer 2023 dates: Hybrid training (In-person and simultaneous livestream for remote attendees) June 22-23, 2023; 10am - ~5pm EDT.    The Machine Learning Boot Camp is a two-day intensive boot camp of seminars combined with hands-on R labs and data applications to provide an overview of statistical concepts, techniques, and data analysis methods with applications in biomedical research. This two-day intensive training will provide a broad introduction to machine learning methodology with applications in biomedical research. Taught by a team of biostatisticians, the Boot Camp will integrate seminar lectures with hands-on R lab sessions to put concepts into practice. Emphasis will be given to supervised (e.g., penalized methods, classification and decision trees, survival forests) and unsupervised methods (e.g., clustering algorithms, dimensionality reduction) with numerous case studies and biomedical applications. The workshop will conclude with an overview and demonstration of  ‘deep learning’ algorithms.  By the end of the boot camp, participants will be familiar with the following topics:  Penalized Regression Methods (Ridge and Lasso)  Classification Models (e.g., Support Vector Machines)  Tree Based Methods (Decision/Regression Trees)  Clustering Algorithms  Principal Component Analysis (PCA)  Deep Learning – Introduction to dense and convolutional neural networks  Audience and Requirements Investigators from any institution and from all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate. There are three requirements to attend this training: Each participant must have an introductory background in statistics (i.e., linear and logistic regression). Each participant must be familiar with R. The main platform used for the workshop will be RStudio Cloud, therefore we strongly recommend that participants have a basic understanding of R/RStudio prior to attending the Training.  Each participant is required to have a personal laptop and a free, basic RStudio Cloud account prior to the first day of the workshop. All lab sessions will be done on this platform. Instructors Cody Chiuzan(link is external and opens in a new window), PhD, Associate Professor, Institute of Health System Science, Feinstein Institutes for Medical Research​, Northwell Health. Jean Feng(link is external and opens in a new window), PhD, Assistant Professor, Department of Epidemiology and Biostatistics, University of California, San Francisco. Noah Simon(link is external and opens in a new window), PhD, Associate Professor, Department of Biostatistics, School of Public Health, University of Washington. Additional Information View the training website for more details. Subscribe for updates on new Boot Camp details and registration deadlines. Contact the Machine Learning Boot Camp team. Capacity is limited. Paid registration is required to attend.

The Exposome Boot Camp is a two-day intensive boot camp of seminars and hands-on analytical sessions to provide an overview of concepts, techniques, and data analysis methods used in studies of the exposome. This two-day intensive boot camp integrates the principle concepts of exposomics and the untargeted approaches of measuring endogenous and exogenous chemical exposures on an omic scale as we step through the tools and techniques currently available to analyze the exposome. Led by a team of expert scientists in the rapidly growing field of exposomics, the boot camp will integrate seminar lectures with hands-on computer lab sessions to put concepts into practice. Emphasis will be given to leveraging existing resources from ongoing studies and initiating new investigations. The afternoon lab sessions will provide an opportunity to work hands-on with real data. Participants will learn and practice data handling, cleaning, and basic analysis of exposomics data. By the end of the workshop, participants will be familiar with the following topics: Principles of exposomics Concept ideation and study design Laboratory methods and instrumentation platforms for analysis Data extraction and cleaning with emphasis on peaks and feature tables Pathway data analysis and identification databases Data visualization and network interpretation Study design advantages and pitfalls Emerging exposomic topics and laboratory platforms AUDIENCE AND REQUIREMENTS Investigators at all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate. This training is designed to accommodate investigators with varying experience levels in R. Knowing R basics is beneficial to maximize training from the boot camp, but is not necessary for you to strongly engage with training material. Data labs will be offered during the training and are meant to accommodate both beginner and advanced data manipulation, with all material and additional resources available after the training. There are four requirements to attend this training: Each participant must have an introductory background in statistics. Each participant is required to have a personal laptop. If you would like to participate in the asynchronous, hands-on data labs offered by the Boot Camp, you will need to have R downloaded and installed as all lab sessions will be done on your personal laptop. R is available for free download and installation on Mac, PC, and Linux devices.   The Boot Camp will provide lab tutorials with data sets in R, therefore we recommend participants have a basic understanding of this platform prior to attending the Boot Camp to get the most out of lab sessions. R knowledge is not required if you do not plan to participate in lab sessions. If you have not used this platform before, step through the tutorials outlined below so you have the basic skills needed for Boot Camp success. ADDITIONAL INFORMATION Subscribe for updates on new Boot Camp details and registration deadlines. Contact the Boot Camp team. Capacity is limited. Paid registration is required to attend.

The Data, Designed Workshop is a two-day intensive hands-on workshop designed for people and teams who want to develop their creative process to create better, more effective data visualizations. Bring your data to life using innovative visualization techniques in this workshop unlike any other, and learn from world leaders that come from two sides of data visualization: data science and design. Explore styles, techniques, and best practices to create meaningful and beautiful visualizations. As the amount and speed of data grows at ever-increasing rates, being able to visualize and communicate data and data analysis has become more and more important. Organizations that are tasked with producing, releasing, and analyzing large amounts of data can more effectively do so by understanding how to visualize their data. In this workshop, you will learn the important underpinnings of the science and design process behind data visualization and how to effectively communicate your work. This workshop will provide you with the core skills needed to conceptualize, plan, and develop data visualization products. Taught by world-renowned leaders in the field—Stefanie Posavec and Jonathan Schwabish—this workshop will inspire you to push beyond your own limits so you can create great, effective visualizations. Step away from your keyboard and enjoy some fresh space for your hands and mind: the entire workshop will taught using nothing more than basic drawing materials and office supplies. By the end of the workshop, participants will be familiar with the following topics: Be familiar with different kinds of data visualizations Identify the different forms and functions of visualizations Learn, and exchange ideas about, visualization best practices Learn how to increase message clarity and impact Learn the basic data analysis skills one needs as a data visualizer Understand the data visualization design process Explore how to create their own custom data visualization system Have a starting point for applying data visualization in their own work Audience and Requirements View Requirements for this training Additional Information Subscribe for updates on new training details and registration deadlines. Contact the Workshop team. Capacity is limited. Paid registration is required to attend. 

The Environmental Mixtures Workshop is a two-day intensive training of seminars and hands-on analytical sessions to provide an overview of environmental mixtures concepts, techniques, and data analysis methods used in health studies. This two-day intensive workshop will provide a rigorous introduction to multiple different techniques to analyze exposure to mixtures in environmental health. Led by a team of world experts in environmental health, epidemiology and statistics, many of whom have developed their own methods to analyze exposure to mixtures, the workshop will integrate seminar lectures with hands-on computer lab sessions to put concepts into practice. Emphasis will be given to supervised and unsupervised methods. Since the choice of method depends on the research question at hand, the workshop will conclude with a panel discussion on when each method presented is appropriate for use and for which research questions.   By the end of the workshop, participants will be familiar with the following topics:  Principal Component Analysis (PCA)  Factor Analysis (FA)  Clustering  Variable Selection (Lasso, elastic net)  Bayesian Kernel (BKMR)  Weighted Quantile Sum Regression (WQS)  Emerging mixtures topics and novel extensions  Tree-based methods  Audience and Requirements Investigators at all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate. There are three requirements to attend this workshop: Each participant must have an introductory background in statistics. Each participant must be familiar with R. Each participant is required to have a personal laptop/computer and a free, basic Posit Cloud (formerly RStudio Cloud) account.  All lab sessions will be done using Posit Cloud (formerly RStudio Cloud). Additional Information Subscribe for updates on new Boot Camp details and registration deadlines. Contact the Boot Camp team. Capacity is limited. Paid registration is required to attend.