Events

The Google Earth Engine Booth Camp is a two-day intensive training workshop that includes seminars and hands-on case studies to provide an overview of concepts, techniques, applications, and data analysis methods for using the Google Earth Engine to estimate environmental exposures and exposures for health research. The Google Earth Engine (https://earthengine.google.com/(link is external and opens in a new window)) combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities to estimate environmental measures at fine spatial and temporal resolutions. To-date, this resource has yet to be fully leveraged for environmental exposure science and health research. This two-day intensive boot camp provides an overview of concepts, techniques, applications, and data analysis methods for applying the Google Earth Engine to estimate environmental factors (e.g., green space, air pollution, climate change) and exposures for health research. Lectures will introduce participants to cutting-edge geospatial exposure assessment methods and example case studies will use the Google Earth Engine to estimate air pollution, green space, built environment, temperature, climate, and natural system-related exposures (e.g. drought, flooding, forest change, etc.). This boot camp is led by experts in the field of environmental exposure science and epidemiology, data science, and geography. By the end of the workshop, participants will be familiar with the following topics: Capabilities of the Google Earth Engine Datasets available for environmental exposure science Data analysis methods within the Google Earth Engine Applications for air pollution, green space, built environment, temperature, and climate exposure research Applications for planetary and human health research Emerging satellite data and exposure science opportunities 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: Basic familiarity with coding and GIS is highly recommended (but not required) to get the most out of the Boot Camp. While there are several APIs to access the Google Earth Engine, we will be using the JavaScript Earth Engine Code Editor. New for 2025, an optional prerecorded lecture and self-paced lab is available for those interested in using Python or R.  Each participant is required to sign up for a Google Earth Engine account prior to the boot camp.  Each participant is required to watch a 30-minute presentation prior to the boot camp which provides an overview of the Google Earth Engine code editor. For those unfamiliar with JavaScript, watching an additional presentation with hands-on examples of running code in the Google Earth Engine is also recommended.  Each participant must have a laptop or desktop with Zoom installed.  To get the most out of breakout groups, we also recommend a working microphone and webcam.  Participants may also wish to share their screen with instructors during one-on-one questions. Additional Information Subscribe for updates on new Boot Camp details and registration deadlines. Contact the Google Earth Engine Boot Camp team. Capacity is limited. Paid registration is required to attend.

Speaker: Christopher Mason, PhD, Professor of Physiology and Biophysics, Director WorldQuant Initiative for Quantitative Prediction and WorldQuant Foundation Research Scholar, Professor of Computational Biomedicine in the Institute of Computational Biomedicine, Professor of Neuroscience in the Brain and Mind Institute, Weill Cornell Medicine Hosted by Aaron Viny, MD, Assistant Professor of Medicine at CUIMC. This event is sponsored by the Herbert Irving Comprehensive Cancer Center's (HICCC) Edward P. Evans Center for Myelodysplastic Syndromes (MDS) at Columbia University, established to foster an all-encompassing approach in studying MDS giving us molecular, cellular genetic and epigenetic insights to develop and test innovative therapies.

The one-year Columbia MS in Human Nutrition is a three-semester program (fall, spring, summer) that combines rigorous coursework with thesis research in basic nutrition science, clinical nutrition, or public health nutrition. Our graduates are prepared for medical school as well as a broad range of careers across today’s healthcare landscape. Join our webinar and alumni panel discussion, where Institute faculty, staff, and alumni will provide an overview of the program and share insights on career pathways and program highlights. The session will conclude with a live Q&A. Click Register for the Webinar to secure your spot. We look forward to connecting with you!

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. 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.