6:30 - Pizza and Networking
7:00 - Announcements
7:05 - Matt Sundquist - Making Plotly graphs with the R API
7:30 - Dag Lohmann - Catastrophe Models: Big Data. Climate and Insurance.
Plotly has a suite of APIs, including an R library, for making interactive, publication-quality plots in your browser. You can interface Plotly's online graphing tools with your desktop environment, then send or stream data to your Plotly graphs in your web browser. You can also style with code or with GUI, share work publicly with a url or privately among other Plotly members, and access your graphs from anywhere.
Matt Sundquist is a co-founder Plot.ly. He previously worked on the Privacy Team at Facebook. Matt studied Philosophy at Harvard College, and has been a writer for SCOTUSblog.com and a Fulbright Scholar in Argentina. A Student Fellow of the Harvard Law School Program on the Legal Profession, he has published articles on the Supreme Court, privacy, the social contract, and sundials. He has been cited in Senate Judiciary Committee Testimony, the New York Times, and Yale Law Journal and is a member of the CA Vital Statistics Advisory Committee.
Catastrophe (cat) models are used to estimate loss distributions from natural hazards like tropical cyclones, floods, or earthquakes. They integrate multiple disciplines such as meteorology, climatology, hydrology, structural engineering, statistics, software engineering and actuarial sciences. The ever increasing complexity of these models, the need for model transparency, as well as the desire to integrate models with diverse APIs have led us to develop a web-based cat model engine based on R using Shiny. By using R, users can easily create custom analytics and integrate auxiliary data from any data source, while being able to probe underlying model assumptions, perform sensitivity analysis and investigate all components of the cat model. We will show various technology components and R packages that we utilized.
Dag Lohmann. Before co-founding the risk modeling company KatRisk LLC, Dag was a Vice President of Model Development at Risk Management Solutions in Newark, CA leading a team of modelers and software engineers building and implementing catastrophe models. He worked for 7.5 years on the development of continental scale flood risk models in RMS and RMS' Next Generation risk modeling methodology. From 1999 to 2004 he was with the National Weather Service, NOAA/NCEP/EMC in Camp Springs, MD, where his main interest was data assimilation with real-time data, forecasting and hydrological modeling. He received a Physics Diploma (Masters) from the Georg-August University in Goettingen (Germany) and a Ph.D. from Hamburg University (Germany) before working for 2 years as a postdoc at Princeton University. He received the 1999 Tison Award of the IAHS and has published numerous papers on risk modeling, hydrological modeling, model uncertainty, forecasting, data assimilation, and climate change.