- Scientific Computing
- Technical level
Exposure to new open source technology for optimization and risk analysis.
We have developed a new rigorous mathematical framework for quantifying risk designed to utilize all available information and predict the impact of Black Swan events. These rigorous predictors are global optimizations over all possible valid scenarios, and do not rely on common approximations in computing priors that lead to the exclusion of high-impact rare events. Such optimizations, however, are high-dimensional, highly-constrained, and non-convex, and generally impossible to solve with current optimization technology. I will present an overview of our mathematical framework, and the powerful optimization software we have developed to rigorously solve real-world problems in predictive science, finance, and technology. The software runs in standard python on a simple laptop -- however, it trivially scales up to potentially petascale and larger calculations running on some of the largest computers on the planet.
Mike has over fifteen years of teaching experience in physics, applied math, and computing, and has taught twenty financial and/or science workshops in the past year alone. He has been a research scientist at Caltech since 2002, where he has served as a project manager and also a lead developer for two $15M software projects on predictive science and large-scale computing. Over the past five years, his software has been the backbone of several research projects on large-scale risk analysis and predictive science. Mike is co-founder of the UQ Foundation, a non-profit for the advancement of predictive science, and a co-creator of OUQ theory, a rigorous mathematical framework for uncertainty quantification. Mike has a B.S. in Applied Physics from Notre Dame, and a Ph.D. in Physics from the University of Alabama Birmingham. He has been developing parallel and distributed computing software infrastructure for ten years, and large-scale optimization and risk analysis software frameworks for over five years. His software frameworks have been used within HPC credit risk applications, at national labs on spallation neutron sources, and are being used as validation and performance testing suites in the design of the first generation of exascale computers. His software has over 15,000 total downloads to unique IP addresses, including one library available in Fedora and RHEL distributions. Mike has developed python code for Enthought, JPMorgan, Caltech, NASA, and has contracted to several of the U.S. National Laboratories.