Collaborative Research: CyberTraining: Pilot: Monte Carlo General Education Network (MCGEN)

About This Grant

Particle and nuclear physics (PNP) are fundamentally probabilistic due to quantum mechanics. Both fields rely on complex Monte-Carlo (MC)-based simulators that use random number sampling to make predictions for nearly all aspects of experimental design and data interpretation. In fact, most branches of science and engineering rely heavily on MC simulations for solving difficult problems, from modeling traffic flow to predicting weather patterns; in the rapidly emerging fields of machine learning and quantum computing, MC methods are essential. Progress in these areas requires developing, validating, and deploying novel and efficient MC algorithms. However, many university computer science programs focus on deterministic methods, with MC techniques covered only in passing, leading to a gap between knowledge and required skills for junior researchers. This project fills the knowledge gap by training graduate students and junior postdoctoral researchers in the development of MC models with traineeships and schools focused on real-world PNP problems. The project has three main goals. The first is to develop summer-school curricula as well as organize summer schools to train graduate students and junior postdoctoral researchers in MC generator algorithms and their applications. The second is to build on summer school material and produce online tutorials for self-guided study. The third goal is to create and run a 2-year pilot program of focused, short-term traineeships for graduate students and postdoctoral researchers, which could in the future be scaled up to include more nodes and mentors in the training network. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Physics within the Directorate for Mathematical and Physical Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.