Computational Stochastic Optimization and Learning
CASTLE Labs works to advance the development of practical, scalable models and algorithms for solving a wide range of applications. Core activities include:
- Modeling - The mathematical representation of stochastic problems is subtle. We use methods that correctly model the flow of information, and which scale to handle complex problems (see the "Jungle of stochastic optimization").
- Theory - We analyze the properties of algorithms, including convergence proofs, rate of convergence, bounds, and properties of estimators in machine learning.
- Computation - We do extensive testing of algorithms on Tower, which is a 15 node compute system with over 400 threads, including three 64 thread machines, one with 1 terabyte of RAM.
Surrounding the core activities in methodology are laboratories focusing on major areas of application:
- Optimal learning - This research addresses the challenges of collecting information, when information (observations, simulations, laboratory and field experiments) are expensive. See our new hOLMES Data Sciences website for learning in materials science.
- Health sciences - Projects in health have included drug discovery, drug delivery, blood management, dosage decisions, personal health, and health policy.
I hope you find the material interesting, and perhaps useful. If you have
any questions, please contact me.