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. The core of CASTLE Labs focuses on advances in fundamental methods, including:
- Modeling - The mathematical representation of stochastic problems is subtle, but represents the hallmark of good research, in addition to being critical for the successful implementation of a model. We also offer assistance with modeling projects outside of the lab.
- 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 for complex materials - Funded by the Air Force, this research is building off our work in optimal learning. Look for more in this area soon.
- Health sciences - Projects in health have included drug discovery, blood management, dosage decisions, personal health, and health policy. Most of these projects represent undergraduate senior thesis research.
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