A methodology of uncertainty quantification developed in a series of studies and termed Bound-to-Bound Data Collaboration (abbreviated to B2B-DC) will be presented. B2B-DC is framework for combining models and training data from multiple sources to explore their collective information content. It is built on an underlying physical process and associated model, a collection of experimental observations with specified uncertainties, algebraic surrogate models (response surfaces) representing parametric dependence of the physical-model predictions of the experimental observables on the uncertain parameters, and specialized constrained-optimization algorithms. The methodology makes predictions on the true feasible set, transfers the uncertainties of both model parameters and training-set experiments directly into prediction, tests and quantifies consistency among data and models, explores sources of inconsistency, discriminates among differing models, and enables analysis of global sensitivities of uncertainty in prediction to the uncertainties in data and model. Applications of the approach include combustion science and engineering, atmospheric chemistry, and system biology.
Abstract: In this talk, I will present a few urban data sets and discuss extracted patterns and models, and their variability. Based on vehicle routes of several data sets, I will then present a novel transport planning algorithm with transfers and show the corresponding resource savings. This work is in collaboration with students, in particular with Brian Coltin.
Biography: Manuela Veloso’s long-term research goal is the effective construction of autonomous agents where cognition, perception, and action are combined to address planning, execution, and learning tasks. Her vision is that multiple intelligent robots with different sets of complementary capabilities will provide a seamless synergy of intelligence. Manuela Veloso’s research focuses on the continuous integration of reactive, deliberative planning, and control learning for teams of multiple agents acting in adversarial, dynamic, and uncertain environments. Her multiagent and multirobot research interests have been motivated by and experimented in the domain of robot soccer. Since 2009, she has been investigating indoor mobile, service, companion robots, CoBots, such that robots and humans interact in a symbiotic relationship building upon individual strengths and limitations. Veloso created and directs her CORAL overarching research lab for the research on intelligent agents that Collaborate, Observe, Reason, Act, and Learn. As of 2010, she has ten PhD students and has graduated other twenty one PhD students, whose theses are available at her website www.cs.cmu.edu/~mmv. She thanks her students for the compelling research that they jointly pursue.
The Grid with Intelligent Periphery - Dr. Kameshwar Poolla - UC Berkeley
Hosted by Mechanical Engineering & Data Science Institute