Uncertainty Quantification Framework for Modeling Prediction

Friday, October 10, 2014 - 11:00am to 12:00pm
233 Mudd
Columbia University
New York, NY 10027
United States

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.


Michael Frenklach
Department of Mechanical Engineering
University of California at Berkeley
Michael Frenklach is Professor in the Department of Mechanical Engineering of the University of California at Berkeley. He received his Diploma in Chemical Technology from the Mendeleyev Russian Chemical-Technological University (Moscow, Russia) in 1969 and his Ph.D. in Physical Chemistry at Hebrew University (Jerusalem, Israel) in 1976. Professor Frenklach’s faculty appointments began in 1979 in the Department of Chemical Engineering at Louisiana State University. He received the Alexander von Humboldt Research Fellowship and spent a year in the Institute of Physical Chemistry at Heidelberg University (Germany). In 1985 he joined the Materials Science Department of the Pennsylvania State University and in 1995 he accepted his current position at Berkeley. Professor Frenklach’s research interests are in the areas of soot formation, diamond synthesis, interstellar dust, kinetic modeling of complex reaction networks, and currently uncertainty quantification and cyber-automation of collaborative science.

Urban Data & Transport Planning with Transfers by Manuela M. Veloso

Thursday, February 13, 2014 - 6:00pm
Davis Auditorium, Room 412, Shapiro SEPSR
500 West 120th Street
New York, NY 10027
United States

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 She thanks her students for the compelling research that they jointly pursue.

Big Data/Digital Scholarship

Monday, November 18, 2013 - 5:00pm to 6:00pm
The Studio@Butler, 208b Butler Library
Columbia University
New York, NY 10027
United States
The University Seminar on Big Data and Digital Scholarship is pleased to announce our next meeting (this Monday):
Daniel Krasner
Data Scientist, Co-founder KFit Solutions/Advisor Johnson Research Labs
High Performance Text Processing and the Columbia Declassification Engine Project


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