uncertainty quantification

Abdul-Lateef Haji-Ali

Ph.D., Applied Mathematics and Computational Science

uncertainty quantification numerical analysis Multilevel Monte Carlo

Shyam Mohan Subbiah Pillai

Research Consultant, Stochastic Numerics Research Group

numerical analysis uncertainty quantification stochastic differential equations

Nabila Bounceur

Research Scientist, Stochastic Numerics Research Group

probabilistic sensitivity analysis statistical emulators uncertainty quantification complex system modeling

My work focuses on the modeling of complex systems from the Probabilistic Sensitivity Analysis (PSA) and Uncertainty Quantification (UQ) perspective. I develop methodologies to efficiently characterize input-output relationships in computationally expensive simulators, enabling both qualitative understanding (which factors matter most?) and quantitative assessment (how much does each factor contribute to output uncertainty?).

Abderrahmene Ben Romdhane

M.S. Student, Stochastic Numerics Research Group

numerical analysis Computational finance uncertainty quantification

Arved Bartuska

Postdoctoral Research Fellow, Stochastic Numerics Research Group

numerical analysis uncertainty quantification optimal experimental design

Uncertainty Quantification with Conformal Prediction in Energy Data

Tarek AlSkaif, Associate Professor, Energy Informatics, Wageningen University (WUR)

Feb 1, 12:00 - 13:00

B9 L2 R2325

conformal prediction machine learning uncertainty quantification

The talk will introduce the fundamentals of conformal prediction (CP) - a flexible, model-agnostic uncertainty quantification framework for generating statistically valid uncertainty estimates in energy applications - and demonstrate how it can be layered on top of machine learning models to produce reliable prediction intervals.

Amal Alghamdi

Founder, Impact Alpha, Saudi Arabia

HPC Python data analysis finite element method uncertainty quantification

Decision-Making Under Risk in Complex Environments

Yury Dvorkin, Associate Professor, Departments of Civil and Systems Engineering and Electrical and Computer Engineering, Johns Hopkins University

Mar 17, 12:00 - 13:00

B9 L2 H2

uncertainty quantification probabilistic optimization

Advances in uncertainty quantification enable more nuanced exploration of decision-making under risk in complex environments.