Workshop: Uncertainty Management and Machine Learning in Engineering Applications Recordings

Pawel Polak: Classification and Severity Progression Measure of COVID-19 Patients Using Proteomic and Metabolomic Sera

Craig Vineyard: Neural Network Approaches for Enabling Automatic Target Recognition

Warren Dixon: Multiple Timescale Deep Learning

Oleg Prokopyev: A Mixed-Integer Fractional Optimization Approach to Best Subset Selection

Andrzej Ruszczynski: A Single Time-Scale Stochastic Approximation Method for Nonsmooth and Nonconvex Stochastic Composition Optimization

Drew Kouri: Design of Experiments for Superquantile Regression

David Stracuzzi: Models of Models: Recognizing and Managing the Uncertainties of Machine Learning in Engineering Applications

Eric Cyr: A Layer-Parallel Approach for Training Deep Neural Networks

Suvrajeet Sen: Stochastic Decomposition – Computational Advances over Three Decades

Bart van Bloemen Waanders: Hyper-Differential Sensitivity Analysis: Managing High Dimensional Uncertainty in Large-Scale Optimization

Thomas Surowiec: Stability Analysis for a Class of Risk-Neutral PDE-Constrained Optimization Problems

Alexander Shapiro: Computational Approaches to Solving Multistage Stochastic Programs

Lars Ruthotto: Machine Learning for High-Dimensional Optimal Transport

Darinka Dentcheva: Bias Reduction in Sample-Based Optimization

Jun-ya Gotoh: Worst-case Sensitivity

Harbir Antil: Role of Fractional DNNs in Inverse Problems

Bogdan Grechuk: Mathematical Foundations for Error Correction in Machine Learning

Grigoriy Zrazhevsky: A Wave Dynamics Inverse Problem: Mathematical Analytics vs Machine Learning

Kostas Spiliopoulos: DGM: A Deep Learning Method to Solve PDEs

Johannes Royset: Diametrical Risk Minimization: Theory and Computations

Eugene Feinberg and Rui Ding: CVaR Optimization for Sequential Decisions Processes

Wilkins Aquino: An Adaptive Sample-Based Approximation Approach for Stochastic Inverse Problems

Nat Trask: A Data-Driven Exterior Calculus for Learning Models with Exact Physics

Ahmad Rushdi: Estimating Neural Networks’ Predictive Uncertainty in SciML

Stan Uryasev: Renyi Entropy and Calibration of Distribution Tails

Pavlo Krokhmal: Risk-Averse Graph Theoretical Problems

Alex Lipton: Observations of a financial supernova: Emerging Trends in Decentralized Finance (DeFi)

James Ostrowski: Quantum Approximate Optimization Algorithm

Gianluca Iaccarino: Data-free and Data-driven Uncertainty Quantification in Turbulence Simulations