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