ECOM 2022 (Virtual)March 31-April 1, 2022 (Eastern Time)
Scientific Program
Detailed Program (LINK)

 

 

Schedule: March 31, 2022 (Eastern Time)

 

Time (EST) Presenter Title Recordings and Slides
10:00 am - 10:15 am Opening Remarks
10:15 am - 11:05 am Michael Ulbrich
(Technical University Munich)		 Tutorial I:
Semismooth Newton Methods – Theory and Applications		  
11:10 am - 12:00 pm Michael Ulbrich
(Technical University Munich)		 Tutorial II:
Semismooth Newton Methods – Theory and Applications		  
12:00 pm - 12:15 pm Networking Break
12:15 pm - 1:05 pm Michael Ulbrich
(Technical University Munich)		 Public Lecture:
An Approximation Scheme for Distributionally Robust Nonlinear Programming with Applications to PDE-Constrained Optimization under Uncertainty		  
1:05 pm - 2:00 pm Lunch & Networking Break
2:00 pm - 2:45 pm Courtney Paquette
(McGill University)		 Algorithms for stochastic nonconvex and nonsmooth optimization
2:45 pm - 3:30 pm Aleksandr Aravkin
(University of Washington)		 Relax-and-split method for structured optimization
3:30 pm - 3:45 pm Networking Break
3:45 pm - 4:45 pm Contributed Talks:
(1) Jiaming Liang
(Georgia Institute of Technology) 		A unified analysis of a class of proximal bundle methods for solving hybrid convex composite optimization problems
(2) Tina Mai
(Duy Tan University at Da Nang (Vietnam) and Texas A&M University at College Station (USA)) 		Theory of functional connections applied to quadratic and nonlinear programming under equality constraints
(3) Alessandro Scagliotti
(Scuola Internazionale Superiore di Studi Avanzati) 		A piecewise conservative method for unconstrained convex optimization
(4) Jennifer Erway
(Wake Forest University) 		Advances in multipoint symmetric secant methods
(5) Tim Roith
(Friedrich-Alexander Universität Erlangen-Nürnberg) 		A Bregman Learning Framework for Sparse Neural Networks
4:45 pm - 5:00 pm Networking Break
5:00 pm - 6:00 pm Contributed Talks:
(6) Vivak Patel
(University of Wisconsin – Madison) 		Global Convergence and Stability of Stochastic Gradient Descent
(7) Roozbeh Yousefzadeh
(Yale University) 		Decision Boundaries and Convex Hulls in the Feature Space that Deep Learning Functions Learn from Images
(8) Xiao Ling
(Virginia Commonwealth University) 		L1-norm regularized L1-norm best fit line problem
(9) Anton Lukyanenko
(George Mason University) 		Sampling-based path planning: a metric approach
(10) Ashwin Renganathan
(University of Utah) 		Lookahead Bayesian optimization and applications

 

Schedule: April 1, 2022 (Eastern Time)

 

Time (EST) Presenter Title Recordings and Slides
10:00 am - 10:10 am SIAM booth
10:15 am - 11:05 am Amir Beck
(Tel-Aviv University)		 Tutorial I:
Proximal-Based Methods in Convex Optimization		  
11:10 am - 12:00 pm Amir Beck
(Tel-Aviv University)		 Tutorial II:
Proximal-Based Methods in Convex Optimization		  
12:00 pm - 12:15 pm Networking Break
12:15 pm - 1:05 pm Amir Beck
(Tel-Aviv University)		 Public Lecture:
On the convergence to stationary points of deterministic and randomized feasible descent directions methods		  
1:05 pm - 2:00 pm Lunch & Networking Break
2:00 pm - 2:45 pm Jelena Diakonikolas
(University of Wisconsin-Madison)		 Faster nonsmooth empirical risk minimization
2:45 pm - 3:30 pm Damek Davis
(Cornell University)		 Avoiding saddle points in nonsmooth optimization
3:30 pm - 3:45 pm Networking Break
3:45 pm - 4:45 pm Contributed Talks:
(1) Malena Espanol
(School of Mathematical and Statistical Sciences, Arizona State University) 		An 𝓁p Variable Projection Method for Large-Scale Separable Nonlinear Inverse Problems
(2) Manuel Weiß
(Interdisciplinary Center for Scientific Computing (Heidelberg)) 		Geometry Segmentation with Total Variation Regularization
(3) Kelsey DiPietro
(Sandia National Laboratories) 		Optimization based solvers for the Monge-Amp ́ere equation with applications to mesh adaptivity
(4) Ratna Khatri
(U.S. Naval Research Laboratory)		 Optimal Control Framework For Deep Autoencoders
(5) Matthias Chung
(Virginia Tech) 		Learning Regularization Parameters of Inverse Problems via Deep Neural Networks
4:45 pm - 5:00 pm Networking Break
5:00 pm - 6:00 pm Contributed Talks:
(6) Mark McFeaters
(University of Tennessee at Chattanooga) 		Design of Compact and Connected Reserve Systems for Ecological Conservation
(7) Logan Beaver
(University of Delaware) 		Constraint-Driven Control for Multi-Agent Systems
(8) Evelyn Herberg
(George Mason University)		 An Optimal Time Variable Learning Framework for DNNs
(9) Steven Rodriguez
(United States Naval Research Laboratory) 		Accelerating Derivative-Free Inverse Analyses in Process Parameter Identification via Model-Reduction with Advection-Segmented Local Reduced-Order Subspaces
(10) Melanie Weber
(University of Oxford) 		Constrained Optimization on Riemannian manifolds
6:00 pm -- Closing Remarks