ECOM 2021 (Virtual)April 1-2, 2021 (Eastern Time)
Scientific Program
Detailed Program (LINK)

 

 

Schedule: April 1, 2021 (Eastern Time)

 

Time (EST) Presenter Title Recordings and Slides
10:00 am - 10:15 am Opening Remarks by GMU COS Dean Fernando Miralles-Wilhelm
10:15 am - 11:05 am Wotao Yin
(University of California, Los Angeles)		 Tutorial I:
Parallel, distributed, and decentralized optimization methods		  
11:10 am - 12:00 pm Wotao Yin
(University of California, Los Angeles)		 Tutorial II:
Parallel, distributed, and decentralized optimization methods		  
12:00 pm - 12:15 pm Networking Break
12:15 pm - 1:05 pm Wotao Yin
(University of California, Los Angeles)		 Public Lecture:
Learning to optimize		  
1:05 pm - 2:00 pm Lunch & Networking Break
2:00 pm - 2:45 pm Madeleine Udell
(Cornell University)		 Scalable semidefinite programming
2:45 pm - 3:30 pm Chris Teixeira
(The MITRE Corporation)		 When Machine Learning Fails
3:30 pm - 3:45 pm Networking Break
3:45 pm - 4:45 pm Contributed Talks:
(1) Stephanie Allen (UMD) Using inverse optimization to learn cost functions in generalized Nash games
(2) Roozbeh Yousefzadeh (Yale) Deep learning generalization and the convex hull of training sets
(3) Chitaranjan Mahapatra (UCSF) A genetic algorithm for optimal estimation of ion-channel kinetics from macroscopic currents in urinary bladder smooth muscles
(4) Nitin Vaidya (Georgetown) Byzantine fault-tolerant distributed Optimization and Learning
(5) Huaiqian You (Lehigh) A computational framework to machine-learn nonlocal constitutive models
4:45 pm - 5:00 pm Networking Break
5:00 pm - 6:12 pm Contributed Talks:
(6) Thomas Brown (GMU) Using DNNs for chemical reactions
(7) Akwum Onwunta (GMU) Novel deep neural networks for solving Bayesian statistical inverse problems
(8) Ryan Vogt (LLNL) Optimal control Of SFQ quantum computers with binary optimal control
(9) Yunan Yang (NYU) The implicit regularization of metrics
(10) Ramesh Sau (IISc Bangalore) Finite element analysis of the constrained Dirichlet boundary control governed by the diffusion problem
(11) Brendan Keith (Brown) Gravitational wave measurements can be used to learn the orbital dynamics of binary black hole systems

 

Schedule: April 2, 2021 (Eastern Time)

 

Time (EST) Presenter Title Recordings and Slides
10:00 am - 10:10 am SIAM booth
10:15 am - 11:05 am Frank E. Curtis
(Lehigh University)		 Tutorial I:
Optimization Methods for Large-Scale Machine Learning		  
11:10 am - 12:00 pm Frank E. Curtis
(Lehigh University)		 Tutorial II:
Optimization Methods for Large-Scale Machine Learning		  
12:00 pm - 12:15 pm Networking Break
12:15 pm - 1:05 pm Frank E. Curtis
(Lehigh University)		 Public Lecture:
Nonconvex Optimization: Opportunities and Challenges		  
1:05 pm - 2:00 pm Lunch & Networking Break
2:00 pm - 2:45 pm Stefanie Guenther
(Lawrence Livermore National Laboratory)		 Simultaneous Layer-Parallel Training for Deep Residual Networks
2:45 pm - 3:15 pm Patrick O'Neil
(BlackSky)		 Analyzing Thin Film Morphologies using Machine Learning and Topological Data Analysis
3:15 pm - 3:30 pm Networking Break
3:30 pm - 4:30 pm Contributed Talks:
(1) Ming Zhong (JHU) Data-driven modeling of celestial motion from modern ephemeris
(2) April sagan (RPI) Provable low-rank plus sparse matrix recovery via nonconvex regularizers
(3) Vivak Patel (Madison) On consistency and asymptotic normality of adaptive stochastic gradient methods
(4) Eswar Kumar Hathibelagal Kammara (UMBC) Column partition based distributed algorithms for coupled convex sparse optimization: dual and exact regularization approaches
(5) Furong Huang (UMD) Escaping from saddle points using asynchronous coordinate gradient descent
4:30 pm - 4:45 pm Networking Break
4:45 pm - 5:57 pm Contributed Talks:
(6) Matthias Chung (VT) Big data inverse problems
(7) Nguyen-Truc-Dao Nguyen (Wayne State) Optimization of fully controlled sweeping processes
(8) Yongchun Li (VT) Exact and approximation algorithms for sparse PCA
(9) Andersen Ang (Waterloo) Nonnegative unimodal matrix factorization
(10) Manuela Girotti (MILA) Condition numbers for first-order optimization
(11) Christian Kümmerle (JHU) A scalable second order method for ill-conditioned matrix completion from few samples
5:57 pm -- Closing Remarks