ALEJANDRO PARADA-MAYORGA

• Invited talk, “Sampling and Uniqueness Sets in Graphon Signal Processing.” American Mathematical Society Western Sectional – Boise, March 7/8, 2026.

• Invited talk, “DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images” at IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2025 August 31-September 3, Istanbul/Turkey

• Invited talk, Codes and Expansions (CodEx) Seminar – theory and applications of harmonic analysis, combinatorics, and algebra, September 2024, “Algebraic Neural Networks: Stability to Deformations”

• “Algebraic Neural Networks: Stability to Deformations”. Invited talk,
  Department of Mathematical and Statistical Sciences, University of Colorado (Denver), September 2024.

• “Algebraic Neural Networks: Stability to Deformations”. Invited talk, ECE Department Seminar, University of Colorado (Denver), April 2024.

• “Algebraic Neural Networks: Stability to Deformations”. Invited talk, Algebra Seminar, Math Department, Temple University, October 2023.

• “Algebraic Neural Networks: Stability to Deformations”. Invited talk, SUMRY (summer math research program), Yale University, June 2023.

• “Algebraic Neural Networks: Stability to Deformations”. Invited talk, CIS department seminar at University of Delaware, 2023.

• “Algebraic Neural Networks: Stability to Deformations”. Invited talk, CMSE department seminar at Michigan State University, 2023.

• “Algebraic Neural Networks: Stability to Deformations”. Invited talk, ESE department seminar at University of Washington in St. Louis, 2023.

• “Algebraic Neural Networks: Stability to Deformations”. Invited talk, ECE department seminar at University of Southern California, 2022.

• “Algebraic Neural Networks: Stability to Deformations”. Invited talk, ECE department seminar at Michigan state university, 2022.

• “Algebraic Neural Networks: Stability to Deformations”. Talk at the THEORINET Retreat.

• “Algebraic Neural Networks: Stability to Deformations” (delivered by professor Alejandro Ribeiro). Seminar “Physics meets Machine Learning” at CERN. [video]

• “Stability of Algebraic Neural Networks to Small Perturbations”. ICASSP 2021. [video]

• “Graphon Pooling in Graph Neural Networks”. EUSIPCO January 19, 2020. [video]

• “Algebraic Neural Networks: Symmetry and Stability” (delivered by professor Alejandro Ribeiro). Workshop on Equivariance and Data Augmentation at University of Pennsylvania, September 4, 2020. [Video]

• “Blue Noise and Optimal Sampling on Graphs” (Dissertation Defense). Department of Electrical and Computer Engineering, University of Delaware, July 2019.

• “Blue-Noise Sampling on Graphs”. 2019 Graph Signal Processing Workshop, University of Minnesota, MINNEAPOLIS, MN.

• “Optimal Sampling Sets in Cographs”. 2019 IEEE Data Science Workshop, University of Minnesota, MINNEAPOLIS, MN.

• “Sampling of Graph Signals with Blue Noise Dithering”. 2019 IEEE Data Science Workshop, University of Minnesota, MINNEAPOLIS, MN.

• “Blue Noise and Optimal Sampling on Graphs” (Dissertation Proposal). Department of Electrical and Computer Engineering, University of Delaware, April 2019.

• “Introduction to Compressed Sensing”. Seminar of the Computational Imaging and Data Science Group. Department of Electrical and Computer Engineering, University of Delaware, Fall 2015.

• “Compressed Sensing (Basics)”. Computational Imaging Seminar (ELEG 667), University of Delaware, Fall 2016.

• “Compressed Sensing (Reconstruction Algorithms)”. Computational Imaging Seminar (ELEG 667), University of Delaware, Fall 2016.