Invited Talks:
41. Bayesian Spatio-Temporal Methods with Edge-Preserving Priors for Inverse Problems, June, 2023, @Foundations of Data Assimilation and Inverse Problems, (FoCM 2023), Paris, France
40. Bayesian Spatio-Temporal Methods with Edge-Preserving Priors for Inverse Problems, May, 2023, @SIAM Conference on Optimization, Seattle, Washington, USA
39. Bayesian Spatio-Temporal Methods with Edge-Preserving Priors for Inverse Problems, February, 2023, @ SIAM Conference on Computational Science and Engineering, Amsterdam, The Netherlands
38. Bayesian Spatio-Temporal Methods with Edge-Preserving Priors for Inverse Problems, January, 2023, @Joint Mathematical Meetings, Boston, Massachusetts, USA
37. Bayesian and Deterministic Spatio-Temporal Methods with Edge-Preserving Priors for Inverse Problems, December, 2022, @John Hopkins University, USA
36. Bayesian and Deterministic Spatio-Temporal Methods with Edge-Preserving Priors for Inverse Problems, December, 2022, @University of Potsdam, Germany
35. Bayesian Spatio-Temporal Methods with Edge-Preserving Priors for Inverse Problems, November, 2022, @CUQI seminar, Technical University of Denmark, Denmark
34. On Deterministic and Statistical Methods for Large-scale Dynamic Inverse Problems. November, 2022, @City University of New York
33. On Deterministic and Statistical Methods for Large-scale Dynamic Inverse Problems. June 12-17, 2022, @ 2022 Program for Women and Mathematics Institute for Advanced Study and Princton University
32. On Deterministic and Statistical Methods for Large-scale Dynamic Inverse Problems. May 21-26, 2022, @Householder Symposium XXI on Numerical Linear Algebra
31. On Deterministic and Statistical Methods for Large-scale Dynamic Inverse Problems. April 20-21, 2022, @Rising Stars in Computational and Data Science Workshop
30. From Challenges to Edge-preserving Methods for Large-scale Dynamic Inverse Problems. April 4-8, 2022, @SIAM Copper Mountain Conference on Iterative Methods (virtual)
29. Majorization minimization-based Laplace approximation for Bayesian inverse problems with arbitrary prior and noise models, April 2022, @SIAM Conference on Uncertainty Quantification
28. Edge-preserving methods for large-scale dynamic inverse problems, March 2022, @Tufts University
27. Edge-preserving methods for large-scale dynamic inverse problems, March 22-25, 2022, @SIAM Conference on Imaging Science 2022
26. New deterministic and learning techniques for solving large-scale inverse problems, January, 2022, @Joint Math Meeting 2022 Special Session on Computer Vision
25. Efficient learning methods for large-scale optimal inversion design, November 2021, @Kent State University
24. Some recent advancements on solving large-scale inverse problems, October 2021, @2021 Fall Western Sectional Meeting
23. Computational methods for large-scale inverse problems, October 2021, @Temple University
22. Some deterministic and learning approaches based on Krylov subspaces to solve large-scale inverse problems, October 2021, @Kansas State University
21. New deterministic and learning approaches for large-scale inverse problems, September 2021, @Research Training Group (RTG), Arizona State University
20. Talk title: From static to dynamic inverse problems: new edge-preserving methods for image reconstruction, September 2021, @SIAM Southeastern Atlantic Section Conference - Auburn University
19. Some old and new dimension-reduction techniques to solve large-scale inverse problems, August 2021, @Presse Lab, Arizona State University
18. Efficient Edge-preserving methods for large-scale dynamic inverse problems, July 19-23, 2021, @Mathematical Congress of the Americas 2021
17. An efficient edge preserving method for dynamic inverse problems, May 21 2021, @SAMSI Numerical Analysis for Data Science Workshop
16. An $\ell_p-\ell_q$ Variable projection method for large-scale separable nonlinear inverse problems, May 17 2021, @SIAM LA21
15. Computationally feasible methods based on Krylov subspaces to Solve large-scale, constrained, and time dependent inverse problems, April 23 2021, @University of South Carolina
14. Efficient edge-preserving methods for the solution of large-scale, time-dependent, dynamic inverse problems, April 9 2021, @Computational Methods Seminar, Kent State University
13. Krylov subspace methods as a tool to solve large-scale inverse problems and estimate maximum a posteriori for non-Gaussian noise, March 29 2021, @Graduate Mathematics Seminar, California State University Channel Island
12. On the Krylov subspace type methods to solve non-negative, large-scale inverse problems and estimate maximum a posteriori for non-Gaussian noise, March 25 2021, @Inverse Problems/Applied Math Seminar, Colorado State University, Fort Collins, Co.
11. A Nonnegative "\ell_p-\ell_q" Method for non-Gaussian noise, March 8 2021, @SIAM CSE21
10. Krylov meets Bregman: Sparse image reconstruction with nonnegativity constraint, January 2021, @JMM AWM-AMS Special Session Women of Color in Applied Math and Analysis
9. Krylov subspace type methods for the computation of non-negative or sparse solutions of ill-posed problems, July 2020, @SIAM Conference on Imaging Science (IS20), Toronto, Canada
8. Modulus-based iterative methods for constrained lp-lq minimization, April 2020, @Central Sectional Meeting Purdue University, West Lafayette, IN April 4-5, 2020 (cancelled)
7. Linearized Krylov subspace Bregman iteration with non-negativity constraint, April 2020, @Spring Central Sectional Meeting Purdue University, West Lafayette, IN April 4-5, 2020 (cancelled)
6. Image Reconstruction strategies by the aid of (generalized) Krylov subspaces on a constrained non-negative domain, December 2019, @John Carroll University, Cleveland, Ohio
5. Gaining knowledge about the data with natural language processing and machine learning, November 2019, @John Carroll University, Cleveland, Ohio
4. Explorations on matrix completion: From image inpainting to machine learning, October 2019, @Computational and Applied Mathematics Seminar, Kent State University
3. Mathematics and machine learning. How can a mathematician contribute in the future of technology?, September 2019, @Graduate Student Seminar, Kent State University
2. Linearized Bregman iteration with Krylov Subspaces, September 2019, @Graduate Student Seminar, Kent State University
1. Constrained Bregman method for inverse problems in Krylov subspaces, July 2019, @International Congress on Industrial and Applied Mathematics (ICIAM), Valencia (Spain)
Contributed talks:
13. Efficient learning methods for large-scale optimal inversion design, November 23, @Isaac Newton Institute for Mathematical Sciences.
12. An optimal experimental design approach to learn parameters for solving large-scale inverse problems, April 2021, @DASIV Spring School on Models and Data.
11. Linearized Krylov subspace Bregman iteration with nonnegativity constraint, October 2020, @AMS Fall Western Sectional Meeting (formerly at University of Utah).
10. Modulus-based iterative methods for constrained "\ell_p-\ell_q" minimization, October 2020, @AMS Fall Southeastern Sectional Meeting (formerly at University of Tennessee at Chattanooga).
9. Modulus-based iterative methods for constrained $\ell_p-\ell_q$ minimization, October 2020, @Communications in NLA online seminar.
8. Regularization methods based on Krylov subspace approach for nonnegative and sparse solution of large scale ill-posed problems, October 2020, @ Postdoc seminar Arizona State University.
7. Krylov type methods for the computation of nonnegative solutions for large-scale inverse problems, September 2020, @ SAMSI working group.
6. Krylov meets Bregman: Sparse image reconstruction with nonnegativity constraint, September 2020, @RTG seminar Arizona State University.
5. Linearized Krylov Subspace Bregman Iteration with Nonnegativity Constraint, May 2020, @Western Sectional Meeting California State University, Fresno, Fresno, CA May 2-3, 2020 (cancelled).
4. Modulus-based iterative methods for constrained $\ell_p-\ell_q$ minimization, May 2020, @SIAM Conference on Mathematics of Data Science (MDS20), (Postponed).
3. Projected nonnegative linearized Bregman on Krylov subspaces, February 2019, @Graduate Research Symposium, Kent State University.
2. Iterated Tikhonov with GSVD, May 2018, @Summer School “Computational Methods for Inverse Problems in Imaging”, Como, Italy.
1. Regularization methods and Iterated Tikhonov with GSVD, May 2018, @Mathematical Sciences Department, Polytechnic University, Tirana, Albania.
Poster Presentations:
5. Efficient learning methods for large-scale optimal inversion design, November 23, @Isaac Newton Institute for Mathematical Sciences. (Complement of the short contributed talk with the same title.)
4. Modulus based iterative methods for $\ell_p-\ell_q$ regularization, July 2020, Learning Models from Data: Model Reduction, System Identification and Machine Learning, @SAMM2020 Max-Planck Institute for Dynamics of Complex Technical Systems (virtual).
3. Krylov subspace type methods for the computation of non-negative or sparse solutions of ill-posed problems, April 2020, @Workshop II: PDE and Inverse Problem Methods in Machine Learning Part of the Long Program High Dimensional Hamilton-Jacobi PDEs (IPAM)(virtual).
2. Image Reconstruction by Linearized Bregman Interations in Krylov Subspaces, February 2019, @Graduate Research Symposium, Kent State University.
1. Modulus-Based Iterative Methods for Constrained $\ell_p-\ell_q$ regularization, April 2019, @First Student Stem Symposium, Kent State University.