Last updated June 2025
Invited Talks:
IT73: A Transport Map Approach for Bayesian Inference of Spatio-temporal Inverse Problems with Heavy-tailed, Priors November 17 - 20, 2025, @ SIAM Conference on Analysis of Partial Differential Equations (SIAM PD25), Pitsburg, PA, USA
IT72: Priorconditioned Sparsity-Promoting Projection Methods for Variational and Bayesian Linear Inverse Problems, October 6 — 10, 2025, @ Data Assimilation and Inverse Problems for Digital Twins Workshop, Institute for Mathematical and Statistical Innovations, Chicago, USA
IT71: Efficient Methods for Dynamic Image Reconstruction with Motion Estimation July 28-Aug. 1, 2025, @ Applied Inverse Problems Conference, Brazil
IT70: Efficient Methods for Dynamic Image Reconstruction with Motion Estimation June 23-27, 2025, @ 26th Conference of the International Linear Algebra Society ILAS2025,Kaohsiung, Taiwan
IT69: A Transport Map Approach for Bayesian Inference of Spatio-temporal Inverse Problems with Heavy-tailed Priors April, 2025, @ Kernel Methods in Uncertainty Quantification and Experimental Design Workshop, Institute for Mathematical and Statistical Innovations, University of Chicago, Chicago, USA
IT68: Statistical Methods for Large-scale Dynamic Inverse Problems March, 2025, @ NASC RTG Research Seminar, Rice University, Texas, USA
IT67: Statistical Methods for Large-scale Dynamic Inverse Problems March, 2025, @ SIAM CSE25, Fort Worth, Texas, USA, USA
IT66: Statistical Methods for Large-scale Dynamic Inverse Problems February, 2025, @ Data Science Seminar, John Hopkins, Maryland, USA
IT65: TRIPs-Py: Techniques for Regularization of Inverse Problems in Python November, 2024, @ CIL user meeting, Rutherford Appleton Laboratory, Oxfordshire, UK
IT64: Efficient Methods for Dynamic Image Reconstruction with Motion Estimation October, 2024, @ SIAM MDS 24, Atlanta, Georgia, USA
IT63: Efficient Methods for Dynamic Image Reconstruction with Motion Estimation September, 2024, @ Numerical Linear Algebra Conference, CIRM, Marseille, France
IT62: Edge-preserving Methods for Spatio-Temporal Inverse Problems June, 2024, @ North American High Order Methods Conference, Dartmouth College, Hanover, New Hampshire, USA
IT61: Matrix and Tensor Dictionary reconstruction for Static and Dynamic Inverse Problems May, 2024, @ SIAM IS 24, Atlanta, Georgia, USA
IT60: Methods with Edge-Preserving Priors for Spatio-Temporal Inverse Problems April, 2024, @ AMS Section Meeting, Washington DC, USA
IT59: Computationally Efficient Methods for Large-scale Inverse Problems March, 2024, @ Postdoc Seminar, Massachusetts Institute of Technology, Cambridge, MA, USA
IT58: Computationally Efficient Methods for Large-scale Inverse Problems February, 2024, @ Computational Science Seminars Lunchtime Computing, University of Massachusetts, Dartmouth, MA, USA
IT57: Methods with Edge-Preserving Priors for Spatio-Temporal Inverse Problems November, 2023, @ Colloquium in Applied Mathematics, University of Chicago, Chicago, USA
IT56: Recycling MMGKS for Large-Scale Dynamic and Streaming Data October, 2023, @ 2023 AWM Research Symposium, Georgia, USA
IT55: Methods with Edge-Preserving Priors for Spatio-Temporal Inverse Problems September, 2023, @ University of Helsinki, Finland
IT54: Recycling MMGKS for Large-Scale Dynamic and Streaming Data August, 2023, @ Numerical Analysis in the 21st Century, University of Oxford, UK
IT53: Spatio-temporal Besov Priors for Bayesian Inverse Problems July, 2023, @ Rising Stars on Data Assimilation, Potsdam, Germany.
IT52: Computational Challenges in Edge-Preserving Priors for Dynamic Inverse Problems June, 2023, @ Cambridge Image Analysis Seminar, University of Cambridge, Cambridge, UK.
IT52: Bayesian Spatio-Temporal Methods with Edge-Preserving Priors for Inverse Problems June, 2023, @ Foundations of Data Assimilation and Inverse Problems, (FoCM 2023), Paris, France
IT51: Bayesian Spatio-Temporal Methods with Edge-Preserving Priors for Inverse Problems May, 2023, @ SIAM Conference on Optimization, Seattle, Washington, USA
IT50: Computational Advancements in Edge-Preserving Methods for Dynamic Inverse Problems March 2023, @ Workshop on Rich and non-linear tomography in medical imaging, materials and non destructive testing, Isaac Newton Institute for Mathematical Sciences, UK
IT49: Bayesian Spatio-Temporal Methods with Edge-Preserving Priors for Inverse Problems March, 2023, @ Delft University of Technology, The Netherlands
IT48: Bayesian Spatio-Temporal Methods with Edge-Preserving Priors for Inverse Problems Feb., 2023, @ SIAM Conference on Computational Science and Engineering, Amsterdam, The Netherlands
IT47: Computational Advancements in Edge-Preserving Methods for Dynamic Inverse Problems Jan., 2023, @ Department of Mathematics, George Mason University, Virginia, USA
IT46: Computational Advancements in Edge-Preserving Methods for Dynamic Inverse Problems Jan., 2023, @ Department of Mathematics, Virginia Tech, Virginia, USA
IT45: Computational Advancements in Edge-Preserving Methods for Dynamic Inverse Problems Jan., 2023, @ Department of Mathematics, Florida State University, Florida, USA
IT44: Computational Advancements in Edge-Preserving Methods for Dynamic Inverse Problems Jan., 2023
@ Scientific Computing Department, Florida State University, Florida, USA
IT43: Bayesian Spatio-Temporal Methods with Edge-Preserving Priors for Inverse Problems Jan., 2023, @ Joint Mathematical Meetings, Boston, Massachusetts, USA
IT42: Bayesian and Deterministic Methods with Edge-Preserving Priors for Dynamic Inverse Problems Dec., 2022, @ John Hopkins University, USA
IT41: Bayesian and Deterministic Methods with Edge-Preserving Priors for Dynamic Inverse Problems Dec., 2022, @ University of Potsdam, Germany
IT40: Bayesian Spatio-Temporal Methods with Edge-Preserving Priors for Inverse Problems Nov., 2022, @ CUQI seminar, Technical University of Denmark, Denmark
IT39: Modern Challenges in Large-Scale and High-Dimensional Data Analysis Nov., 2022, @ Data Science and Machine Learning Seminar, Florida State University, Florida, USA
IT38: On Deterministic and Statistical Methods for Large-scale Dynamic Inverse Problems. Sep., 2022, @ City University of New York
IT37: 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 Princeton University
IT36: On Deterministic and Statistical Methods for Large-scale Dynamic Inverse Problems. May 21-26, 2022, @ Householder Symposium XXI on Numerical Linear Algebra
IT35: On Deterministic and Statistical Methods for Large-scale Dynamic Inverse Problems. April 20-21, 2022, @ Rising Stars in Computational and Data Science Workshop
IT34: From Challenges to Edge-preserving Methods for Large-scale Dynamic Inverse Problems April 4-8, 2022, @ SIAM Copper Mountain Conference on Iterative Methods (virtual)
IT33: Majorization Minimization-based Laplace Approximation for Bayesian Inverse Problems with Arbitrary Prior and Noise Models. April 12-15, 2022, @ SIAM Conference on Uncertainty Quantification
IT32: New Deterministic and Learning Techniques for Solving Large-scale Inverse Problems. April, 2022, @ Joint Math Meeting 2022 Special Session on Computer Vision
IT31: Edge-preserving Methods for Large-scale Dynamic Inverse Problems. March 26-27, 2022, @ Spring Central Sectional Meeting, Purdue University, West Lafayette, IN
IT310: Edge-preserving Methods for Large-scale Dynamic Inverse Problems. March 22-25, 2022, @ SIAM Conference on Imaging Science 2022
IT29: Seeing the Invisible: Mathematics of Imaging. March 18, 2022, @ Celebration of Women in Mathematics, Arizona State University
IT28: On Deterministic and Statistical Methods for Large-scale Dynamic Inverse Problems. March 7, 2022, @ Tufts University
IT27: A Journey on Computational and Learning Methods for Large-scale Inverse Problems. Feb. 16, 2022, @ Arizona State University, Postdoc Seminar
IT26: Computational and Learning Methods for Large-scale Inverse Problems. Jan. 18, 2022, @ Rochester Institute of Technology
IT25: Some Recent Advancements on Solving Large-scale Inverse Problems. Oct. 2021, @ 2021 Fall Western Sectional Meeting
IT24: Computational Methods for Large-scale Inverse Problems. Oct. 2021, @ Temple University
IT23: Some Deterministic and Learning Krylov Subspaces Methods for Large-scale Inverse Problems. Oct. 2021, @ 2021 Kansas State University
IT22: New Deterministic and Learning Approaches for Large-scale Inverse Problems. Sept. 2021, @Research Training Group (RTG), Arizona State University
IT21: From Static to Dynamic Inverse Problems: New Edge-preserving Methods. Sept. 2021, @ SIAM Southeastern Atlantic Section Conference - Auburn University
IT20: Some Old and New Dimension-reduction Techniques to Solve Large-scale Inverse Problems. Aug. 2021, @ Presse Lab, Arizona State University
IT19: Efficient Edge-preserving Methods for Large-scale Dynamic Inverse Problems. July 19-23, 2021, @ Mathematical Congress of the Americas 2021
IT18: An Efficient Edge Preserving Method for Dynamic Inverse Problems. May 21 2021, @ SAMSI Numerical Analysis for Data Science Workshop
IT17: An `p Variable Projection Method for Large-scale Separable Nonlinear Inverse Problems. May 17 2021, @ SIAM LA21
IT16: Computationally Feasible Methods Mased on Krylov Subspaces to Solve Large-scale, Constrained, and Time Dependent Inverse Problems. April 23 2021, @ University of South Carolina
IT15: Efficient Edge-preserving Methods for Large-scale, Time-dependent Inverse Problems. April 9 2021, @ Computational Methods Seminar, Kent State University
IT14: 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
IT13: On the Krylov Subspace-type Methods to solve Non-negative, Large-scale Inverse Problems and Estimate Max-
imum a Posteriori for Non-Gaussian Noise. March 25 2021, @ Inverse Problems/Applied Math Seminar, Colorado State University, Fort Collins, Co.
IT12: A Nonnegative `p − `q Method for Non-Gaussian Noise. March 8 2021, @ SIAM CSE21
IT11: Talk title: Krylov Meets Bregman: Sparse Image Reconstruction with Nonnegativity Constraint. Jan. 2021, @ JMM AWM-AMS Special Session Women of Color in Applied Math and Analysis, (held virtually due to COVID-19)
IT10: Talk title: 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 (held virtually due to COVID-19)
IT9: Talk title: Modulus-based iterative methods for constrained lp-lq minimization. April 2020, @ Central Sectional Meeting Purdue University, West Lafayette, IN April 4-5, 2020 (cancelled)
IT8: Talk title: 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)
IT7: Talk title: Image Reconstruction strategies by the aid of (generalized) Krylov subspaces on a constrained nonnegative domain. Dec. 2019, @ John Carroll University, Cleveland, Ohio
IT6: Talk title: Gaining Knowledge about Data with Natural Language Processing and Machine Learning.Nov. 2019, @ John Carroll University, Cleveland, Ohio
IT5: Talk title: Explorations on Matrix Completion: From Image Inpainting to Machine Learning. Oct. 2019, @ Computational and Applied Mathematics Seminar, Kent State University
IT4: Mathematics and Machine Learning. How Can a Mathematician Contribute in the Future of Technology? Sept.2019, @ Graduate Student Seminar, Kent State University
IT3: Linearized Bregman Iteration with Krylov Subspaces. Sept. 2019, @ Graduate Student Seminar, Kent State University
IT2: Constrained Bregman Method for Inverse Problems in Krylov Subspaces. July 2019, @ International Congress on Industrial and Applied Mathematics (ICIAM), Valencia (Spain)
IT1: Regularization Methods and Iterated Tikhonov with GSVD. May 2018, @ Mathematical Sciences Department, Polytechnic University, Tirana, Albania
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.