1. Tikhonov functionals with a tolerance measure introduced in the regularization
by Iwona Piotrowska-Kurczewski and Georgia Sfakianaki.
Available online at: https://arxiv.org/abs/2007.06431

5. Deep Learning and Inverse Problems Autumn School.
University of Bremen, Germany. November 04-08, 2019.
Participation.

4. Annual Meeting of the International Association of Applied Mathematics and Mechanics - GAMM 2019.
TU Vienna, Austria. February 18-22, 2019.
Talk title: "Tikhonov regularization including tolerances in parameter space".

3. Chemnitz Symposium on Inverse Problems 2018.
TU Chemnitz, Germany. September 27-28, 2018.
Talk title: "On the Connection Between Sparsity and Tolerances in Parameter and Data Space".

2. Franco-German Summer School Inverse Problems and Imaging.
University of Bremen, Germany. September 18-22, 2017.
Participation.

1. Women in PDEs @ Karlsruhe.
KIT University, Karlsruhe, Germany. April 27—28, 2017.
Participation.

4. Seminar presentation at the Research Training Group π³.
University of Bremen, Germany. June 19, 2019.
Talk title: "Tikhonov regularization incorporating tolerances in the penalty term".

3. Presentation in the Data Analysis & Inverse Problems in Engineering Seminar, at the Center for Industrial Mathematics.
University of Bremen, Germany. April 30, 2019.
Talk title: "Regularization of ill-posed inverse problems".

2. Seminar presentation at the Department of Mathematics and Applied Mathematics.
University of Crete, Greece. November 22, 2018.
Talk title: "Tikhonov regularization for the solution of inverse problems".

1. Seminar presentation at the Research Training Group π³.
University of Bremen, Germany. January 24, 2018.
Talk title: "On the Connection Between Sparsity and Tolerances in Parameter and Data Space".

Data Assimilation Methods (in English) -- my Master's thesis under the supervision of Prof. Michael Plexousakis.
Description: In this thesis, I studied variational (3D-Var, 4D-Var) and sequential (Kalman Filter and its variants Extended (EKF) and Ensemble Kalman Filter (EnKF)) data assimilation methods under the prism of numerical weather prediction (NWP). I implemented numerically and tested the 3D-Var and EnKF methods applied to the Lorenz 96 model, a dynamical system which is frequently used as a toy-model in NWP as it shares certain properties with many atmospheric models.
Here you will find a poster about my Master's thesis.

Principal Component Analysis (in Greek) -- my Bachelor's thesis under the supervision of Prof. Michael Plexousakis.
Description: In this thesis, I studied the principal component analysis (PCA) for dimensionality reduction and data processing. In particular, I applied PCA to different examples, such as

• feature extraction from a data set concerning the eating habits of people in four different areas of the UK
• classification of iris flowers based on four characteristics (length and width of sepals and petals)
• image compression of greyscale and RGB images
• dimensionality reduction of time-dependent meteorological data sets

Linear Inverse Problems: 1-week compact course (lectures & exercise sessions) for PhD students, tutoring in English

Data analysis and inverse problems in engineering: semester accompanying seminar for graduates, offered in English

Theory of linear inverse problems: co-organizer of exercise sessions, graduate course, tutoring in English

Programming in Python: lab sessions, undergraduate course, tutoring in Greek