(Scientific paper written with Lioudmila Vostrikova and published in "Theory of Probability and its Applications", august 2020)
(Scientific paper written with Yuchao Dong and published in "Insurance: Mathematics and Economics", march 2020)
In this paper, we developed a new functional limit theorem similar to Donsker's theorem and the Cox-Ross-Rubinstein binomial approximation which shows that Generalized Ornstein-Uhlenbeck processes appear naturally in numerous problems in applied probability. The preprint version is available on arXiv.
(PhD thesis, defended in december 2019, supervised by Lioudmila Vostrikova)
In my thesis, I studied Generalized Ornstein-Uhlenbeck processes in the context of applied probability and, in particular insurance mathematics. The presentation slides of the defense (PDF) are available by following this link. The full thesis (PDF) is available by following this link.
(Python code, since september 2020)
In this project, I implemented a few useful data science functions in Python. In the first part concerning exploratory data analysis and based on mdUtils, Sklearn and XGBoost, I created a script to generate a Markdown summary file from a Pandas DataFrame and I implemented the "gradient boosting" method for linear models. The code is available on GitHub. In the second part concerning classification problems, I used the Keras-Sklearn wrapper to tune the hyper-parameters of a neural network using Sklearn's "GridSearchCV" function. The code is available on GitHub.
(Python application, since may 2020)
Note Binder is a tool to organise multiple text files in one place. It creates a database linking to the files and gives access through a simple user interface. The goal is to create an offline and movable "note book" which supports different file types and encryption. Created using Python, PyQt5 and sqlite3. The code and a macOS binary are available on GitHub.
(Series of blog posts and Python code, since march 2020)
(Music and DJing project, since 2018)