I held the ceremonial public defence of my thesis on March 5th, 2021, and officially received the title Docteur ès Sciences (PhD).
I gave an invited talk at the Computational and Methodological Statistics conference (CMStatistics 2020) that took place in December 2020. In my talk entitled I spoke primarily about my second paper as well as my case study of US Treasury yield curve and its interaction with the US macroeconomy indicators.
I defended my PhD thesis on September 17, 2020, at Swiss Federal Institute of Technology in Lausanne (EPFL). The president of my jury, Sofia Olhede, and the examiners Łukasz Kidziński, Shahin Tavakoli, and Stephan Morgenthaler as well as my thesis advisor, Victor Panaretos, gave me constructive feedback and I thank them for the overall pleasant experience.
In July 2019 I had the opportunity to travel to Palermo, Italy, to give a talk at the European Meeting of Statisticians, the largest statistics conference in Europe. In my talk entitled “Sparsely observed functional time series: estimation and prediction” I spoke about my first paper carrying the same name.
The colleagues from Fribourg organised Doctoral day in June 2019 for the PhD students of the universities in western Switzerland (Conférence universitaire de Suisse occidentale, CUSO). I gave a talk on my core research around Sparsely observed functional time series.
In April 2019 I travelled to Limassol, the Republic of Cyprus, where I attended a workshop on multivariate data analysis. This workshop was the first conference where I’ve given a talk.
In September 2018 I had chance to follow my PhD supervisor, Victor Panaretos, to Copenhagen, Denmark, and attend this workshop and listen to the presentations of leading experts on time series analysis and stochastic processes.
In September 2018 I flew to Iași, Romania, to take part in the 2nd CRoNoS Summer Course on Functional Data Analysis. I followed two summer school courses and listened to some conference talks.
In June 2017 I travelled to Tübingen, Germany, to follow two weeks long series of lectures in machine learning. The speakers covered topics including deep learning, reinforcement learning, causality, network analysis, optimisation, Bayesian inference and much more.