MOBIKO

Yousra studied Mathematics and Computational Statistics. She did a PhD in Statistical Learning followed by a post-doc both at EPFL, where she developed empirical Bayes methods for automatic L2 regularization problems in smooth regression for big data. Before joining SDSC, she worked at SIB/UNIL, where she developed a statistical optimization solution to the parent-of-origin identification problem in human genetics. Her technical expertise includes supervised learning, numerical optimization for machine learning, statistical modeling and methodology, high-performance and distributed computing for big data, Bayesian computation and time series analysis. She worked on applied problems in quantitative finance and environmental sciences.

Carl holds a Ph.D in Mathematics from École des Ponts ParisTech and Université Gustave Eiffel in Paris. He has broad interests in statistics and stochastic control, and works on reinforcement learning, generative methods and time series forecasting, with applications in various domains such as energy, finance and physics. He worked with EDF R&D and Finance des Marchés de l’Energie (FiME) laboratory on applications of machine learning to risk management, including time series generation and deep hedging. He joined the SDSC in 2022 as a senior data scientist in the academic team at École Polytechnique Fédérale de Lausanne (EPFL).

Anisia Katinskaia joined the SDSC in August 2025 as a Senior Data Scientist in the Research team in Lausanne.
Anisia is an NLP researcher with a PhD in Computer Science from the University of Helsinki, Finland. She also holds a Master's degree in Computational Linguistics from the Russian State University for the Humanities. Anisia's research has primarily focused on applying NLP to education, particularly in the context of the development of Revita, an online Intelligent Tutoring System for language learning. During her time at the University of Helsinki, she worked on grammatical error detection and correction, text difficulty estimation, text simplification, low-resource NLP, model interpretability via probing, tokenization for language models, and the development of educational LLM-based agents.

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Luis is originally from Spain, where he completed his bachelor's studies in Electrical engineering, and the Ms.C. on signal theory and communications, both at the University of Seville. During his Ph.D. he started focusing on machine learning methods, more specifically message passing techniques for channel coding, and Bayesian methods for channel equalization. He carried it out between the University of Seville and the University Carlos III in Madrid, also spending some time at the EPFL, Switzerland, and Bell Labs, USA, where he worked on advanced techniques for optical channel coding. When he completed his Ph.D. in 2013, he moved to the Luxembourg Center on Systems Biomedicine, where he switched his interest to neuroscience, neuroimaging, life sciences, etc., and the application of machine learning techniques to these fields. During his 4 and a half years there as a Postdoc, he worked on many different problems as a data scientist, encompassing topics such as microscopy image analysis, neuroimaging, single-cell gene expression analysis, etc. He joined the SDSC in April 2018.  As Lead Data Scientist, Luis coordinates projects in various domains. Several projects focus on the application of natural language processing and knowledge graphs to the study of different phenomena in social and political sciences. In the domains of architecture and engineering, Luis is responsible for projects centered on the application of novel generative methods to parametric modeling. Finally, Luis also coordinates different projects in robotics, ranging from collaborative robotic construction to deformable object manipulation.