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Simon joined the SDSC as a senior data scientist in April 2022. He conducted his doctoral studies on statistical modeling of genetic data at ETH Zürich and obtained his MSc and BSc degrees at Technical University Munich in computer science. Before joining the SDSC, Simon worked as a freelance statistical consultant, and as an ML scientist at an AI startup in Lugano where he built experience in various topics ranging from generative modeling over Bayesian optimization to time series forecasting. Simon's research interests and expertise lie broadly in probabilistic machine and deep learning, causal inference, generative modeling, and their application in the natural sciences. Simon is an avid open-source software contributor and particularly enthusiastic about probabilistic programming languages, such as Stan.

During 2023, Ilnura was doing a postdoc at the Technion, Israel, working with Prof. Dr. Kfir Levy. She received her PhD in September 2022 from the Automatic Control Laboratory (IfA) at ETH, Zürich. She joined IfA in 2017 to the Prof. Dr. Maryam Kamgarpour group and was co-supervised by Prof. Dr. Andreas Krause, working on safe learning methods. She obtained her Master's Degree jointly in Applied Mathematics at Institut Polytechnique de Grenoble (France) and Moscow Institute of Physics and Technology (Russia) in 2017. Her Master's thesis research was on stochastic optimization theory under the supervision of Prof. Dr. Anatoli Juditsky. Her Bachelor of Physics and Applied Math was obtained from the Moscow Institute of Physics and Technology (Russia) in 2015. Ilnura was born and finished high school in Almaty, Kazakhstan.

Johannes’ research focuses on developing practical andprincipled algorithms for sequential decision-making. His expertise spans awide range of topics from reinforcement learning theory and control, Bayesian optimization, safety and robustness to modern deep learning. He worked on challenging application domains, including deploying state-of-the-art data-driven optimization algorithms on two particle accelerators at the Paul Scherrer Institute. Before joining the SDSC in August 2023, Johannes was a postdoctoral researcher at the University of Alberta and completed an internship at Google DeepMind. Johannes earned his PhD in Computer Science in 2016 at ETH Zurich with Prof. Andreas Krause, and he holds a Master in Mathematics from ETH Zurich.

Benjamín Béjar received a PhD in Electrical Engineering from Universidad Politécnica de Madrid in 2012. He served as a postdoctoral fellow at École Polytechnique Fédérale de Lausanne until 2017, and then he moved to Johns Hopkins University where he held a Research Faculty position until Dec. 2019. His research interests lie at the intersection of signal processing and machine learning methods, and he has worked on topics such as sparse signal recovery, time-series analysis, and computer vision methods with special emphasis on biomedical applications. Since 2021, Benjamin leads the SDSC office at the Paul Scherrer Institute in Villigen.

Xiaoran Chen joined SDSC as a senior data scientist in July 2022. Prior to this, she received her PhD at ETH Zurich in 2021. Her research was focused on unsupervised learning and anomaly detection on magnetic resonance imaging (MRI) scans. She also holds a master’s degree in bioinformatics and bachelor’s degree in biological science. Her research interest includes self-supervised learning, representation learning and general applications using machine learning methods.

Luis Barba Flores joined the SDSC in 2022 as Senior Data Scientist. He received a joined PhD in Computers Science in 2016 from the Université Libre de Bruxelles and Carleton University. He served as a postdoctoral researcher at ETH Zurich from 2016 to 2019, and then moved to EPFL Lausanne to work in the Machine Learning and Optimization Group until 2022. His research interests include distributed optimization algorithms, first-order optimization methods and their applications to Deep Learning models.

Luke completed his PhD in oceanography at the University of Cape Town in 2017.

After a short stint at the Council for Scientific and Industrial Research in Cape Town, he joined the Environmental Physics group at ETH Zürich as a postdoc in 2019.  Here, his research focussed on ocean biogeochemistry.  Specifically, he used machine learning approaches to estimate surface ocean pH and surface ocean carbon fluxes from satellite and ship-based observations.

Luke has also been involved developing scientific data processing tools for several autonomous observation platforms.

Konstantinos obtained a Master's degree in Mechanical Engineering from the Technical University of Delft, Netherlands and conducted his doctoral studies in the Chair of Structural Mechanics and Monitoring at ETH Zurich with a focus on the fusion of physics-based and data-driven models for vibration-based monitoring of structural and mechanical systems. Before joining SDSC, he was a postdoctoral researcher at ETH Zurich. His research interests revolve around machine learning, uncertainty quantification, inference of probabilistic models, time series forecasting and Bayesian modeling.

Luke completed his PhD in oceanography at the University of Cape Town in 2017.

After a short stint at the Council for Scientific and Industrial Research in Cape Town, he joined the Environmental Physics group at ETH Zürich as a postdoc in 2019.  Here, his research focussed on ocean biogeochemistry.  Specifically, he used machine learning approaches to estimate surface ocean pH and surface ocean carbon fluxes from satellite and ship-based observations.

Luke has also been involved developing scientific data processing tools for several autonomous observation platforms.

Luis Barba Flores joined the SDSC in 2022 as Senior Data Scientist. He received a joined PhD in Computers Science in 2016 from the Université Libre de Bruxelles and Carleton University. He served as a postdoctoral researcher at ETH Zurich from 2016 to 2019, and then moved to EPFL Lausanne to work in the Machine Learning and Optimization Group until 2022. His research interests include distributed optimization algorithms, first-order optimization methods and their applications to Deep Learning models.

Johan joined the SDSC industry cell in May 2019. After completing his M.Sc. in Computer Science at EPFL, he worked for a few years as a consultant in the industry. Specialized in natural language processing and computer vision, he loves challenges in document analysis and knowledge extraction.

Suman pursued his doctoral studies in the Visual Artificial Intelligence Laboratory at Oxford Brookes University, United Kingdom, focusing on spatiotemporal human action localization using deep learning techniques. Suman received a Ph.D. in Computer Science and Mathematics from Oxford Brookes University in 2017. He served as a postdoctoral fellow at Oxford Brookes University between December 2017 and July 2018. Then he moved to CVL (Computer Vision Lab) at ETH Zurich, holding a postdoctoral researcher position until June 2023. Suman's research has centered around unsupervised domain adaptation (UDA) for visual scene understanding (semantic and panoptic segmentation), human behavior understanding, and vision-based biometrics (face anti-spoofing). He also worked on semi-supervised learning for semantic segmentation by leveraging self-supervised depth estimation. His internship at Disney Research Zurich involved designing deep generative models for unsupervised facial expression learning. Additionally, Suman tackled research problems in multi-task learning (MTL) by addressing two common challenges in developing multi-task models, incremental learning and task interference.

Johannes’ research focuses on developing practical andprincipled algorithms for sequential decision-making. His expertise spans awide range of topics from reinforcement learning theory and control, Bayesian optimization, safety and robustness to modern deep learning. He worked on challenging application domains, including deploying state-of-the-art data-driven optimization algorithms on two particle accelerators at the Paul Scherrer Institute. Before joining the SDSC in August 2023, Johannes was a postdoctoral researcher at the University of Alberta and completed an internship at Google DeepMind. Johannes earned his PhD in Computer Science in 2016 at ETH Zurich with Prof. Andreas Krause, and he holds a Master in Mathematics from ETH Zurich.

Johannes’ research focuses on developing practical andprincipled algorithms for sequential decision-making. His expertise spans awide range of topics from reinforcement learning theory and control, Bayesian optimization, safety and robustness to modern deep learning. He worked on challenging application domains, including deploying state-of-the-art data-driven optimization algorithms on two particle accelerators at the Paul Scherrer Institute. Before joining the SDSC in August 2023, Johannes was a postdoctoral researcher at the University of Alberta and completed an internship at Google DeepMind. Johannes earned his PhD in Computer Science in 2016 at ETH Zurich with Prof. Andreas Krause, and he holds a Master in Mathematics from ETH Zurich.

Paul joined the SCSC in July 2023. He received a PhD in Computer Science from EPFL, focusing on sampling and optimisation algorithms, Causality, robust Machine Learning and Reinforcement Learning. Prior to his PhD, he obtained a Bachelor degree in Physics and a Master in Computational Science and Engineering at EPFL. During his studies, he focused on the numerical integration of Ordinary and Stochastic differential equations, in addition to Machine Learning.