Paul-Remo Wagner received his bachelor degree in civil engineering from ETH Zurich in September 2014.

After a year of internships abroad he started a MSc in structural engineering at ETH with a focus on metamaterials for seismic hazard mitigation and, in collaboration with EMPA, shape memory alloys for civil engineering applications. He prepared his master thesis in Spring 2016 as a visiting student researcher at the Department of Mechanical and Civil Engineering at Caltech (USA), extending his metamaterials research to the field of reliability-based optimization.

Since February 2017, Paul has been a Ph.D. student at the Chair of Risk, Safety and Uncertainty Quantification. He is working in the field of Bayesian inversion and model calibration using adaptive surrogate models and he is currently the main developer of the Bayesian inversion module for UQLab.

Damar Wicaksono received his Master's degree in nuclear engineering from both EPF Lausanne (EPFL) and ETH Zürich in 2012. After a couple of industrial and research internships in Switzerland, he started his Ph.D. research at the Paul Scherrer Institute (PSI) in 2013. The Ph.D. project "Bayesian Uncertainty Quantification of Physical Models in Thermal-Hydraulics System Codes" was jointly conducted at laboratories from EPFL and PSI. The project aimed at quantifying the uncertainty of numerous model parameters used in thermal-hydraulic simulation codes, which are important tools for assessing the safety of nuclear reactors. The project adapted and applied various techniques from global sensitivity analysis, metamodeling, and Bayesian statistics. Damar received his Doctoral degree in physics from EPFL in February 2018.

Damar joined the Chair of Risk, Safety and Uncertainty Quantification in June 2018 with the responsibility to develop a community platform for UQLab users and developers as well as to contribute to the development of the UQLab platform. He co-established and currently maintain UQWorld, an online community for applied UQ practitioners. As a UQLab developer, he contributes the Gaussian process regression feature in the Kriging module and the high-performance computing (HPC) dispatcher module.

Christos holds a Diploma in Mechanical Engineering from the National Technical University of Athens, focusing on industrial automation and control theory and an MSc. in robotics, systems, and control from ETH Zurich.

Back in 2013, after completing his Master's studies, Christos joined the Chair of Risk, Safety and Uncertainty Quantification as a full-time scientific developer contributing to the UQLab project. During that time, he participated in the development of several UQLab modules (Input, Model, Kriging, Support vector machines) as well as several front- and back- end services. Christos then started a Ph.D. at the Chair in September 2015, titled “Data-driven uncertainty quantification for high-dimensional engineering problems” under the co-supervision of Prof. Dr. Bruno Sudret and Dr. Stefano Marelli.

Starting from February 2020, Christos is a member of the Chair as a post-doctoral researcher primarily working on high-performance computing algorithms and their capitalization into UQLab, funded by the SAMOS project.

Emiliano Torre received his BSc (2009) and MSc (2011) in Mathematics from the University of Torino, Italy in 2011, where he specialized in probability and mathematical statistics. His MSc thesis concerned the use of copula models of temporal dependencies to generalize stochastic diffusions. In 2011, Emiliano joined the Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) of the Jülich Research Centre, Germany, where, as a PhD student of the RWTH Aachen University, he pursued his PhD in computational and statistical neuroscience. He obtained his PhD in 2016 with a thesis of pattern mining and efficient Monte Carlo statistical testing for massively parallel spike train data.

From 2016 to 2019, Emiliano was a joint research fellow at the Chair of Risk, Safety and Uncertainty Quantification and at the RiskLab of ETH Zürich. Together with Prof. Bruno Sudret and Prof. Emer. Paul Embrechts, he investigated copula dependence models for improving estimation in uncertainty quantification and machine learning problems. His research focus had been on risk and reliability analysis for computationally expensive models and data-driven predictions under data scarcity. As a UQLab developer, he was responsible for a major overhaul of the INPUT module, newly featuring vine copula models of input dependencies as well as statistical inference of probabilistic input models.

Philippe Wiederkehr received his MSc in civil engineering with majors in structural and geotechnical engineering from ETH Zurich in February 2018. His MSc research focused on sensitivity analysis for dependent input variables at the Chair of Risk, Safety and Uncertainty Quantification.

From 2018 to 2019, he was a scientific assistant at the Chair of Risk, Safety and Uncertainty Quantification, where he was working as a UQLab developer and research support staff. As such, he implemented new uncertainty quantification methods in the platform, improved and maintained the existing modules and provided end-user support.

Roland Schöbi graduated in 2012 with a MSc in Civil Engineering at ETH Zurich, with a thesis on subset simulation in engineering problems, in collaboration with the Hong Kong University of Science and Technology (HKUST). Afterwards, he worked for the chair of structural dynamics at ETH Zurich on the adaptation of the method of partially observable Markov decision processes (POMDP) to maintenance planning in civil engineering.

From 2013 to 2017, Roland was a PhD student at the Chair of Risk, Safety and Uncertainty Quantification at ETH Zurich. His research topics included structural reliability methods and methods to quantify uncertainty beyond probability theory. In parallel, he contributed to the development of the PC-Kriging and the reliability analysis modules for UQLab.

Originally from Greece, Katerina conducted her graduate studies at the University of California, Berkeley, where she earned a Master's and a PhD degree in Civil and Environmental Engineering and a Master's degree in Statistics. She thereafter held post-doctoral positions at the Civil Engineering Department of the Denmark Technical University (November 2011 - September 2013) and the Chair of Risk, Safety and Uncertainty Quantification of ETH Zürich (October 2013 - December 2016). Katerina's research has spanned the areas of uncertainty, sensitivity and reliability analysis, probabilistic modeling and decision making, including applications in earthquake and environmental engineering.

Charilaos Mylonas has acquired his diploma in Civil Engineering from Aristotle University of Thessaloniki where he conducted his thesis on Numerical Homogenization of Composite Structures with Finite Elements. He acquired his M.Sc. on Computational Science and Engineering (CSE) at ETH Zurich where he conducted his thesis in the Mathematics department on numerical Shape Optimization with the Boundary Element Method. As a CSE student he was introduced on Computational Statistics and Machine Learning techniques and performed an internship as a quantitative software developer in the banking sector.

He contributed to UQLab from December 2015 until September 2017 as a research assistant through development and integration of novel surrogate modelling and sensitivity analysis techniques and the maintenance of the existing ones.

Carlos Lamas Fernandez holds a diploma in Mathematics from the University of Santiago de Compostela, with a major in Applied Mathematics. During his thesis project, he studied the damage caused by high temperatures to simple civil engineering structures. After his studies, he joined the SGL Group in Germany, where he worked on the simulation of industrial processes and on the solution of inverse problems to determine material properties.

From August 2013 to August 2014, Carlos was a member of the Chair of Risk, Safety and Uncertainty Quantification at ETH Zürich, as a contributor to the development of the UQLab Uncertainty Quantification software framework.