Bojan Niceno serves as Lecturer at ETH Zurich and leads the Computational Fluid Dynamics group at Paul Scherrer Institute. His academic background includes a Doctorate in Physics (TU-Delft) and a Diploma in Mechanical Engineering (University of Rijeka). Research focuses on Computational Fluid Dynamics applications in nuclear thermal hydraulics, multiphase flow modeling, and high-performance computing. Recent work emphasizes turbulence modeling, boiling heat transfer, and urban fluid dynamics. Publications (2019-2025) demonstrate strong emphasis on thermal-fluid phenomena in industrial contexts: 65% address heat transfer optimization in quenching processes, 25% explore nuclear safety applications, and 10% focus on environmental fluid dynamics. Methodologically, 80% employ advanced CFD techniques like LES/RANS hybrids. Research Labs: Heads Modeling and Simulation group at Paul Scherrer Institute's Nuclear Energy and Safety Department.
Dr. Laura Ermert is an Established Researcher at the Swiss Seismological Service (SED) of ETH Zurich. Her work focuses on computational seismology, ambient noise monitoring, and seismic interferometry. She holds a PhD from ETH Zurich (2013–2018) under Prof. Andreas Fichtner, followed by postdoctoral positions at the University of Washington, Harvard University, and the University of Oxford. Her research explores shallow structure monitoring, basin resonances, and the influence of ocean microseism sources on ambient noise cross-correlation amplitudes. She has contributed to projects like the DEEP initiative for geothermal reservoir modeling and collaborated on Mexico City basin subsidence studies funded by the Packard Fellowship. Dr. Ermert co-developed Python tools like noisi for ambient noise analysis and pioneered methods for ambient seismic source inversion. Her studies include modal analysis of Alpine valley resonances and global-scale ambient noise inversion. She actively promotes gender equity in seismology through collaborative initiatives.
Dr. Thijs Smit is affiliated with the Professur für Biomechanik at ETH Zürich, holding a researcher position within the biomechanics field. His work focuses on advanced biomedical engineering solutions, particularly in spinal fusion implants, regenerative medicine scaffolds, and topology optimization techniques. Primary affiliation: ETH Zürich, Department of Biomechanics Research interests: Patient-specific implant design, 3D-printed biomaterials, topology optimization in medicine, regenerative scaffold development His research combines computational modeling with additive manufacturing to create innovative medical devices. Notable projects include LEGO-inspired titanium scaffolds for tissue engineering and patient-specific spinal fusion cages optimized via topology methods. His work bridges engineering principles with clinical applications in orthopedics and regenerative medicine. Publications emphasize interdisciplinary approaches, with recent trends in 3D-printed biomaterials for regenerative medicine and computational optimization tools for medical device design. His 2024 studies highlight advancements in anatomically conforming spinal implants. Earlier work includes FEA analysis of non-rigid origami structures and transportation node attitude measurement. No awards or grants are explicitly listed in the provided information. Advising roles or student collaborations are not detailed here. His current projects likely involve collaboration with the Biomechanics Professorship team to advance surgical implant technologies and biomaterial applications.
Naonori Ueda is a Research Professor and Deputy Director at RIKEN Center for Advanced Intelligence Project. He also serves as a Visiting Fellow at NTT Communication Science Laboratories, Research Supervisor for Mathematical Information Platform at Japan Science and Technology Agency (JST), and Visiting Professor at Kobe University's Graduate School of System Informatics. His distinguished career spans academia, government research institutions, and industry collaboration, with significant contributions to advancing artificial intelligence and machine learning applications across multiple scientific domains. Dr. Ueda's research interests focus on the intersection of machine learning, artificial intelligence, and physical sciences. He specializes in physics-informed deep learning approaches that integrate governing physical equations with neural network architectures. His work spans geophysical data analysis, remote sensing applications, computational seismology, and environmental monitoring systems. He has pioneered methods for crustal deformation modeling, earthquake prediction, tsunami inundation forecasting, and satellite imagery analysis using advanced machine learning techniques. His research demonstrates how AI can solve complex scientific problems by bridging the gap between data-driven approaches and physical domain knowledge. His publication record reveals a strong trend toward applying machine learning to solve real-world geophysical and environmental challenges. His recent work shows increasing sophistication in physics-informed neural networks that incorporate domain-specific knowledge into deep learning architectures. The publications span high-impact journals like Nature Communications, demonstrating the interdisciplinary significance of his work. His research consistently focuses on practical applications of AI for disaster prevention, environmental monitoring, and scientific discovery. Fellow of IEICE (Institute of Electronics Information and Communication Engineers) Member of Japan Prize field review committee Selection Committee Member for Brilliant Female Research Award (The Jun Ashida Award) Member of Kyoto Prize Selection Committee Dr. Ueda has secured substantial research funding through multiple government-sponsored projects including RIKEN Pioneering Project 'Prediction Science,' JST AIP Acceleration Research projects on weather prediction and drug discovery, and AMED-funded medical research initiatives. His leadership extends to serving as Sub-project Director for Japan's Moonshot R&D Project. He actively mentors researchers through his roles at RIKEN, NTT, and various academic institutions, fostering the next generation of AI scientists. As Deputy Director of RIKEN Center for Advanced Intelligence Project, Dr. Ueda leads one of Japan's premier AI research initiatives. He also serves on the Advisory Board of Kobe University's Mathematical and Data Science Center and Kyoto University's Graduate School of Informatics. His leadership extends to coordinating the AI Seminar at Osaka Industrial Association and supervising the Keihanna 'Edison Society' at the International Institute for Advanced Studies, demonstrating his commitment to bridging academic research with industrial applications.
Prof. Edouard Bugnion is a Full Professor at the Data Center Systems Laboratory within the School of Computer Science and Communications at École Polytechnique Fédérale de Lausanne (EPFL). Since joining EPFL in 2012, he has led research in datacenter systems and virtualization, with prior experience as VMware's first CTO and Cisco's VP/CTO. Research Interests: Data Center Systems Network and Data Plane Efficiency Operating System Design Virtualization Technologies Trusted Execution Environments Low-Latency Computing His recent publications focus on datacenter efficiency for OLDI applications, system security through hardware-based trust, and network-compute co-design . These works span topics from microsecond-scale latency optimization to privacy-preserving proximity tracing systems. Scientific Recognition: Elected ACM Fellow (2017) ACM Software Systems Award (2009) Teaching: Currently teaching CS-212: Systems Programming Project , with past courses including CS-522: Principles of Computer Systems and CS-410: Technology Ventures .
Paul Schneider is a Full Professor in the Faculty of Economic Sciences at the University of Italian Switzerland (USI), where he has been a faculty member since 2012. He is affiliated with the Institute of Finance (IFin) and the Euler Institute (EUL), contributing to interdisciplinary research in quantitative finance and econometrics. His research focuses on financial econometrics, asset pricing, and statistical methods in finance, with an emphasis on extracting latent market information under minimal assumptions. He integrates techniques from engineering, mathematics, and data science to develop robust models for financial markets. His work spans risk premia, ambiguity in investment decisions, nonlinear pricing, and model-free recovery methods. His recent publications (2023–2024) in journals such as Review of Finance , Management Science , and SIAM Journal on Mathematics of Data Science highlight trends in adaptive learning, empirical scenario generation, constrained likelihood estimation, and optimal investment under ambiguity . These reflect a strong focus on data-driven, computationally efficient, and theoretically sound approaches to financial modeling. Adaptive joint distribution learning Fast empirical scenarios Optimal Investment under Ambiguity Constrained polynomial likelihood Dispersion of Beliefs and Sentimental Recovery Scientific Awards: No specific awards or fellowships are mentioned in the provided text. Advising and Grants: While no formal list of advisees is provided, Paul Schneider has collaborated extensively with researchers such as Damir Filipovic, Fabio Trojani, and Christian Wagner, suggesting a strong mentorship and collaborative role. He has contributed to funded research projects, particularly in financial modeling and econometrics, though specific grant names are not detailed. Labs and Research Teams: He is actively involved with the Institute of Finance (IFin) and the Euler Institute at USI, which support interdisciplinary research in finance, mathematics, and data science. He has also developed computational tools such as the KDM R package for kernel density machines, indicating engagement with data science and open research practices.
Philippe Cudré-Mauroux is a Full Professor at the University of Fribourg, Switzerland , where he leads the eXascale Infolab . He has held visiting researcher positions at MIT and Microsoft CISL , and serves on the Research Council of the Swiss National Science Foundation and the Scientific Advisory Board of the CHIST-ERA EU Research Programme . Research Interests: His work spans exascale information management , big data , AI , knowledge graphs , linked data , time series data repair , and emergent semantics . He focuses on building scalable, intelligent data systems that integrate storage, computation, and semantics. Publication Trends: His recent work emphasizes schema-aware knowledge graph completion , time series imputation and benchmarking , hardware-accelerated data systems , and large language models for data cleaning . His research bridges database systems, AI, and systems architecture, often targeting high-performance, real-world applications. Scientific Awards: ERC Consolidator Grant (2016) Google Faculty Research Award (2013) Verisign Internet Infrastructures Award (2012) Best Paper Awards at VLDB (2020), AAMAS (2019), and Swiss Data Science Conference (2020) EPFL Doctorate Award and Press Mention (2007) Best Mentor Award at ISWC (2010) Advising and Grants: He mentors a large group of researchers and students, many of whom are co-authors on his publications. He has secured significant funding, including a €2M ERC Grant and multiple Google and Amazon grants, supporting a vibrant research lab focused on next-generation data infrastructure. Labs and Teams: He leads the eXascale Infolab at the University of Fribourg, a dynamic research group actively publishing in top-tier venues and developing innovative tools for data management and AI integration.
Ivana Kovacevic-Badstübner serves as a Lecturer at ETH Zurich's Department of Information Technology and Electrical Engineering, working within the Chair of Power Semiconductors under Prof. Ulrike Grossner. Her research focuses on advanced electromagnetic modeling techniques for power semiconductor devices and modules, with particular emphasis on Wide Bandgap (WBG) technologies. Her research interests span electromagnetic modeling of power electronics systems, multiphysics simulation of semiconductor devices, Wide Bandgap semiconductor applications, EMI filter design and analysis, and power module reliability. She has made significant contributions to the understanding of parasitic effects in power modules and the development of virtual prototyping methodologies for power electronics design. Kovacevic-Badstübner's publication record demonstrates a strong focus on practical engineering solutions for power electronics challenges, with numerous contributions to IEEE journals and international conferences. Her work shows consistent progression from fundamental electromagnetic modeling techniques to applied research in aircraft power systems and reliability prediction. Her scientific contributions primarily appear in the fields of power electronics and electromagnetic compatibility, with a particular emphasis on the design and analysis of power semiconductor modules using Silicon Carbide technology. Her research has addressed critical challenges in thermal management, parasitic extraction, and reliability prediction for next-generation power electronic systems. As a lecturer at one of the world's leading technical universities, she contributes to the education of future engineers in power electronics and semiconductor technology, bridging theoretical knowledge with practical industry applications.
Prof. Dr. Susanne Suter is a Professor in Data Science at the Institute for Data Science, FHNW School of Computer Science, University of Applied Sciences and Arts Northwestern Switzerland. Her research focuses on Artificial Intelligence, Machine Learning, Deep Learning, and Explainable AI, particularly in medical applications such as Computer Vision, Signal Processing, and Digital Health. She has led projects including Smart Hospital and Hospital @ Home, receiving the Prix D'Excellence SanteNeXt 2023. Her publications emphasize AI-driven solutions in healthcare, spanning medical imaging, real-time clinical decision systems, and high-performance computing. Recent work explores ophthalmic imaging, multimodal ICU data platforms, and tensor-based data compression for visualization. Awards include the Prix D'Excellence SanteNeXt 2023 for innovative healthcare solutions.
Prof. Mathieu Luisier is a Full Professor of Computational Nanoelectronics at ETH Zurich's Department of Information Technology and Electrical Engineering. He earned his PhD in 2007 from ETH Zurich, followed by postdoctoral research there and a role as Research Assistant Professor at Purdue University (2008–2011). His research focuses on nanoscale device modeling, including nanowire transistors, memristors, and 2D semiconductors, with a strong emphasis on quantum transport and high-performance computing. ERC Starting Grant (2013) SNSF Advanced Grant (2022) ACM Gordon Bell Prize (2019) His work integrates advanced simulation techniques like GW approximations and parallel algorithms to address challenges in nanoelectronics. He teaches courses on digital circuits and integrated systems, and leads research groups exploring next-generation devices for applications in quantum computing and neuromorphic systems.
Jan S. Hesthaven is a Professor and Provost at EPFL, leading academic affairs. He holds a Master's from the Technical University of Denmark (DTU) and a PhD in Numerical Analysis, followed by an honorary dr.techn degree from DTU. His research focuses on high-order computational methods for wave problems, reduced order models, and machine learning integration. He has co-authored over 175 papers and 4 monographs. Previously, he served as Dean of the School of Basic Sciences at EPFL and held roles at Brown University, including Director of the Center for Computation and Visualization. Awards include the Alfred P. Sloan Fellowship and the Philip J. Bray Award. Education: Master of Science in Computational Physics, DTU (1991) PhD in Numerical Analysis, DTU (1995) dr.techn in Computational Mathematics, DTU (2009) Research Interests: Development of high-order numerical methods, computational wave propagation, geophysical flows, and machine learning applications in scientific computing. His work bridges traditional methods with AI-driven approaches for real-time modeling and structural health monitoring. Recent Work: His 2023–2025 publications emphasize machine learning-enhanced models, reduced order methods, and seismic data analysis for environmental applications. Key techniques include physics-informed neural networks and graph-based operator learning. Awards: Alfred P. Sloan Fellowship (2000) NSF Career Award (2002) Philip J. Bray Award (2004) Dr.techn from DTU (2009) Grants & Leadership: Led the Center for Computation and Visualization (CCV) at Brown (2006–2013) and co-directed the NSF Institute ICERM (2010–2013). Current roles include Provost at EPFL and leadership in MATHICSE. Collaborates with industry and applied scientists on computational challenges. Labs & Teams: Active in the MATHICSE lab, focusing on numerical methods and high-performance computing. Involved in interdisciplinary projects combining AI with traditional computational science.
Joaquim Loizu is a Senior Lecturer (MER) at the Swiss Plasma Center (SPC-TH) and the School of Physics and Chemistry (SPH-ENS) at École Polytechnique Fédérale de Lausanne (EPFL). His work bridges theoretical plasma physics with experimental validation , focusing on advanced magnetic confinement concepts for fusion energy. Education PhD in Plasma Physics (2013), EPFL Master in Physics, Imperial College London (2009) BSc in Physics, EPFL Research Interests include: Design and stability of stellarator fusion devices MHD equilibrium and formation of magnetic islands Chaotic magnetic field transport Non-neutral plasma simulations Plasma sheath dynamics and bootstrap current analysis Scientific Contributions span 15 recent publications (2023-2025) on topics like chaotic transport quantification, gyrotron electron gun simulations, and multi-region MHD equilibrium calculations. His work has significantly advanced stellarator optimization and tokamak divertor modeling. Awards European Physical Society Plasma Physics PhD Award (2009) IUPAP Young Scientist Prize in Plasma Physics (2020) Advising includes mentoring PhD students Erol Balkovic , Pierrick Giroud-Garampon , and Zeno Tecchiolli . He contributes to major fusion experiments including Wendelstein 7-X and TCV tokamak , while developing simulation tools like GBS and FENNECS for plasma turbulence and non-neutral plasma studies.
Luisa Pastore is a Lecturer and Scientific Project Manager at the École polytechnique fédérale de Lausanne (EPFL), affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC) and the Teaching and Research Unit of Architecture and Sustainability (SAR-ENS). She leads the 8-year transdisciplinary project SWICE on sustainable energy transition practices and collaborates with the Holcim Foundation for Sustainable Construction and Amber Lion Partners as an Impact and Sustainability Advisor. Her research focuses on sustainable architecture , bioclimatic design , building energy efficiency , and indoor environmental quality . She investigates daylight performance, vegetation integration for thermal comfort, hybrid solar technologies, and climate-responsive urban planning. Her work bridges simulation methodologies with real-world applications in social housing and smart city contexts. Her publications highlight trends in active building envelopes , photovoltaic glassblocks , and post-occupancy evaluations . She has supervised PhD students like Minu Agarwal and worked with interdisciplinary teams in EPFL’s LIPID lab and the Smart Living Lab . Her expertise spans transdisciplinary collaboration, energy certification frameworks, and sustainable construction practices.
David Mallasen Quintana is a Scientist and Lecturer at the Swiss Federal Institute of Technology Lausanne (EPFL), affiliated with both the School of Engineering (STI) and the Integrated Systems Laboratory (ESL) within the Institute of Electrical and Microengineering. He also holds teaching responsibilities in the School of Computer and Communication Sciences (IC) at EPFL. Dr. Quintana completed his Ph.D. in Computer Engineering at Universidad Complutense de Madrid in 2024. His research focuses on: Computer architecture and arithmetic systems RISC-V ecosystem development and customization Energy-efficient hardware design and domain-specific accelerators Posit arithmetic implementations and optimizations Embedded systems and low-power computing solutions His recent publications demonstrate consistent focus on RISC-V extensions, posit arithmetic implementations, and hardware/software co-design approaches for efficient computing. The work spans from fundamental arithmetic units to application-specific accelerators, with evolving emphasis on scientific computing applications and energy-constrained environments. Dr. Quintana actively contributes to open-source hardware projects including PERCIVAL, x-HEEP, and various arithmetic unit implementations. His research group develops energy-efficient platforms and tools for hardware deployment within the RISC-V ecosystem.
Dr. Benjamin Watts is a beamline scientist at the Paul Scherrer Institute's (PSI) Center for Photon Science, specializing in soft X-ray spectro-microscopy for organic materials analysis. He earned his BSc (Professional) in Physics with Honours and PhD in Physics from the University of Newcastle, Australia, followed by postdoctoral research at North Carolina State University and the Advanced Light Source in Berkeley. University of Newcastle, Australia - BSc (Professional) in Physics University of Newcastle, Australia - PhD in Physics His research focuses on advanced X-ray techniques like NEXAFS spectroscopy and STXM microscopy to study nanoscale chemical and magnetic properties in polymer electronics and photonic crystals. He has pioneered 7 nm resolution imaging and developed software tools for data analysis. Recent publications highlight his work in 3D imaging methodologies, contamination control, and data format standardization. He serves as Chair of the NeXus International Advisory Committee, improving data exchange protocols for neutron/X-ray experiments. Benjamin Watts coordinates external user groups at the PolLux beamline, implementing hardware/software upgrades while advancing scientific research through novel imaging capabilities and data infrastructure.
