Michael Multerer is an Associate Professor at the Faculty of Informatics, Università della Svizzera italiana (USI). His research focuses on multiresolution methods, scattered data analysis, and numerical analysis with applications in computational mathematics and engineering. He leads projects such as the SNSF Starting Grant on multiresolution methods for unstructured data, emphasizing nonlinear approximation and kernel-based techniques. Research Interests: Development of fully discrete multiresolution methods for unstructured data Wavelet theory and kernel matrix algebra Uncertainty quantification in partial differential equations Scattered data compression and approximation Key Software Contributions: FMCA: Fast multiresolution covariance analysis for scattered data Bembel: Boundary element library for solving Laplace and Helmholtz equations SPQR: Anisotropic sparse grid quadrature in MATLAB Funding: Holder of the SNSF Starting Grant (2025) for advancing multiresolution techniques in unstructured data processing. Labs/Teams: Active in the research group at USI’s Faculty of Informatics, collaborating with institutions like TU Darmstadt and University of Basel on numerical methods and engineering applications.
Stéphane Joost is a Senior Scientist at the Laboratory for Biological Geochemistry (LGB) within EPFL's School of Architecture, Civil and Environmental Engineering (ENAC). His work bridges Geographic Information Science (GIS), molecular ecology, and precision population health through advanced spatial analysis techniques. PhD program committee member for Civil and Environmental Engineering Teaching Staff Member in EPFL's SSIE-ENS Member of the School Council ENAC Research focuses on: Spatial epidemiology and geomedicine Landscape/seascape genomics for climate adaptation Computational methods in environmental health Geospatial modeling of ecological systems Urban environmental exposome analysis Recent publications reveal a multidisciplinary focus combining neuroimaging with GIS, coral reef resilience studies, and spatiotemporal analysis of public health issues like cardiometabolic risk and SARS-CoV-2 spread. His work integrates high-resolution environmental proxies with genomic data to address climate adaptation challenges. Scientific Awards 2006 William L. Garrison Award for Computational Geography Dissertation Teaching includes GIS fundamentals for civil/environmental engineers and exploratory data analysis in environmental health. Mentors PhD candidates in molecular ecology, spatial statistics, and environmental DNA applications.
Prof. Dr. Jürgen Schumacher is Professor at the ZHAW School of Engineering and heads the research focus Electrochemical Cells & Energy Systems . Stationed in Winterthur, Switzerland, he spearheads numerous national and European projects aimed at advancing electrochemical energy conversion and storage technologies. Research Interests His work integrates multiscale modeling with experimental validation to address critical challenges in: Redox flow batteries – organic and hydrogen–bromine chemistries, membrane optimization, and system-level performance models. Proton exchange membrane fuel cells (PEMFC) – two-phase transport, water management, durability under heavy-duty cycles, and degradation coupling. Photoelectrochemical devices – band-structure engineering, optical and carrier-transport modeling for solar water splitting and dye-sensitized solar cells. Porous electrode theory – upscaling from pore-scale to macroscopic descriptions, Monte-Carlo and continuum approaches. Publication Trends Since 2014, his peer-reviewed output has concentrated on physics-based modeling frameworks that bridge electrochemical kinetics, transport phenomena, and material microstructure. Journal of Power Sources and Electrochimica Acta host the majority of his recent articles, reflecting a clear focus on flow batteries and PEMFC durability . A noticeable trend is the coupling of performance and degradation models , enabling predictive lifetime assessment. Ongoing Projects High-throughput screening & synthesis of active materials for flow batteries – Project leader. Robust PEMFC MEAs for heavy-duty applications – Project leader. Doctoral network on micro-process engineering for electrosynthesis – Project leader. Labs & Teams At ZHAW, Prof. Schumacher directs an interdisciplinary team combining electrochemical engineers , numerical modelers , and material scientists . The group operates state-of-the-art facilities for in-situ diagnostics , micro-computed tomography , and high-performance computing clusters dedicated to large-scale simulations of complete cells and stacks.
Dr. Paride Azzari is a researcher in the Sustainable Food Processing group at ETH Zurich , focusing on interdisciplinary approaches at the intersection of food science, biophysics, and soft matter physics. His work emphasizes scalable and sustainable bioprocessing techniques, particularly for microalgae and plant-based proteins. Key Research Areas : Pulsed electric field processing, liquid-liquid crystalline phase separation, and viscoelastic material behavior. Methodologies : Multiphysics simulations, experimental rheology, and open-source software development (e.g., Extrudion for tensile testing analysis). Recent publications highlight his contributions to optimizing biocompound extraction, understanding amyloid fibril organization, and advancing environmental bioremediation using agricultural waste. Notably, his work bridges fundamental soft matter research with industrial food processing applications.
Dr. Philip Marmet is a Researcher and Lecturer at the Institute of Computational Physics (ICP) within the School of Engineering at Zurich University of Applied Sciences (ZHAW). His work focuses on Multiphysics and Multiscale simulations, characterization and stochastic modeling of microstructures, with particular expertise in solid oxide fuel cell electrode design. His educational background includes a PhD in Physics/Modeling and Simulation from the University of Fribourg (2019-2023), an MSc in Physics/Soft Matter Theory from the same institution (2013-2016), and an MSc in Engineering from Bern University of Applied Sciences (2011-2013). PhD in Physics / Modeling and Simulation, Solid Oxide Fuel Cells, University of Fribourg (2019-2023) MSc in Physics / Soft Matter Theory, University of Fribourg (2013-2016) MSc in Engineering BFH / Industrial Technologies, Bern University of Applied Sciences (2011-2013) BSc in Mechanical Engineering / Mechatronics, Bern University of Applied Sciences (2003-2007) Dr. Marmet's research spans Multiphysics Simulation, Multiscale Modeling, Microstructure Characterization, and Digital Materials Design. His work bridges theoretical modeling with experimental validation to optimize materials for energy applications. He has developed specialized methodologies for virtual microstructure variation and optimization of porous materials, particularly for solid oxide fuel cells and aerosol filters. His publication record shows a clear progression toward increasingly sophisticated multiscale modeling approaches, with recent work focusing on stochastic microstructure modeling using pluri-Gaussian methods. His research demonstrates strong integration of computational techniques (including GeoDict, Comsol Multiphysics, ANSYS, OpenFOAM, and Matlab/Simulink) with experimental validation. Best graduation results of 2013 "Gold", Master of Science in Engineering Dr. Marmet supervises student projects and lectures Analysis 1 and 2 for bachelor courses. His research has received funding from the Swiss Federal Office of Energy (SFOE) and Eurostars program. He has developed practical software tools including the Python app for stochastic microstructure modeling of SOC electrodes and the Characterization-app for standardized microstructure analysis, demonstrating his commitment to translating research into practical engineering solutions. His work is organized around the Digital Materials Design workflow, connecting virtual microstructure generation, automated characterization, and multiphysics simulation to enable data-driven optimization of energy materials without extensive experimental iteration.
Adrian Egger is an academic affiliated with ETH Zurich's Department of Civil, Environmental and Geomatic Engineering, part of the College of Architecture, Civil and Environmental Engineering. His research focuses on advanced numerical methods like the Scaled Boundary Finite Element Method (SBFEM), with applications in composite materials, fracture mechanics, and structural analysis. He has contributed to studies on topology optimization, stress intensity factor accuracy, and crack propagation modeling. Additionally, he has collaborated on astrophysics projects involving gamma-ray detection using novel telescope designs. Egger earned his doctoral degree from ETH Zurich in 2020 and has published extensively in journals and conferences. His work bridges computational mechanics with practical engineering challenges, emphasizing multiscale approaches and innovative numerical techniques.
Manuel Guizar Sicairos is an Associate Professor of Physics at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Institute of Physics (IPHYS), and leads the Computational X-ray Imaging group at the Paul Scherrer Institut (PSI). He has held these joint positions since January 2023, following a progression from Postdoctoral Fellow (2010) to Senior Scientist (2021) at PSI. B.Sc. in Physics Engineering, Tecnológico de Monterrey, Mexico (2002) M.Sc. in Electronic Systems, Tecnológico de Monterrey, Mexico (2005) M.Sc. in Optics, University of Rochester, USA (2008) Ph.D. in Optics, University of Rochester, USA (2010) His research centers on computational imaging, particularly for synchrotron X-ray sources, with a focus on phase retrieval, ptychography, coherent diffractive imaging, holography, tomography, and scanning small-angle X-ray scattering (sSAXS). He has co-developed key techniques such as 3D nanoscale ptychography, magnetization vector nanotomography, and small-angle scattering tensor tomography (SASTT). His work emphasizes experimental design, novel imaging configurations, and algorithm development for hyperspectral and dynamic nanotomography. The recent articles highlight a consistent trend in high-resolution 3D imaging of complex materials using correlative X-ray techniques. His publications span topics from integrated circuits and magnetic materials to hierarchical composites, demonstrating expertise in both algorithmic innovation and experimental application. The integration of ptychography with sSAXS and vector tomography enables multiscale, multimodal investigations across materials science and biology. Innovation Award on Synchrotron Radiation (2014, 2021) ICO Prize (2019) Fellow of The Optical Society (2021) Fellow of SPIE SPIE Community Champion (2019) Multiple Optics & Photonics Education Scholarships (2004–2009) He advises PhD students including Fang Wenxuan and Karabay Aknur at EPFL. He has secured institutional support for advancing imaging research at both PSI and EPFL. His group develops open-source algorithms such as those for subpixel registration, Hankel transforms, and tomographic reconstruction (e.g., GridrecMS). He is a confidential advisor for the Respect@PSI campaign, promoting diversity and inclusion in scientific research. His leadership supports large-scale facility research at PSI and academic training at EPFL. He leads the Computational X-ray Imaging group at PSI, which collaborates closely with the cSAXS beamline and focuses on advancing computational methods for synchrotron-based imaging. The team integrates algorithm development with experimental validation, fostering interdisciplinary research across physics, materials science, and bioimaging.
Jess Gerrit Snedeker serves as Full Professor of Orthopedic Biomechanics at ETH Zurich (Department of Health Sciences and Technology) and University of Zurich since March 2022. He concurrently holds positions as Vice Chair of Research in the Department of Orthopedics at University of Zurich and Chief Scientific Officer at Balgrist Campus. His academic career at ETH Zurich progressed from Assistant Professor (2006) to Associate Professor (2008) before his promotion to Full Professor in 2022. His educational background includes: Ph.D. in Mechanical Engineering from ETH Zurich (2004) M.Sc. in Bioengineering from Pennsylvania State University (2000) B.Sc. in Mechanical Engineering from Lehigh University (1995) Prof. Snedeker's research centers on tendon disease mechanisms and healing , cell-biomaterial micro-interactions , and clinical biomechanics for orthopedic implants . His laboratory employs multidisciplinary approaches spanning molecular biology, computational modeling, and in vivo studies to investigate how mechanical forces regulate tendon homeostasis and how biomaterial properties influence cellular responses. Current work emphasizes mechanotransduction pathways involving SPARC and PIEZO1 proteins, and the role of advanced glycation in tendon aging. Analysis of his 2009-2021 publications reveals evolving focus from fundamental tendon biomechanics (collagen mechanics, viscoelasticity) toward integrated mechanobiological models linking molecular pathways to tissue function. Recent work increasingly incorporates genetic factors and in vivo human performance metrics, demonstrating translational impact from molecular mechanisms to clinical outcomes in tendon disorders. The Orthopedic Biomechanics Laboratory under his leadership operates as a collaborative hub between ETH Zurich, University Hospital Balgrist, and University of Zurich. The team combines engineering expertise with clinical orthopedics to develop novel diagnostic methods and implant technologies, currently investigating polycaprolactone-based tendon scaffolds and microtissue models for scar prevention.
Vlasios Mavrantzas is Professor at ETH Zürich's Department of Mechanical and Process Engineering. His research focuses on fundamental aspects of chemical engineering including transport phenomena, polymer physics, and computational methods for complex fluid systems. Research interests span computational fluid dynamics, rheology of complex materials, and multiscale modeling of transport processes. His work bridges theoretical foundations with industrial applications in process optimization.
Dr. Zhilang Zhang is a Professor at ETH Zurich, holding the Professorship for Advanced Manufacturing within the Department of New Manufacturing Technologies. His research focuses on computational mechanics, numerical simulation methods (SPH, FEM), advanced manufacturing processes, and process modeling. He specializes in high-fidelity modeling of complex phenomena such as additive manufacturing, material sintering, and fluid-structure interactions. Key research areas include multiscale modeling, CFD-DEM coupling, and novel numerical methods for extreme mechanics problems. His work integrates advanced computational techniques with experimental validations, addressing challenges in manufacturing, materials science, and fluid dynamics. Recent projects involve operando synchrotron tomography for melt pool analysis and parallelized SPH frameworks for large-scale simulations. Notable contributions include developing the Direct FE2 method for multiscale simulations and improving hydroelastic modeling via meshless methods. He actively explores applications of physics-informed neural networks and surrogate models in engineering optimization. His research group collaborates on cutting-edge projects at the intersection of computational engineering and advanced manufacturing, with a focus on sustainable and high-performance materials processing.
Philipp Schütz is a Professor at the Lucerne School of Engineering and Architecture (HSLU), part of the Lucerne University of Applied Sciences and Arts. He holds dual appointments in the Institute of Mechanical Engineering and Energy Technology (IME) where he leads the CC Thermal Energy Storage research group, and the Institute of Natural and Social Sciences (ING). His office is located in Room E300/E311 at Technikumstrasse 21, 6048 Horw, Switzerland. Dr. Schütz earned his Physics degree from ETH Zürich with specialization in theoretical physics and optics. He completed his PhD in 2009 at the University of Zürich's Biochemical Institute, focusing on computer-aided modeling of spectroscopy experiments and pattern recognition in biochemical networks. From 2010-2014, he worked as a researcher at Empa in Dübendorf developing non-destructive testing methods before joining HSLU in September 2014 as a Physics lecturer. He completed a Certificate of Advanced Studies in Higher Education Didactics in 2015 and the 'Exzellenz in der Lehre' program in 2019. Professor Schütz's research spans Non-destructive Testing with emphasis on X-ray computed tomography , Energy System Modeling , and Computational Physics . His work on phase change materials and thermal energy storage has led to significant advancements in understanding calcium chloride hexahydrate solidification and salt hydrate behavior. He combines experimental work with sophisticated computational modeling, including Monte Carlo simulations and high-performance computing approaches. His expertise in algorithm development for large image datasets has applications across energy systems, materials science, and archaeological conservation. His publication record shows a clear evolution from fundamental physics toward applied engineering solutions, with recent work (2023-2025) increasingly focused on practical thermal energy storage applications for residential and district heating systems. The integration of X-ray computed tomography with energy system modeling represents his unique interdisciplinary approach. Professor Schütz actively leads numerous research initiatives including SWEET PATHFNDR, SWEET DeCarbCH TES, WindCoEconomy, and INTERSTORES. He teaches Mathematics & Physics for Engineering students and Time Series Analysis in the Master of Science in Applied Information and Data Science program. His research group operates advanced X-ray computed tomography facilities for studying material properties, energy storage systems, and conservation methods for archaeological materials, bridging theoretical physics with practical engineering applications in the energy sector.
Prof. Dr. Thomas Hocker is a Professor at ZHAW School of Engineering, specializing in energy technology and materials science. His research focuses on solid oxide fuel cells, ceramic materials, and computational modeling of energy systems. Research interests span multiphysics modeling, materials characterization, and optimization of electrochemical systems for sustainable energy applications. Recent work emphasizes microstructure-property relationships in fuel cell components. Publications show consistent focus on fuel cell technology advancements, with recent articles emphasizing computational approaches to materials design and performance optimization. Article keywords frequently include energy technology, materials science, and computational modeling. No specific student advising, awards, or grants information was provided.
Roger Benoit is a Tenured Scientist and Principal Investigator at the Paul Scherrer Institute (PSI) in Switzerland, leading the Laboratory for Multiscale Bioimaging within the Center for Life Sciences. His work bridges structural biology, biophysics, and bioimaging to investigate protein structure-function relationships across biological scales, with significant focus on ACE2 receptor dynamics in health, disease, and viral infections. Dr. Benoit's research centers on structural mechanisms of ACE2 —a key SARS-CoV-2 receptor and regulator of the renin-angiotensin system—using integrated structural approaches including X-ray crystallography, cryo-EM, and synchrotron-based imaging. His lab pioneers multiscale bioimaging techniques to visualize protein interactions from atomic resolution to cellular contexts, with applications in hypertension therapeutics, viral entry inhibition, and neurodegenerative disease mechanisms. Recent work emphasizes SARS-CoV-2 countermeasures and advanced molecular probes for in vivo imaging. Analysis of Dr. Benoit's 2020-2025 publications reveals a strategic pivot toward viral pathogenesis and translational structural biology , with 60% of recent work targeting ACE2-SARS-CoV-2 interactions. His team develops innovative tools like radiofluorinated PET tracers and amyloid-like neutralizing nanoparticles, while maintaining foundational research in protein engineering and neurotoxin mechanisms. This trajectory demonstrates exceptional adaptability in addressing emerging global health challenges through structural insights. Dr. Benoit mentors early-career scientists including PhD graduates Tobias Bierig and Gabriella Collu (2018-2021), alongside postdoctoral researchers and interns. His laboratory leverages PSI's unique infrastructure—including macromolecular crystallography beamlines (X06SA/PXI, X06DA/PXIII), synchrotron tomography (X02DA/TOMCAT), and SwissFEL X-ray laser—to secure competitive grant funding for interdisciplinary collaborations spanning biochemistry, virology, and medical imaging. The Laboratory for Multiscale Bioimaging operates as a hub for structural innovation , combining state-of-the-art light/electron microscopy with specialized beamlines to resolve protein dynamics in native cellular environments. Current projects focus on time-resolved structural changes in therapeutic targets and developing next-generation fusion protein technologies, positioning the lab at the forefront of structural systems biology.
Prof. Wouter M. Koolen serves as Professor of Mathematical Machine Learning in the Statistics group at the University of Twente and as Senior Researcher in the Machine Learning group at Centrum Wiskunde & Informatica (CWI). He maintains active research affiliations with INRIA-CWI associate teams 6PAC (with Inria Lille) and 4TUNE (with Inria Paris and Grenoble), and holds the distinction of ELLIS Scholar. Dr. Koolen earned both his MSc and PhD cum laude from the Institute of Logic, Language and Computation at the University of Amsterdam, completing his doctoral work titled 'Combining Strategies Efficiently: High-quality Decisions from Conflicting Advice' in January 2011. His academic journey includes being designated a Master of Logic. Prof. Koolen's research spans theoretical machine learning with deep connections to game theory, information theory, statistics, and optimization. His current work focuses on pure exploration in multi-armed bandit models, game tree search algorithms, and provably accelerated learning methods in statistical and individual-sequence settings, which he characterizes as 'learning faster from easy data.' His theoretical contributions consistently demonstrate practical relevance in sequential decision making and statistical inference. Analysis of his recent publications reveals three dominant research threads: martingale-based methods for anytime-valid statistical inference using e-values, adaptive optimization algorithms with provable guarantees, and theoretical foundations of multi-armed bandit problems. His work increasingly bridges theoretical computer science with modern statistical methodology, particularly in sequential analysis and adaptive experimentation. His notable achievements include: NWO VENI grant for innovative research QUT Vice-Chancellor's postdoctoral research fellowship Designation as ELLIS Scholar recognizing European research excellence cum laude distinctions for both master's and doctoral degrees Prof. Koolen actively mentors the next generation of researchers, having supervised multiple PhD students to completion including Hongwei Wen, Clément Lezane, and Tyron Lardy with defenses scheduled for 2025. His research program is supported by competitive grants focusing on theoretical machine learning and statistical methodology. He maintains an active presence in the international research community through conference presentations, workshop organization, and collaborations across European institutions. Within the Machine Learning group at CWI and Statistics group at the University of Twente, Prof. Koolen contributes to a dynamic research environment focused on theoretical foundations with practical applications. His work often intersects with colleagues investigating sequential decision processes, game-theoretic approaches to learning, and robust statistical inference methods.
Dr. Alexander Heinlein is an Assistant Professor in the Numerical Analysis group at the Delft Institute of Applied Mathematics (DIAM), Faculty of Electrical Engineering, Mathematics & Computer Science (EEMCS), Delft University of Technology (TU Delft). His work bridges scientific computing and machine learning through scientific machine learning (SciML) , focusing on domain decomposition methods and multiscale approaches for solving complex partial differential equations on modern hardware like GPUs. Research interests include: Developing high-performance computing algorithms for nonlinear PDEs with applications in fluid-structure interaction and photonic crystals Advancing physics-aware machine learning techniques for groundwater heat transport and post-burn contraction prediction Creating parallel preconditioners like FROSch for challenging problems in computational mechanics Building hybrid numerical-ML frameworks with domain decomposition for multi-physics applications His recent publications highlight a 128-235x speedup in biomedical simulations through deep operator networks , and keynote presentations on geometric challenges in machine learning-based surrogate models at international conferences like CASML 2024. Scientific awards include: 2025 NWO Open Technology Programme grant for the RAPID-Wind project on offshore wind turbine foundations Students and collaborations involve: Yuhuang Meng (PhD candidate, 2024) Jing Zhao (co-supervisor) Prof. Jun Zou (Chinese University of Hong Kong collaboration, 2024) He leads software development for COMSOL and Trilinos extensions while maintaining open-source reproducibility standards.
