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Stefano BuosoView profile

Lecturer

• Biomedical Engineering
• Cardiovascular Modeling
• Computational Fluid Dynamics
• +4 more

Dr. Stefano Buoso is a Lecturer at ETH Zürich's Department of Information Technology and Electrical Engineering, specializing in biomedical imaging and computational mechanics. His research focuses on cardiovascular modeling, fluid-structure interaction, and reduced-order modeling. He holds a position in the Professur für Biomedizinische Bildgebung (Biomedical Imaging Professorship). Key research areas include computational fluid dynamics applied to cardiovascular systems, development of machine learning tools for medical imaging (e.g., MRI and echocardiography), and creation of patient-specific numerical phantoms. His work bridges engineering and medicine, with applications in clinical diagnostics and personalized medicine. Recent articles emphasize innovations like automated 3D heart modeling from 2D echocardiography, synthetic MRI data generation, and turbulence analysis in aortic flow. He has developed open-source tools like CMRsim for cardiovascular simulations. Buoso collaborates on digital twin technologies for carotid artery disease assessment and fluid dynamics modeling of biological systems. His technical contributions include methods for rapid mesh generation, stabilized reduced-order models, and MRI-guided cardiac shape modeling. Research spans from microvascular neurovascular unit studies to bio-inspired membrane wing aerodynamics, reflecting interdisciplinary expertise.

Efthymios N. KaratzasView profile

Assistant Professor

• Numerical Analysis
• Computational Mathematics
• Optimal Control
• +5 more

Efthymios N. Karatzas serves as an Assistant Professor in the Department of Mathematics at Aristotle University of Thessaloniki, Faculty of Sciences, within the Computer Science and Numerical Analysis Section. He maintains an active research profile in computational mathematics with strong institutional affiliations including collaborations with SISSA mathLab and FORTH Institute of Applied and Computational Mathematics. His academic credentials include: PhD in Mathematics, National Technical University of Athens (2015) Master's in Applied Mathematical Sciences – Computational Mathematics, NTUA (2009) Master's in Applied Mathematics, University of Patras (2001) Bachelor's in Mathematics (Computational Mathematics), University of Patras (1999) Dr. Karatzas' research program centers on advanced numerical techniques for partial differential equations , with pioneering work in reduced order modeling , embedded boundary methods , and optimal control systems . His expertise spans computational fluid dynamics, uncertainty quantification, and biomechanical applications, characterized by methodological innovation in handling geometrically complex domains through cut finite element approaches and shifted boundary formulations. Analysis of his 15 most recent publications reveals a cohesive research trajectory focused on developing efficient numerical frameworks for parametrized PDE systems. His work consistently bridges theoretical rigor with practical implementation, particularly in advancing reduced basis methods for fluid-structure interaction and biological modeling, demonstrating significant contributions to computational mathematics through high-impact journal publications. No major scientific awards are documented in the available sources. Dr. Karatzas demonstrates research leadership through project management roles including Scientific Manager for the ELIDEK project at NTUA (2019-2021) and Project Manager for the European Social Fund HEaD initiative at SISSA (2017-2019). His grant administration experience encompasses coordinating interdisciplinary teams and securing external funding for computational mathematics research. He maintains active collaborations with the SISSA mathLab in Trieste (particularly with Prof. Gianluigi Rozza's group) and the FORTH Institute in Crete, participating in international workshops including the Reduced Order Methods in CFD Summer School (2019) and SIAM UQ conferences. His research network spans computational mathematics groups across Europe with emphasis on advancing numerical methodologies for real-world engineering and biological applications.

Camilla FioriniView profile

Associate Professor

• Computational Fluid Dynamics
• Numerical Analysis
• Partial Differential Equations
• +4 more

Camilla Fiorini is an Associate Professor at the National Conservatory of Arts and Crafts (CNAM) in Paris, where she conducts research at the Mathematical and Numerical Modeling Laboratory (M2N). She serves as Principal Investigator for the ANR-funded SPARCL project (2025-2029) focusing on structure-preserving reduced order models for conservation laws. Her academic background includes a PhD in Applied Mathematics from the University of Versailles and both MSc/BSc degrees in Mathematical Engineering from Politecnico di Milano. Her research centers on computational fluid dynamics, numerical analysis of PDEs, and sensitivity methods, with specific applications in uncertainty quantification and reduced order modeling. Current projects develop novel approaches for conservation laws that maintain structural properties while improving computational efficiency and reliability. Fiorini's publication record demonstrates consistent focus on sensitivity analysis techniques for complex fluid systems, shock-capturing methods, and uncertainty propagation in hyperbolic PDEs. She received the SMAI-GAMNI PhD Award 2019 (French ECCOMAS Award) for her doctoral dissertation on sensitivity analysis for nonlinear hyperbolic systems. As Principal Investigator of the SPARCL project, she leads a team developing new reduced basis construction techniques for conservation laws. Fiorini actively advises graduate researchers including PhD students Nathalie Nouaime (2021-2024) and Nicolas Lepage (2022-present), plus multiple Master's candidates. Her research group collaborates with institutions including Inria, Sorbonne University, ONERA, and CEA. Current projects include ANR JCJC-funded SPARCL and ANR AHEAD initiatives. She leads the SPARCL research group at M2N laboratory, collaborating with researchers including Alessia Del Grosso, Iraj Mortazavi, and Taraneh Sayadi on reduced order modeling techniques. The team focuses on developing computationally efficient ROMs that preserve physical structures in conservation laws.

Ngoc Cuong NguyenView profile

Researcher

• Computational Mechanics
• Molecular Mechanics
• Nanophotonics
• +7 more

Ngoc Cuong Nguyen is a Principal Research Scientist in the Department of Aeronautics and Astronautics at MIT and a member of the MIT Center for Computational Engineering. His research focuses on computational mechanics, numerical simulation, and advanced numerical methods such as hybridizable discontinuous Galerkin (HDG) methods for multi-scale and multi-physics problems. Education: PhD in High Performance Computation for Engineered Systems (2005), National University of Singapore BEng in Aeronautical Engineering (2001), Ho Chi Minh City University of Technology Research Interests: Computational Mechanics, Molecular Mechanics, Nanophotonics Numerical Simulation & Optimization, Scientific Computing, Machine Learning Reduced Basis Methods, High-Order Methods (e.g., HDG), Uncertainty Quantification Key Projects: Development of HDG methods for fluid dynamics, structural mechanics, and electromagnetics Plasmonic nanostructure simulations using quantum hydrodynamic models Space weather modeling via GPU-accelerated HDG approaches Optimization of photonic crystals and nanostructured materials Large-eddy simulation (LES) of hypersonic flows and buffet phenomena Labs & Teams: Active contributor to the MIT Center for Computational Engineering, leading projects in numerical methods, computational fluid dynamics, and interdisciplinary applications of advanced simulation techniques.

Anthony T. PateraView profile

Professor

• Numerical methods
• Partial differential equations
• Variational techniques
• +5 more

Anthony T. Patera is a Professor of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), affiliated with the School of Engineering. His research focuses on numerical methods, partial differential equations, and computational techniques such as finite element methods and reduced basis methods. He holds a prominent role in advancing real-time computing and parameter estimation methodologies. He is based at MIT’s Department of Mechanical Engineering in Room 3-266. His work bridges theoretical developments in computational science with practical engineering applications, emphasizing variational techniques and error estimation frameworks. His contributions have significantly impacted fields requiring efficient computational models for complex systems. While no specific awards or grants are listed in the provided text, his extensive involvement in computational modeling and simulation research underscores his scholarly contributions. No advisee students are explicitly mentioned here.

Omer SanView profile

Associate Professor

• Scientific Machine Learning
• Digital Twins
• Artificial Intelligence
• +3 more

Omer San is an Associate Professor in the Department of Mechanical, Aerospace and Biomedical Engineering at the University of Tennessee, Knoxville (2023–present). Previously, he served as Associate Professor (2021–2023) and Assistant Professor (2015–2021) at Oklahoma State University. His research focuses on scientific machine learning, digital twins, and fluid dynamics, emphasizing hybrid analysis and modeling approaches to address challenges in turbulence simulation and data assimilation. PhD in Engineering Mechanics, Virginia Tech (2012) MS in Aerospace Engineering, Old Dominion University (2007) BS in Aeronautical Engineering, Istanbul Technical University (2005) Research interests include physics-guided machine learning, data-driven reduced order modeling, and large eddy simulation closure techniques. His work bridges first-principles models and data-driven methods to enhance predictive accuracy in complex systems such as geophysical flows and turbulence. He leads the Digital Twin Lab, advancing hybrid modeling frameworks for digital twin technologies. Notable awards include the DOE ASCR Early Career Award (2018) and OSU’s Distinguished Early Career Faculty Award (2022). His research is funded by AFOSR, DOE, NSF, NASA, and industry partnerships.

Benjamin StammView profile

Professor

• Numerical Analysis
• Scientific Computing
• High-Performance Computing (HPC)
• +7 more

Prof. Benjamin Stamm is a Professor of Numerical Mathematics at the University of Stuttgart, leading the Chair of Numerical Mathematics for High Performance Computing within Faculty 08. He holds a Ph.D. and master's degree in mathematics from École Polytechnique Fédérale de Lausanne (EPFL) and has previously worked at RWTH Aachen University, Sorbonne Université UPMC Paris 6, UC Berkeley, and Brown University. His research focuses on numerical analysis, scientific computing, and simulations, particularly efficient discretizations for PDEs, eigenvalue problems, error certification, reduced basis methods, and HPC implementations. He develops scalable numerical methods for problems in computational chemistry and physics, emphasizing accuracy, efficiency, and interdisciplinary collaboration with chemists, physicists, and materials scientists. Prof. Stamm’s work includes contributions to domain decomposition methods, polarization energy calculations, and software development like the ddX library. His publications span topics such as model order reduction, quantum simulations, and molecular dynamics. Collaborations and software tools underscore his commitment to bridging computational methods with real-world scientific challenges.

Luke OlsonView profile

Professor

• Numerical Analysis
• High-Performance Computing
• Scientific Computing
• +4 more

Luke Olson is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), part of the College of Engineering. He holds an affiliate appointment in the Department of Mechanical Science and Engineering. His research focuses on numerical methods, high-performance computing, and parallel algorithms, particularly algebraic multigrid (AMG) solvers and sparse matrix computations. He leads the development of open-source libraries like PyAMG and RAPtor, advancing computational tools for scientific and engineering applications. Education: Ph.D. in Applied Mathematics from the University of Colorado Boulder (2003), M.S. in Mathematics from the University of Iowa (1999), and B.A. in Mathematics and Physics from Luther College (1997). Research Interests: Algebraic multigrid methods and preconditioners High-performance computing and parallel algorithms Numerical solutions to partial differential equations Scientific computing and software development Machine learning integration in numerical simulations Recent Articles Trends: Recent work merges machine learning with traditional numerical methods, such as neural network closures for turbulent combustion and reduced basis approximations using neural networks. Ongoing contributions include optimizing multigrid methods for exascale architectures and enhancing communication efficiency in distributed systems. Awards: Recognized with the NSF CAREER Award (2007), UIUC Campus Award for Excellence in Teaching (2024), and the Donald Biggar Willett Faculty Scholar distinction (2016). Active in conference organization, including the Copper Mountain Conference on Multigrid Methods. Teaching & Grants: Teaches courses like Numerical Methods for PDEs and Scientific Machine Learning. Leads projects funded by NSF and industry collaborations, emphasizing education innovation through the AE3 fellowship (2014–2016). Labs/Teams: Directs the Scientific Computing Group at UIUC and contributes to interdisciplinary initiatives like the Center for Exascale-enabled Scramjet Design (CEESD).

Tom ClarkeView profile

Professor

• Enzyme characterization
• Metal cofactors
• Bacterial respiration
• +7 more

Tom Clarke is a Professor of Protein Biochemistry at the University of East Anglia, where he serves as a member of the School of Biological Sciences, Centre for Molecular and Structural Biochemistry, Energy Materials Laboratory, and Molecular Microbiology. He currently holds the position of Biological Sciences Postgraduate Research Co-Director and Natural Sciences Exam Board Chair. Professor of Protein Biochemistry (2021-present) Lecturer (2011-2021) RCUK Research Fellow (2006-2011) Senior Research Associate (2003-2006) Research Fellow, University of Michigan (2001-2003) Professor Clarke's research focuses on the characterization of metal cofactor-containing enzymes involved in bacterial respiration, including cytoplasmic, periplasmic, membrane, and extracellular proteins. His work combines biological, biochemical, and biophysical techniques to explore protein-protein interactions, electron transfer mechanisms, and enzyme catalysis. Current research projects include extracellular electron transfer, interactions between outer membrane cytochromes and mineral surfaces, and reverse electron transfer (bioelectrosynthesis). His recent publications demonstrate a continued focus on bacterial electron transport mechanisms, with particular emphasis on multiheme cytochromes, extracellular catalysis, and the structural basis of electron transfer across bacterial cell envelopes. His research group employs diverse techniques from cyclic voltammetry to microbial culturing, sharing laboratory space with colleagues studying related biochemical processes. The Biochemical Society (Ambassador since 2022) Centre of Electromicrobiology, Aarhus (Board Member since 2018) Scientific Reports (Associate Editor since 2017) Professor Clarke actively supervises PhD students and postdoctoral researchers, with current projects focusing on environmental processing, metal recovery, mitochondrial uncoupling proteins, and bacterial bioelectronics. His teaching portfolio includes Microbial Biotechnology, Molecular Enzymology, Biochemistry, Microbiology, and Physical and Analytical Methods in Biological Sciences.

Jenny GuView profile

Lecturer

• Mindfulness
• Clinical Psychology
• Mental Health Interventions
• +3 more

Dr. Jenny Gu is an Associate Tutor in the School of Psychology at the University of Sussex, with a secondary role as a Support Worker in Student Advice and Guidance. She holds a PhD from the University of Sussex (2018) focusing on mindfulness and compassion interventions. Her research interests span mindfulness-based cognitive therapy (MBCT), compassion measurement, healthcare worker well-being, psychometric scale development, and clinical trial methodologies. She has contributed to studies on youth mental health interventions, healthcare staff stress reduction, and the neuroscientific basis of brain structure variation. Notable projects include the EYE-2 trial on engagement strategies for psychosis patients and evaluations of mindfulness-based programs for stress management. Education: PhD in Psychology (2018): University of Sussex, Thesis: 'Mindfulness and compassion: measurement and mechanisms of interventions' Research Highlights: Her work emphasizes translating mindfulness and compassion research into practical healthcare applications. Key areas include validating compassion scales (e.g., Sussex-Oxford Compassion Scales), analyzing mindfulness questionnaire efficacy, and designing clinical trials for youth mental health. She also explores interventional strategies to improve engagement in first-episode psychosis care and reduce workplace stress among healthcare professionals. Grants & Collaboration: Her collaborative projects involve institutions like King's College London and the University of Oxford, focusing on randomized controlled trials and systematic reviews. Funding sources include mental health research grants and university partnerships. Labs/Teams: Active within the University of Sussex's clinical psychology research group, contributing to interdisciplinary mental health initiatives.

Gustavo ScuseriaView profile

Professor

• Computational Quantum Chemistry
• Electronic Structure Theory
• Strong Correlation
• +8 more

Gustavo Scuseria is the Robert A. Welch Professor of Chemistry, Professor of Physics and Astronomy, and Professor of Materials Science and NanoEngineering at Rice University . He is a leading figure in computational quantum chemistry , with seminal contributions to electronic structure theory , coupled cluster methods , and density functional theory (DFT) functionals like HSE and PBE0. His research spans strong correlation , symmetry-projection techniques , and quantum computing applications . Education: PhD in Physics (1983) from University of Buenos Aires Research: Pioneered linear scaling quantum methods , developed HSE functional for semiconductor band gaps, and advanced symmetry-projected wave function approaches Awards: Feynman Prize in Nanotechnology, Humboldt Research Award, Guggenheim Fellowship, and multiple Fellowships from ACS, APS, and RSC Software Contributions: Key developer of Gaussian suite and TURBOMOLE implementations His recent publications focus on symmetry-projected methods for spin systems, dualities in electron correlation , and quantum computing applications . Collaborations with institutions like Los Alamos National Laboratory and Max-Planck Institute have shaped his interdisciplinary approach. Scuseria's work remains foundational for quantum chemistry software and materials science research.

Silke GlasView profile

Research Fellow

• Reduced basis method
• Variational inequalities
• Wave equations
• +3 more

Silke Glas is a Postdoctoral Researcher at the Institute of Numerical Mathematics, Ulm University, a position she has held since July 2018. Previously, she served as a Research Assistant at the same institute from April 2016 to July 2018 and at the Chair of Energy Trading and Finance, University of Duisburg-Essen (2012-2016), funded by the German Research Foundation's Priority Programme 1324. Her research visits include the Institut Henri-Poincaré in Paris and SISSA in Trieste. Her educational background features: Diploma in Mathematics and Economics from Ulm University (2006-2012), thesis on "Reduced Basis Method for Variational Inequalities" Master of Mathematics from the University of South Florida (2009-2010) Dr. Glas specializes in model reduction for nonlinear problems, with core expertise in reduced basis methods applied to variational inequalities, wave equations, Hamilton-Jacobi-Bellman equations, and space-time formulations. Her work bridges theoretical numerical analysis with practical applications in energy markets, particularly intraday electricity trading. She has developed novel approaches for noncoercive and parabolic systems, addressing challenges in error estimation and computational efficiency. Her publication trajectory reveals evolving sophistication in handling time-dependent nonlinear systems, with increasing emphasis on financial applications. Early work focused on theoretical foundations of variational inequalities, while recent publications integrate model reduction with optimal control for energy trading problems, demonstrating cross-disciplinary impact. Scientific recognition includes: No formal awards documented in source material Research funding has been secured through the German Research Foundation's Priority Programme 1324. No student advising activities are mentioned, though her collaborative work involves prominent researchers like K. Urban and Anthony T. Patera. Her primary research environment at Ulm University's Institute of Numerical Mathematics supports her focus on computational mathematics and real-world applications.

Gary GoldsteinView profile

Professor

• Theoretical High Energy Physics
• Nuclear Physics
• Quantum Chromodynamics
• +5 more

Gary Goldstein is a Professor of Physics & Astronomy at Tufts University's School of Arts and Sciences. His research focuses on theoretical high-energy and nuclear physics, including quantum chromodynamics (QCD), spin dynamics, and the Standard Model. He also explores science policy, nuclear non-proliferation, and science education. Goldstein has held faculty roles at Tufts since 1970, progressing from Assistant to Associate and full Professor. Education: PhD (1969), SM (1964), and SB (1962) in Physics from the University of Chicago. Research interests span particle interactions at medium/high energies, gluon spin distributions, and applications of quantum computing in physics. He has published extensively on topics like generalized parton distributions (GPDs), light-front quantization, and experimental analyses using colliders like the LHC and JLab. Goldstein has secured grants from the U.S. Department of Energy and NSF for quantum simulation and science education initiatives. Teaching includes advanced courses in quantum theory, electromagnetism, and thesis supervision. He actively participates in science policy events, advocating for nuclear disarmament and peace through initiatives like MIT's 'Reducing the Threat of Nuclear War' conferences. His work bridges theoretical physics innovations with societal impact.

Felix FritzenView profile

Professor

• Data Analytics in Engineering
• Computational Mechanics
• Material Modeling
• +7 more

Prof. Felix Fritzen is a Heisenberg Professor (W3) for Data Analytics in Engineering at the University of Stuttgart's Institute of Applied Mechanics (MIB). His work is embedded in the Cluster of Excellence Data-Integrated Simulation Science (SimTech). He leads the EMMA Emmy Noether group (2015–2020) and previously headed the KIT Young Investigator Group CAMM. His research focuses on data-driven surrogate models, uncertainty quantification, and computational mechanics of materials, with emphasis on nonlinear model reduction and multiscale simulations. Education: Ph.D. (Dr.-Ing., summa cum laude) from KIT (2011), Dipl.-Math. techn. (2007), Dipl.-Ing. (2006). Research interests include machine learning integration with mechanics, microstructure-property relations, and high-performance simulation techniques. Notable contributions involve FFT-based homogenization, reduced order modeling, and GPU-accelerated methods. He has authored over 60 peer-reviewed papers, including works on surrogate models for microstructure forecasting and thermoelastic material analysis. Awards include the KIT PhD Award (2012) and recognition as a GAMM Junior (2012–2014). His teaching includes courses on data processing for engineers and model order reduction. Funded projects include DFG grants (EXC-2075, HE 7919/1) and the Heisenberg Professorship (FR2702/8).

• Numerical Analysis
• Applied Mathematics
• Scientific Computing
• +4 more

Virginie Ehrlacher is a Professor at CERMICS, École des Ponts ParisTech (ENPC), France. She specializes in applied mathematics with a focus on high-dimensional problems, numerical analysis, and computational modeling. Her work bridges quantum chemistry, materials science, and machine learning through innovative mathematical frameworks. Education includes: PhD in Mathematics (2012) from ENPC: Mathematical models in quantum chemistry and uncertainty quantification Habilitation (2020) from Université Paris-Dauphine: Mathematical and numerical analysis of high-dimensional and multiscale problems in materials science Research spans multiscale modeling, tensor decompositions for high-dimensional systems, cross-diffusion equations, and scientific machine learning. Her work frequently addresses challenges in quantum mechanics, materials science, and computational physics using advanced numerical techniques. Publications emphasize: Algorithms for high-dimensional PDEs and eigenvalue problems Model reduction techniques (tensor networks, reduced basis methods) Cross-diffusion systems with biological/physical applications Neural networks for scientific computing Awards and distinctions: Irène Joliot-Curie Prize (2023) Chevalier de l’Ordre National du Mérite (2025) Leadership includes: ERC Starting Grant HighLEAP (2023–2028) ERC Synergy project EMC2 (2020–2026) ANR JCJC project COMODO (2019–2023) She co-leads the EMS Topical Activity Group on Scientific Machine Learning. Affiliated with the CERMICS laboratory, she collaborates on interdisciplinary teams tackling multiscale and data-driven modeling challenges.