جست‌وجوی استاد و پژوهشگر

Marek Behrمشاهده پروفایل

استاد

• Computational Mechanics
• Fluid Dynamics
• Biomedical Engineering
• +۳ مورد دیگر

Marek Behr is a Professor (C4/W3) and Chairholder at the Chair for Computational Analysis of Technical Systems within the Faculty of Mechanical Engineering at RWTH Aachen University since 2004. He also holds an Adjunct Professorship at Rice University’s Department of Chemical and Biomolecular Engineering since 2005. His academic leadership roles include serving as Founding Director of the Center for Simulation and Data Science (CSD) since 2018, President of the German Association for Computational Mechanics (GACM) since 2021, and Executive Council Member of the International Association for Computational Mechanics (IACM) since 2020. He coordinates the International Research Training Group 2379 Modern Inverse Problems with the University of Texas at Austin. Behr’s research focuses on computational fluid dynamics (CFD) , biomedical engineering , and finite element methods , with applications in cardiovascular modeling, multiphysics systems, and manufacturing processes. His work bridges engineering and medicine, addressing challenges like in-stent restenosis, hemolysis prediction, and advanced material processing. He has an h-index of 45 (Google Scholar) and over 3,600 citations. Teaching responsibilities include courses such as Simulation Methods in Mechanical Engineering , Parallel Computing in Computational Mechanics , and Finite Elements in Fluids . His research group actively collaborates with institutions like the Jülich-Aachen Research Alliance (JARA) and the Oden Institute . Recent publications emphasize computational models for cardiovascular devices, machine learning in structural health monitoring, and advanced manufacturing simulations, reflecting his interdisciplinary approach to solving complex engineering problems.

Antoine Marteauمشاهده پروفایل

پژوهشگر ارشد

• Numerical Analysis
• Partial Differential Equations
• Multiscale Modelling
• +۳ مورد دیگر

Antoine Marteau is a Research Fellow at the School of Mathematics, Monash University, specializing in numerical methods for multi-physics systems. He holds a PhD in Electrical Engineering (2024) and a Master's in Applied Mathematics (2020) from Grenoble Alpes University, with research bridging applied mathematics, computer science, and physical modeling. Education: PhD in Electrical Engineering, Universite Grenoblé Alpes, 2020-2024 (awarded April 3, 2024) Master in Applied Mathematics and Computer Sciences, Institut polytechnique de Grenoble, 2017-2020 (awarded April 15, 2021) Research Focus: Dr. Marteau develops advanced numerical discretization techniques for Partial Differential Equations on evolving surfaces, with expertise in finite element analysis of multi-physics problems, high-order spatial discretization, and Heterogeneous Multiscale Methods (HMM). His PhD work revolutionized understanding of dynamic magnetic hysteresis in heterogeneous media through novel homogenized magnetic field definitions. This research directly supports UN Sustainable Development Goals for industry innovation and clean energy infrastructure. Publication Trends: His sole 2024 output—a 194-page PhD thesis—exemplifies his concentrated focus on multiscale electromagnetic modeling. The work demonstrates consistent integration of computational mathematics with real-world engineering challenges, particularly in nonlinear transient phenomena and confined eddy current analysis. Future directions indicate expansion into surface PDE applications and high-order functional discretization. Professional Engagement: Dr. Marteau actively shapes academic discourse through conference leadership, having organized the 2024 Computational Techniques and Applications Conference and co-organizing the 2025 MATRIX workshop on Numerical Analysis of Interface and Multiphysics Problems. Advising & Resources: No current advisees or grant funding are documented in available records. Research Environment: While affiliated with Monash University's School of Mathematics, specific laboratory or team structures remain unreported.

Catherine Constableمشاهده پروفایل

استاد

• Geomagnetism
• Paleomagnetism
• Mathematical and Theoretical Geophysics
• +۳ مورد دیگر

Catherine Constable is a Professor in the Department of Geophysics at Scripps Institution of Oceanography, University of California San Diego. She is also affiliated with the Institute of Geophysics and Planetary Physics (IGPP). Her research focuses on Earth’s magnetic field dynamics, combining paleomagnetism, geomagnetism, and advanced numerical modeling techniques. She actively develops open-source tools and databases to standardize geophysical data sharing. Education: B.S., University of Western Australia; M.S., Australian National University; Ph.D., Scripps Institution of Oceanography, UCSD. Research Interests: Catherine’s work explores secular variation, geomagnetic reversals and excursions, core dynamics, and mantle conductivity influences on the geodynamo. She emphasizes statistical methods and inverse problem solutions in geophysical analysis. Key projects include the Magnetic Information Consortium (MagIC) database and FIESTA platform integration. Grants & Collaboration: She leads NSF- and NERC-funded initiatives such as the CSEDI project (2023) and collaborates on frameworks like FIESTA to enhance data interoperability. No formal advisees are listed, but her programs like Scripps-GEO Scholars support student development. Labs & Resources: Hosts a research lab at IGPP with dedicated websites (igppweb.ucsd.edu/~cathy and igpp.ucsd.edu) for data visualization tools, animations of geomagnetic components, and PmagPy software development efforts.

Masoud Baratiمشاهده پروفایل

استادیار

• Electric Power Systems Modeling
• Quantum Computing
• Optimization and Simulation
• +۸ مورد دیگر

Masoud Barati is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Pittsburgh's Swanson School of Engineering. Previously, he held similar roles at the University of Houston and Louisiana State University. He earned his PhD in Electrical Engineering from the Illinois Institute of Technology (2009-2013) and completed a postdoctoral fellowship at the University of Chicago Booth School of Business in 2014. His research bridges pure and applied mathematics with electric power systems, focusing on resilience, quantum computing, and control systems. Barati’s academic journey includes a diverse range of studies: his doctoral work at Illinois Tech focused on electrical engineering, while his postdoctoral research at the University of Chicago explored business-related systems through a computational lens. His current role at the University of Pittsburgh allows him to advance interdisciplinary research in energy systems. Research interests span algebraic geometry and category theory for computational problem-solving in power systems, alongside applied areas like optimization, quantum computing, Bayesian statistics, and deep learning. He investigates resilience in power grids under natural hazards and cyber-attacks, with specific attention to AC optimal power flow, energy justice, and multi-interdependent infrastructure management. His work integrates smart city technologies, blockchain for energy trading, and data-driven methods such as kernel regression and neural networks for real-time grid analysis. His recent publications emphasize quantum protocols for entanglement detection, smart city energy infrastructure resilience, and machine learning-augmented approaches to power system control. These trends reflect a focus on leveraging advanced computational tools to address modern energy challenges. Barati has contributed to collaborative research projects, including a global algorithm for AC-OPF optimization funded by the National Science Foundation (NSF). While no scientific awards are explicitly listed, his research portfolio demonstrates significant contributions to energy systems and quantum computing. His advisory work includes various projects on grid resilience, though formal student advisee names are not provided in the text. Grants and funding are associated with his collaborative efforts in developing scalable optimization algorithms for power systems. Research activities involve partnerships with institutions like IBMQ for quantum simulations and local case studies in Pittsburgh. He explores innovative solutions such as mobile charging stations for EVs and blockchain-based energy trading models, reflecting a commitment to both theoretical and applied advancements in energy engineering.

Ellen Luckinsمشاهده پروفایل

مدرس

• Applied Mathematics
• Fluid Mechanics
• Continuum Mechanics
• +۷ مورد دیگر

Dr. Ellen Luckins is a Warwick Zeeman Lecturer and Leverhulme Early Career Fellow at the University of Warwick's Mathematics Institute. Her research focuses on fluid and continuum mechanics, particularly in industrial and environmental applications involving porous media, phase changes, and free-boundary problems. She employs homogenization techniques, asymptotic analysis, and multi-scale modeling to study evaporation, decontamination processes, and material science challenges. Recent work includes modeling impurity deposition during evaporation in porous materials and the dynamics of mushy zones in binary alloys. Education includes a DPhil (PhD) from the University of Oxford's InFoMM CDT, with a thesis on silicon production processes in submerged arc furnaces. She has contributed to study groups on industrial mathematics, addressing problems like energy harvesting and chocolate processing. Her teaching includes advanced fluid mechanics courses at Warwick. Awards include the prestigious Leverhulme Early Career Fellowship, supporting her research in applied mathematics. Her work bridges theoretical modeling with real-world applications, often collaborating with industry partners like Elkem. Key research themes include mathematical decontamination of porous materials, thermal-electrical interactions in metallurgical processes, and evaporation-driven flows in environmental systems.

Alessio Alexiadisمشاهده پروفایل

دانشیار

• Physics-Informed Machine Learning
• Particle Methods (MD, DEM, SPH)
• Computational Fluid Dynamics
• +۷ مورد دیگر

Dr. Alessio Alexiadis is a Reader in Chemical Engineering at the University of Birmingham, specializing in mathematical modeling and physics-informed machine learning. He holds a PhD from Politecnico di Torino (2001) and has held academic positions across 11 institutions in 8 countries. His research focuses on particle methods (MD/DEM/SPH), multiphase flows, and interdisciplinary applications in materials science, biomedical engineering, and environmental modeling. Education: PhD in Chemical Engineering, Politecnico di Torino, 2001 BSc in Chemical Engineering, 1998 Research Interests: Development of Discrete Multiphysics frameworks for complex systems AI-driven simulations and physics-informed machine learning Modeling fluid-structure interactions, colloids, and biophysical systems Applications in energy storage, climate modeling, and biomedical devices Grants & Funding: EPSRC CDT in Formulation Engineering (PI, £multi-million) Leverhulme Trust (PI, 2019–2023) US Office of Naval Research (PI, 2018–2022) Awards & Editorial Roles: Marie Curie Fellowship (2008) Editorial Board member: PLOS ONE, Scientific Reports (Nature), and ChemEngineering Labs & Collaborations: Modelling Concepts and Tools (MCT) group at Birmingham International collaborations spanning 4 continents

• Mathematical Statistics
• Probability Theory
• Learning Theory
• +۷ مورد دیگر

Nikita Zhivotovskiy is a tenure-track Assistant Professor in the Department of Statistics at the University of California, Berkeley. He previously held postdoctoral positions at ETH Zürich and Google Research, Zürich, and was affiliated with the Technion I.I.T. His academic background includes a PhD from the Moscow Institute of Physics and Technology, with affiliations during his studies at the Institute for Information Transmission Problems, Higher School of Economics, and Skoltech. His research lies at the intersection of mathematical statistics, probability theory, and learning theory . Key areas include robust estimation, online learning, statistical learning theory, algorithmic stability, and high-dimensional statistics. His work emphasizes theoretical foundations of machine learning, with a focus on generalization, risk bounds, and learning under non-standard assumptions. The recent publications reflect a strong trend in theoretical machine learning , particularly in understanding the limits and optimality of learning algorithms. Topics span PAC learning, online classification, private estimation, and clustering, often achieving dimension-free or high-probability guarantees. His work frequently appears in top venues such as NeurIPS, COLT, and FOCS, indicating significant impact in the field. Scientific Awards: Best Paper Award at Conference on Learning Theory (COLT), 2020 Nikita Zhivotovskiy has served as a reviewer for leading journals including Annals of Statistics , Probability Theory and Related Fields , and IEEE Transactions on Information Theory , and as a senior program committee member for COLT and ALT. He has co-taught courses at ETH Zürich and has advised or collaborated with numerous researchers, though formal students are not listed. His research has been supported through academic and industrial collaborations, including at Google Research. His work is embedded within the theoretical machine learning community, with active participation in workshops such as those at BIRS, and a growing body of work that bridges statistical theory and algorithmic design. While no formal lab is mentioned, his research group at UC Berkeley likely focuses on foundational aspects of learning and inference.

Nao Tsuchiyaمشاهده پروفایل

استاد

• Consciousness
• Neuroscience
• Attention
• +۶ مورد دیگر

Professor Nao Tsuchiya is a neuroscientist at Monash University's Turner Institute for Brain & Mental Health, specializing in consciousness studies. He holds a PhD in Computation and Neural Systems from Caltech and a Bachelor's in Science from Kyoto University. His research explores the neural basis of consciousness, attention, and qualia, employing methods like neuroimaging (EEG/MEG/fMRI), psychophysical experiments, and mathematical frameworks (category theory, quantum cognition). Current projects include the Dreamscape Project (neurophysiology of dreams), analyzing multi-channel neurophysiological data, and testing quantitative theories of consciousness. He also leads the 'Lifting the Veil' project on visual perception disorders and serves as an editorial board member for Neuroscience of Consciousness . His work addresses fundamental questions about consciousness in animals, machines, and humans, contributing to UN Sustainable Development Goals. Education PhD in Computation and Neural Systems, California Institute of Technology (2000-2005) Bachelor in Science, Kyoto University (1996-2000) Research Interests His lab investigates: Neuronal correlates of conscious/non-conscious processing Consciousness vs attention mechanisms Quantitative theories of consciousness (e.g., Integrated Information Theory) Qualia structure mapping via mathematical models Cross-modal perception and emotion Grants & Projects Leads/co-leads 15+ research projects including: Australian Research Council-funded Dreamscape Project (2024-2027) NHMRC Equipment Grant for neural stimulation technology (2023-2024) Adversarial testing of consciousness theories (2021) Awards & Recognition While no explicit awards are listed, his work has been referenced in multiple Wikipedia pages and news outlets, indicating academic impact. Labs & Teams Runs the Tsuchiya Lab at Monash with active collaborations across disciplines (psychology, physics, computer science), focusing on experimental and theoretical approaches to consciousness.

Adrian Bauleمشاهده پروفایل

استاد

• Statistical Mechanics
• Non-equilibrium Systems
• Active Matter
• +۳ مورد دیگر

Adrian Baule is a Professor in the School of Mathematical Sciences at Queen Mary University of London (QMUL), holding the title of Reader in Applied Mathematics. He joined QMUL in 2011 as a Lecturer after completing postdoctoral positions at the Rockefeller University and the City College of New York. His doctoral work in theoretical physics was conducted at the University of Leeds, culminating in a PhD in 2008. His research focuses on non-equilibrium statistical mechanics, particularly in active, granular, and living matter systems. He employs stochastic processes, field theory, and machine learning techniques to investigate emergent macroscopic behaviors in complex multi-particle systems. Notable areas include diffusion dynamics in active suspensions, granular packing optimization, and metastable system transitions driven by non-Gaussian noise. Baule’s recent work highlights machine learning applications for optimizing granular packing densities and exploring universal statistical phenomena in dilute active suspensions. His publications span high-impact journals like PNAS , Nature , and Reviews of Modern Physics , reflecting interdisciplinary collaborations in physics, applied mathematics, and computational science. No scientific awards are explicitly mentioned in the text, though his prolific publication record indicates sustained research excellence. He contributes to academic mentorship through courses like Actuarial Mathematics I and engages in collaborative projects addressing packing problems and non-equilibrium dynamics.

Jayathi Y. Murthyمشاهده پروفایل

استاد

• Nanoscale Heat Transfer
• Computational Fluid Dynamics
• Multiscale Multi-Physics Simulations
• +۱۲ مورد دیگر

Jayathi Y. Murthy is a Professor in the Department of Mechanical and Aerospace Engineering at the UCLA Henry Samueli School of Engineering and Applied Science. She served as the Ronald and Valerie Sugar Dean of the school from January 1, 2016, to July 31, 2022, becoming the first woman to hold this position. Her leadership emphasized transformative growth in areas such as engineering in medicine, artificial intelligence, sustainable urban systems, and advanced materials. Research Interests: Murthy's research spans nanoscale heat transfer , computational fluid dynamics (CFD) , and multiscale multi-physics simulations of micro- and nano-electromechanical systems (MEMS/NEMS). She also investigates uncertainty quantification in simulations, particularly in sub-micron thermal transport. Her work bridges fundamental thermal sciences with industrial applications, including energy systems and microelectronics. The 15 most recent articles reflect a strong focus on thermal modeling at micro- and nanoscales , advanced numerical methods in CFD , and uncertainty-aware simulations . Keywords include nanotechnology, heat transfer, computational engineering, and materials science. Subfields span phonon transport, adaptive mesh refinement, hybrid numerical schemes, and inverse methods, showing a consistent trajectory toward high-fidelity, predictive modeling in thermal systems. Scientific Awards and Honors: National Academy of Engineering (NAE) Member Foreign Fellow, Indian National Academy of Engineering (INAE) Fellow, American Society of Mechanical Engineers (ASME) ASME Heat Transfer Memorial Award (2016) ASME Electronics and Photonics Packaging Division Clock Award Advising and Grants: While no specific students are listed, Murthy has led major research centers, including the $21 million NNSA-supported PRISM Center at UT Austin. She has authored over 330 publications and edited the second edition of the Handbook of Numerical Heat Transfer . Her editorial roles include service on Numerical Heat Transfer and the International Journal of Thermal Sciences . Labs and Research Teams: Murthy led the Center for Prediction of Reliability, Integrity and Survivability of Microsystems (PRISM), a multidisciplinary research hub. Her work involves collaborations across mechanical engineering, materials science, and computational modeling, often integrating industrial and academic partners.

Ramakrishna Govinduمشاهده پروفایل

عضو هیئت علمی

• Operations Research
• Decision Support Systems
• Advanced Analytics
• +۴ مورد دیگر

Ramakrishna Govindu is an Assistant Professor of Instruction in the School of Information Systems and Management at the Muma College of Business, University of South Florida Sarasota-Manatee. He holds a Ph.D. in Industrial Engineering from Wayne State University, an M.Tech. from Indian Statistical Institute, and a B.E. from Osmania University. His certifications include ASQ Certified Six Sigma Black Belt (CSSBB) and Certified Quality Engineer (CQE). Education: Ph.D., Industrial Engineering, Wayne State University (2006) M.Tech., Quality, Reliability & Operations Research, Indian Statistical Institute (1992) B.E., Mechanical Engineering (Production Specialization), Osmania University (1990) Research interests focus on operations research, decision support systems, advanced analytics, multi-agent systems, healthcare analytics, supply chain optimization, and cybersecurity threats. He has published extensively in peer-reviewed journals and book chapters, including work on inmate misconduct prediction, IoT analytics frameworks, and supply chain modeling. His funded projects include 'Assessing the Needs, Motivation, and Resilience of Manatee County Veterans' (2018–present). Awards include admission to the Order of Engineer (2006), Young Researcher Connection (INFORMS, 2008), and teaching excellence recognition (2016). Teaching responsibilities include courses in business statistics, operations management, and applied analytics. He has over 20 years of industry and academic experience, specializing in algorithmic solutions for complex business problems and successful project execution strategies.

Bev Johnمشاهده پروفایل

استاد

• Psychological Health
• Substance Misuse
• Behavioral Interventions
• +۳ مورد دیگر

Professor Bev John is a prominent academic at the University of South Wales, affiliated with the Faculty of Life Sciences and Education and the Applied Psychology Research and Innovation Group. She holds the rank of Professor and focuses on applied psychological health research, particularly in promoting positive behavioral change in mental/physical health, substance misuse, and intervention development/evaluation. Her key roles include: Observer member of the Welsh Government's Problem Gambling Cross-Party Group Visiting expert on the Substance Misuse Treatment Framework for the Alcohol-related Brain Damage (ARBD) Project Board Steering group member of the international Beat the Odds Initiative Research Interests: Systematic reviews on addiction treatment economics (e.g., alcohol withdrawal) Substance interactions and mental health (e.g., cocaethylene's effects) Cultural adaptations of emotional regulation interventions Public health policy for gambling and alcohol-related disorders Awards & Recognition: Chairman's Award (2024) for innovative healthcare initiatives NIHR West-Midlands Clinical Research Network Award (2023) Recent Projects (2023-2025): Developing alcohol withdrawal management strategies Improving access to psychological interventions for marginalized communities Global recovery group collaboration through the Beat the Odds Initiative Labs/Teams: Active in multidisciplinary teams addressing substance misuse, public health policy, and behavioral interventions across Wales and internationally.

Mahdi Jafari Siavoshaniمشاهده پروفایل

استادیار

• Machine Learning
• Optimization
• Data Science
• +۱۴ مورد دیگر

Mahdi Jafari Siavoshani is an Assistant Professor in the Department of Computer Science and Engineering at Sharif University of Technology, where he leads the Information, Network, and Learning (INL) Lab. He holds a PhD and MSc from the Swiss Federal Institute of Technology (EPFL), and a BSc in Electrical Engineering and Physics from Sharif University of Technology. Prior to his current role, he was a postdoctoral fellow at the Institute of Network Coding, The Chinese University of Hong Kong. His research spans fundamental problems in information processing, transmission, and analysis, with a focus on theoretical limits and practical algorithm design. Key areas include Machine Learning, Optimization, Data Science, Information and Communication Theory, and Computer Networks. His work integrates probabilistic modeling, network coding, and deep learning to address challenges in secure communication, traffic classification, and distributed systems. The recent publications highlight a strong trend in network coding, information theory, and machine learning applications. His work explores coding-theoretic solutions for secrecy, load balancing in cache networks, belief propagation algorithms, and encrypted traffic classification using deep learning. These contributions reflect a blend of theoretical depth and practical relevance in modern communication systems. Assistant Professor, Department of Computer Science and Engineering, Sharif University of Technology PhD and MSc, Swiss Federal Institute of Technology (EPFL) BSc in Electrical Engineering and Physics, Sharif University of Technology Postdoctoral Fellow, Institute of Network Coding, The Chinese University of Hong Kong He advises research in the INL Lab and teaches courses such as Computer Networks, Engineering Probability and Statistics, Network Coding, and Stochastic Processes. While no formal students are listed, his lab environment fosters collaborative research. There is no mention of external grants or funding in the provided texts. The INL Lab serves as the primary research unit, focusing on information theory, network algorithms, and machine learning applications. The lab integrates theoretical research with practical implementations, particularly in networked systems and data analysis.

• Calculus of Variations
• Partial Differential Equations
• Homogenisation
• +۹ مورد دیگر

Lucia Scardia is an Associate Professor in the Department of Mathematics at Heriot-Watt University (Edinburgh EH14 4AS). Her research focuses on calculus of variations, partial differential equations, homogenisation (deterministic and stochastic), and harmonic analysis, with applications in material science including plasticity, dislocations, non-local aggregation problems, nonlinear elasticity, and fracture theories. Research Focus Primary research domains include: Variational methods for PDEs and homogenisation Mathematical modeling of dislocations and plasticity Stochastic analysis of material defects Nonlocal interaction problems and fracture mechanics Anisotropic energy minimization Publication Trends Recent publications (2019-2025) demonstrate strong focus on: Homogenisation techniques in stochastic/perforated domains; variational analysis of anisotropic/nonlocal energies; explicit minimisers for Coulomb/Riesz interactions; free-discontinuity problems; and mathematical descriptions of dislocation patterns. Work consistently employs Gamma-convergence and variational methods. Student Supervision & Grants Supervised 18+ students including PhD candidate Xavier Pellet (2015-2019) and numerous Bachelor/Master projects on variational methods, dislocations, and PDEs. Major grants include: EPSRC EP/V00204X/1: Mathematical Theory of polycrystalline materials (£576,848, 2021-2024) EPSRC EP/V008897/1: Generalised Solutions of Nonlinear PDEs (£35,960, 2021-2024) EPSRC First Grant EP/N035631/1: Dislocation Patterns beyond equilibrium (£95,699, 2016) Academic Leadership Organizes major conferences including 'Optimal Transport and Calculus of Variations' (ICMS 2023) and 'Polycrystals: Microstructure and Plasticity' (ICMS 2022). Co-founder of the 'One World PDE seminar' (2021-present).

Iason Zisisمشاهده پروفایل

پژوهشگر

• Computational Mechanics
• Fluid Dynamics
• Solid Mechanics
• +۳ مورد دیگر

Iason Zisis is a Greek researcher specializing in computational mechanics and numerical methods, affiliated with Eindhoven University of Technology. He completed his PhD in 2017 with foundational work on smoothed particle hydrodynamics (SPH) for shock simulations in inhomogeneous media, primarily operating within the Center for Analysis, Scientific computing and Applications (CASA). His educational background includes: PhD in Engineering/Mathematics from Eindhoven University of Technology (2017) Zisis's research integrates continuum mechanics, material science, and numerical analysis to develop SPH methodologies for complex physical phenomena. Key focus areas include shock wave propagation in heterogeneous materials, hypervelocity impact dynamics on laminated structures, and mathematical convergence of particle-based simulations. His work bridges theoretical rigor with engineering applications, particularly in fluid-structure interaction and high-speed deformation scenarios. Publication trends from 2012-2018 reveal progression from mathematical derivations (2015 reports) to engineered applications like laminated plate deformation (2016) and formal convergence proofs (2018). The research consistently emphasizes cross-disciplinary validation between continuum theory and particle-system implementations, with growing emphasis on real-world material behavior under extreme conditions. Zisis was embedded in CASA (Center for Analysis, Scientific computing and Applications) at Eindhoven University of Technology, a research group specializing in mathematical modeling and computational techniques for engineering systems. CASA's interdisciplinary environment facilitated his work on multi-physics simulations requiring advanced numerical algorithms and high-performance computing resources.