Dr. Sung Sik Lee serves as a Lecturer in the Department of Materials at ETH Zurich, Switzerland. Affiliated with ScopeM (Scientific Center for Optical and Electron Microscopy), he develops microfluidic platforms for real-time cellular analysis at the HPM C 52.2 facility (Otto-Stern-Weg 3, Zürich). His research bridges engineering and biology to investigate cellular responses to mechanical and chemical stimuli. His primary research domains include: Microfluidics : Design of microfabricated devices for cell stretching, particle separation, and dynamic stimulation Cellular Aging : Mechanisms of chromosome loss and nuclear pore complex reorganization in yeast models Nanotoxicology : Impact of nanoplastics on macrophage inflammation and intestinal barrier integrity Advanced Imaging : Application of holotomography and Raman spectroscopy for label-free cellular analysis His work consistently targets translational applications in disease modeling and diagnostics. Analysis of his 50+ publications reveals strong interdisciplinary integration, particularly the convergence of machine learning with microscopy (e.g., automated vacuole quantification in yeast) and the development of open-access resources like MicrobioRaman. Recent trends emphasize nanoparticle-cell interactions and microfluidic solutions for inflammatory conditions including IBD and acute kidney injury. Dr. Lee actively contributes to ScopeM's mission of advancing microscopy techniques, maintaining collaborations across ETH Zurich's research ecosystem. His laboratory focuses on microfluidic device fabrication, cellular mechanotransduction studies, and biophysical characterization of particles and cells, with ongoing projects extending through 2025.
Karol Budohoski, MD, PhD, FRCS, is an Assistant Professor in the Department of Neurosurgery at the University of Utah , with additional Adjunct Assistant Professor status in Radiology & Imaging Sciences. He specializes in cerebrovascular , endovascular , and skull base neurosurgery , treating complex pathologies like brain aneurysms, arteriovenous malformations, and skull base tumors. Education: PhD in Neurosurgery (University of Cambridge), MD (Medical University of Warsaw), Clinical Fellowships at University of Utah and UCSF His academic focus on subarachnoid hemorrhage pathophysiology and cerebral vasospasm has led to innovations in brain monitoring tools. Recent publications (2023-2025) span neurovascular surgery , stroke interventions , global neurosurgery , and cerebral autoregulation studies. He practices at the Clinical Neurosciences Center in Salt Lake City, Utah, with a patient rating of 4.9/5 (125 reviews), praised for clarity, attentiveness, and technical expertise.
Paul A. Yushkevich is a Professor of Radiology at the Perelman School of Medicine, University of Pennsylvania , with affiliations in the Bioengineering Graduate Group . He leads the Penn Image Computing and Science Laboratory (PICSL) , focusing on advanced biomedical image analysis techniques. Developed first-of-its-kind computational atlas of the hippocampal formation Led NIH R01-funded research on MRI-derived biomarkers for Alzheimer's disease Created open-source software tools: ITK-SNAP and Convert3D Expert in statistical shape modeling and histology-MRI co-registration Research Focus: Specializes in hippocampal segmentation using high-resolution MRI, with applications in Alzheimer's disease research and cardiac imaging . His work combines differential equations and machine learning for accurate image analysis. Scientific Achievements: First-place MICCAI segmentation challenges (2012, 2013) Over 169 PubMed publications in neuroimaging and computational anatomy Developed DTI-TK toolkit for diffusion MRI analysis Collaborations: Works with Alzheimer's Disease Neuroimaging Initiative (ADNI) and multiple international institutions. Supervises graduate students in biomedical image analysis.
Jason Au is an Assistant Professor at the University of Waterloo, specializing in vascular physiology and exercise-related cardiovascular dynamics. His research focuses on understanding complex blood flow patterns, arterial stiffness, and the impact of exercise on vascular health. He leads the Vascular Observations through Research on Technology and Exercise (VORTEX) Lab, integrating advanced imaging techniques like high-frame-rate ultrasound. Dr. Au holds a BSc and PhD in Kinesiology from McMaster University and a Postdoctoral Fellowship in Electrical & Computer Engineering at the University of Waterloo. His research interests include three main areas: 1) Complex blood flow and wall motion in arteries/veins, 2) Sedentary behavior/exercise exposure on vascular risk, and 3) Novel biomarkers of vascular disease progression. His work combines experimental models with computational approaches to study vascular health comprehensively. Key publications highlight innovations in ultrasound imaging (e.g., vector projectile imaging) and physiological mechanisms like arterial wall motion regulation. Dr. Au’s lab offers graduate supervision across MSc, PhD, and postdoctoral levels, with active projects exploring exercise countermeasures to vascular disease and technological advancements in medical imaging. Laboratory activities emphasize translational research, bridging basic science and clinical applications to improve cardiovascular health outcomes. Collaborations span engineering, physiology, and clinical domains to address unresolved questions in vascular biology.
Naftali Raz is a Professor of Psychology at Stony Brook University, specializing in Integrative Neuroscience. He holds a Ph.D. from the University of Texas at Austin (1985) and a B.A. from the Hebrew University of Jerusalem (1979). His research focuses on understanding age-related changes in the brain and cognition, particularly exploring metabolic, vascular, and inflammatory risk factors influencing cognitive aging. He employs neuroimaging techniques such as MRI, MRS, and fMRI to study brain structure, function, and metabolism in healthy aging populations. Raz’s research emphasizes the 'FRIENDS' model (Free-Radical Induced Energetic and Neural Decline in Senescence), linking aging to energy production decline. His work includes longitudinal studies on brain atrophy, myelin content, and iron accumulation. He investigates how physiological risk factors like cardiovascular disease and metabolic syndrome impact neurocognitive trajectories. Current grants include NIA funding for neural correlates of cognitive aging and hippocampal glutamate modulation studies. Education: Ph.D. in Psychology, University of Texas at Austin (1985) B.A. in Psychology, Hebrew University, Jerusalem, Israel (1979) Labs/Facilities: Integrative Neuroscience Group, SCAN Center (Stony Brook Advanced Neuroimaging) His publications span over three decades, with recent works on recognition memory strategies, hippocampal subfield analysis, and cerebral blood flow dynamics. Collaborations include multi-institutional projects on neuroimaging protocols and aging mechanisms.
Mohamed Farhat is a Senior Scientist at EPFL's School of Engineering, Department of Mechanical Engineering, where he leads the Research Group on Cavitation and Interface Phenomena. He serves as PhD Director, Lecturer, and Member of EPFL Doctoral Committee (Mechanics), while also representing EPFL at CLUSER association and coordinating activities at the Société Hydrotechnique de France (SHF). His research expertise spans Cavitation & Multiphase flows, Flow Induced Noise & Vibration, Fluid-Structure Interaction, Flow control, Flow instabilities in hydro turbines and pumps, Condition monitoring of Hydraulic Machines, Hemodynamics, and Advanced Instrumentation in Fluid Dynamics. Farhat's work uniquely bridges fundamental fluid mechanics with practical applications across hydropower, marine propulsion, healthcare, and water management sectors. Analysis of his recent publications reveals strong focus on cavitation bubble dynamics, with particular emphasis on measurement techniques for collapsing bubbles, vortex shedding control, hydrodynamic monitoring of hydraulic machinery, and biomedical applications of cavitation phenomena. His work increasingly integrates advanced imaging techniques with computational modeling to understand complex multiphase flow phenomena. 2021: Life Sciences Book Award of the International Academy of Astronautics 2019: 1st Prize Winner of Scientific Image Contest (Swiss National Science Foundation) 2020: EPFL-Rhyming Prize (Best PhD thesis in Fluid Mechanics) 2018: EPFL-EDME Prize (Best PhD thesis in Mechanics) 2015: Edmund Optics Educational Award 2014: APS-DFD Gallery of Fluid Motion Award Farhat has successfully supervised numerous PhD students including Ali Amini, Philippe Ausoni, and Outi Supponen, with research spanning from fundamental bubble dynamics to practical hydraulic machinery applications. His Cavitation Research Group maintains strong collaborations with industry partners in hydropower and medical device sectors. Current research directions include advanced instrumentation for cavitation monitoring, condition-based maintenance of hydraulic machinery, and biomedical applications of cavitation phenomena in therapeutic ultrasound and drug delivery.
Joseph Katz is the William F. Ward Distinguished Professor of Mechanical Engineering at Johns Hopkins University's Whiting School of Engineering and a member of the National Academy of Engineering. His research focuses on experimental fluid mechanics, multiphase flow, cavitation phenomena, and advanced optical diagnostics. He directs the Laboratory for Experimental Fluid Dynamics and co-founded the Johns Hopkins Center for Environmental and Applied Fluid Mechanics. Key research areas include: - Turbulent boundary layers and compliant wall interactions - Cavitation dynamics in turbomachinery - Environmental fluid dynamics (oil spills, oceanic flows) - Medical imaging applications of fluid mechanics - Turbomachinery flow control (axial compressors) His work has been funded by agencies including the Office of Naval Research, NSF, NASA, and DOE. Over 150+ journal papers, 220+ conference papers, and 7 patents reflect his prolific output. Notable awards include the ASME Fluids Engineering Award and fellowships from ASME and APS. Key Contributions: - Developed novel optical diagnostics techniques - Advanced understanding of tip clearance flows in compressors - Studied oil dispersion mechanisms in marine environments - Pioneered holographic PIV for 3D flow visualization
Eva Gerdts is a Professor at the Clinical Institute 2, University of Bergen, and is affiliated with Haukeland University Hospital. She is a Member of the Norwegian Academy of Sciences and leads the Bergen Hypertension and Cardiac Dynamics Group. Her work is central to the Center for Research on Heart Disease in Women, established in 2020 with support from the Heart Foundation, Bergen Women's Health Association, and the Grieg Foundation. University: University of Bergen Affiliation: Clinical Institute 2 Research Group: Hypertension and Cardiac Dynamics Center: Center for Research on Heart Disease in Women Email: eva.gerdts@uib.no Her research focuses on heart disease in women , particularly as influenced by hypertension, aortic valve stenosis, obesity, and autoimmune diseases. She investigates sex-specific differences in cardiac strain, arterial stiffness, and myocardial remodeling. Her work emphasizes how male-based data cannot be extrapolated to women, advocating for gender-specific cardiovascular guidelines. Her recent publications span population studies like the Tromsø and Hordaland Health Surveys, clinical trials, and international collaborations. Key trends include sex differences in hypertension outcomes , cardiac effects of bariatric surgery , cryptogenic stroke in young adults , and inflammatory markers in autoimmune diseases . Her research integrates echocardiography, longitudinal data, and public health implications. Scientific recognition includes: Member of the Norwegian Academy of Sciences National Heart Association's Heart Research Prize 2022 She supervises multiple PhD and Master’s students, including Ester Kringeland, Sahrai Saeed, and Arleen Aune. She leads the PhD course NORHEART901 in Cardiovascular Imaging and teaches in medical education on hypertension, cardiac ultrasound, and women's heart health. Her research is supported by large-scale population studies and clinical collaborations, particularly through the NOR-SYS and SECRETO projects. She also organizes professional education on valvular disease and dyspnea. She leads or collaborates with several research teams: Bergen Hypertension and Cardiac Dynamics Group Center for Research on Heart Disease in Women NOR-SYS (Norwegian Stroke in the Young Study) SECRETO (Searching for Explanations for Cryptogenic Stroke in the Young) FATCOR Study (Fitness, Adiposity, and Cardiovascular Risk)
Prof. Dr.-Ing. Maria Francesca Spadea serves as Director of the Institute of Biomedical Engineering (IBT) at Karlsruhe Institute of Technology (KIT), part of the Helmholtz Association. Her leadership role includes overseeing research initiatives, teaching activities, and administrative responsibilities within the institute. Located in space 512, she maintains regular consultation hours on Wednesdays from 10:30-11:30 am by appointment. Professor Spadea's research spans several cutting-edge areas in biomedical engineering, with particular focus on medical image processing, artificial intelligence applications in healthcare, and radiomics. Her work bridges computational techniques with clinical applications, emphasizing practical solutions for medical imaging challenges. She has pioneered approaches in federated learning for medical image translation, particularly in CT/MRI synthesis for radiation therapy applications. Her research also extends to cancer cell analysis, vascular biomechanics, and medical robotics, demonstrating a broad yet cohesive research portfolio that addresses critical challenges in modern healthcare. Analysis of Professor Spadea's recent publications reveals a strong emphasis on AI-driven medical imaging solutions, particularly in the translation between different imaging modalities (like MRI-to-CT) using federated learning approaches that preserve patient privacy. Her work demonstrates growing specialization in radiation therapy applications, with multiple publications addressing synthetic CT generation for treatment planning. There's also a clear trajectory toward multi-institutional collaboration, as evidenced by her involvement in projects spanning multiple research centers across Europe. Professor Spadea actively mentors numerous students, including M. Krohmer Zabaleta, N. Skupien, and M. Destito, who have completed bachelor's and master's theses under her supervision. Her research group appears well-integrated within the broader Institute of Biomedical Engineering, collaborating extensively with colleagues like P. Zaffino and C.B. Raggio on multiple projects. The group maintains strong connections with clinical partners, as evidenced by publications addressing real-world medical challenges in radiation therapy, cardiology, and neurosurgery. The research activities of Professor Spadea's team are centered within the Institute of Biomedical Engineering at KIT, with particular focus on medical imaging processing and AI applications. Her laboratory appears to specialize in developing computational tools for medical image analysis, with recent work emphasizing privacy-preserving federated learning frameworks that enable multi-institutional collaboration without sharing sensitive patient data. The team maintains active collaborations with clinical departments, particularly in radiation oncology, as evidenced by numerous publications addressing CT synthesis for radiation therapy planning.
Prof. Dr. Oliver Hayden holds the Heinz-Nixdorf-Chair for Biomedical Electronics at the TUM School of Computation, Information and Technology, Technical University of Munich. His research develops innovative in-vitro diagnostic techniques using interdisciplinary approaches spanning electronics, optics, microfluidics, and materials science. Current investigations focus on magnetic/optical diagnostics for blood cell analysis. Education includes a biochemistry degree and doctorate (1999) from the University of Vienna, postdoctoral work at Harvard University, and a teaching qualification in Analytical Chemistry. Professional experience encompasses positions at IBM Research Zurich and Siemens Healthcare, where he pioneered organic electronic techniques for medical diagnostics prior to joining TUM in 2017. Research explores biomedical electronics, microfluidic systems, and materials science for diagnostic applications. Recent publications demonstrate strong focus on quantitative imaging and magnetic cytometry for blood analysis, particularly in point-of-care diagnostics, COVID-19 severity assessment, and leukemia detection. Scientific Awards: European Inventor Award (2017) AMA Innovation Award (2016) Siemens NTF Award for Medical Imaging Patents (2013) Young Investigator Award, Society of Austrian Chemists (2002)
Professor Mauricio Villarroel is an Associate Professor of Biomedical Engineering at the University of Oxford's Institute of Biomedical Engineering and a Fellow of Magdalen College. He leads the Laboratory for Computational Medicine and Technology (LCMT), which focuses on improving clinical decision-making through digital health innovations for both high-income and low- or middle-income countries. Villarroel was born in Bolivia where he completed his undergraduate engineering degree before obtaining his doctoral degree in Engineering Science from the University of Oxford. He previously worked as a research scientist at the Health Sciences and Technology department at MIT and Harvard University, collaborating with multidisciplinary teams from academia, hospitals, and industry to develop advanced monitoring algorithms for intensive care. He returned to Oxford as a post-doctoral research assistant in Data Fusion & Telehealth and later served as a Senior Researcher in Next Generation of Digital Health. His research focuses on developing non-contact video-based physiological monitoring technologies to create personalized biomarkers of health. He has founded the spinout company OxeHealth based on his early work. Currently, his laboratory develops AI models to identify meaningful physiological changes using multimodal sensing technologies including video cameras, wearable devices, wireless technologies, smartphones, and body-worn sensors. His primary research areas include cardiovascular disease and neurodegenerative diseases, spanning from early detection of chronic conditions to in-hospital monitoring and remote management in community settings. He is also the first academic appointment of The Podium Institute for Sports Medicine and Technology, where he develops technologies to monitor factors leading to sports injuries in young athletes aged 11-18 years. Analysis of his recent publications reveals a strong focus on non-contact physiological monitoring, particularly using photoplethysmography and video-based technologies. His work spans cardiovascular monitoring (blood pressure estimation, circadian rhythms), neurological applications (movement disorders), respiratory monitoring (particularly in infants), and sports medicine (athlete screening). A consistent theme across his research is the development of AI-driven, multimodal approaches to extract meaningful clinical information from non-invasive or contactless monitoring systems. Villarroel has received significant recognition for his work, with multiple publications referenced in patents and clinical guidelines. His research has been picked up by news outlets and widely shared on social media platforms, indicating substantial impact in both academic and practical domains. His work on non-contact monitoring has particularly gained attention for its potential applications in resource-limited settings. As a research leader, Villarroel collaborates extensively with clinicians, engineers, and industry partners. His laboratory offers DPhil opportunities at the intersection of medicine, engineering, and technology. His research has led to practical applications including technologies for monitoring post-operative patients, detecting apnea in infants, and screening athletes for cardiac conditions that could lead to sudden death. The Laboratory for Computational Medicine and Technology maintains strong connections with Oxford's Medical Sciences campus, adjacent to the Churchill Hospital, facilitating direct translation of engineering innovations into clinical practice. The lab's work bridges multiple domains including computer vision, signal processing, AI, and clinical medicine to address significant healthcare challenges.
Associate Professor Sara Baratchi heads the Mechanobiology and Microfluidics Laboratory at the Baker Heart and Diabetes Institute and co-leads the Heart Attack Research Program. She holds academic appointments as a supervisor at RMIT University and the University of Melbourne, and is the Alice Baker and Eleanor Shaw Gender Equity Fellow. Her interdisciplinary work bridges engineering, immunology, and clinical science to address cardiovascular pathologies through innovative bioengineering approaches. Dr. Baratchi's research centers on mechanotransduction in vascular and immune cells, particularly how hemodynamic forces and extracellular matrix stiffness regulate cellular behavior in diseases like atherosclerosis and calcific aortic valve disease. She pioneers organ-on-a-chip platforms that replicate human vascular systems under pathological conditions, integrating microfluidics, single-cell omics, and patient-derived samples to develop ethical alternatives to animal testing and identify novel therapeutic targets. Her recent publications demonstrate a cohesive research trajectory focused on Piezo1-mediated mechanosensing, microfluidic device innovation, and the pathophysiological impact of altered hemodynamics. Key themes include endothelial cell responses to shear stress, substrate stiffness effects on vascular cells, and the development of dynamic flow systems for cardiovascular modeling, all aimed at translating mechanobiological insights into clinical interventions. Dr. Baratchi has received significant recognition including: Australian Vascular Biology Society Achievement and Career Development Award (2023) Alice Baker and Eleanor Shaw Gender Equity Fellowship (2023) ARC Discovery Early Career Researcher Award (2017-2020) Best Basic Research Award at Baker Institute (2020) RMIT University Established Researcher Award (2022) She has secured over $2.5 million in competitive funding from ARC and NHMRC, mentoring 20+ PhD researchers who now lead in academia and industry. As President Elect of the Australian Society for Mechanobiology and committee member for MicroTAS 2024-2025, she actively shapes the field through leadership and international collaboration. Her laboratory develops cutting-edge microfluidic platforms adopted globally, collaborating with institutions across 11+ disciplines. Current work focuses on dissecting how matrix stiffness and hemodynamic alterations in cardiovascular conditions drive pathological cellular crosstalk, aiming to establish foundational knowledge for non-invasive disease-modifying therapies.
Cam Ha Tran is an Assistant Professor in the Department of Physiology and Cell Biology at the University of Nevada, Reno, affiliated with the Institute of Neuroscience. Her research focuses on neurovascular unit interactions, particularly how blood flow regulation impacts brain function under health and disease conditions such as stroke and dementia. She employs advanced techniques like two-photon imaging, optogenetics, and electrophysiology to study astrocyte-endothelial communication and vascular reactivity. Education: PhD in Cardiovascular and Respiratory Sciences from the Cumming School of Medicine, University of Calgary (Canada); Master of Biomedical Technology and Bachelor of Science from the University of Alberta (Canada). Research emphasizes understanding how astrocytes and endothelial cells coordinate to maintain cerebral blood flow, with implications for neurological disorders. Her recent work explores TRPA1 channels in neurovascular coupling, astrocyte dysfunction in Alzheimer’s, and seizure-induced vascular changes. Techniques include in vivo imaging and chemogenetic approaches to dissect cellular mechanisms. Key contributions include uncovering astrocyte roles in functional hyperemia and identifying therapeutic targets for cerebrovascular diseases. Her lab’s findings bridge basic science and clinical applications, aiming to improve diagnostics and treatments for stroke and neurodegenerative conditions.
Philippe Moireau is a Full Professor in the Department of Applied Mathematics at École Polytechnique, where he is also affiliated with the Center for Applied Mathematics (CMAP). He serves as the head of the Inria Project-Team MΞDISIM (Mathematical and Mechanical Modeling with Data Interaction for Simulation in Medicine) and holds the distinguished position of Ingénieur Général of The Corps des Mines. His primary research focuses on inverse problems and data assimilation for partial differential equation models, with particular emphasis on: Observer-based methods from optimal control perspectives Stabilization approaches for evolution equations Numerical analysis of time-dependent control problems Digital twin applications in cardiovascular medicine Professor Moireau's publication portfolio demonstrates consistent focus on mathematical methods for physical systems, with recurring themes in: Data assimilation techniques for PDE-based models Numerical stabilization and discretization methods Cardiovascular biomechanics and hemodynamics Stochastic modeling of biological systems Epidemiological forecasting and control He leads the ANANKΞ project-team at Inria focused on Analysis And Numerics of physical-Knowledge-based Estimation. His educational contributions include lectures on data assimilation theory at CEMRACS and courses on mathematical modeling in cardiac biomechanics at Institut Polytechnique de Paris.
Rainald Loehner is a Distinguished Professor of Fluid Dynamics at George Mason University's Center for Computational Fluid Dynamics. Since 2003, he has led the Center for Computational Fluid Dynamics at George Mason University. He is currently a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS) for 2023, hosted by Professors Kai-Uwe Bletzinger and Roland Wüchner in the 'Adjoint-Based System Identification of Large-Scale Structures' Focus Group. Loehner received his Diplom Ingenieur (Maschinenbau) degree from the Technical University of Braunschweig, and his PhD and a DSc in civil engineering from the University College of Swansea, Wales. After teaching at Swansea for a year, he worked at the Naval Research Laboratory in Washington, DC, followed by a research professorship at George Washington University. He joined George Mason University as an associate professor and was promoted to full professor in 1995 and distinguished professor in 2004. With over 35 years of experience, Professor Loehner's research spans the complete pipeline of numerical solvers and simulation tools. His expertise includes pre-processing, grid generation, numerical methods, field solvers, parallel computing, adaptive mesh refinement, fluid-structure interaction, shape optimization, system identification, and computational crowd dynamics. His current work focuses on developing advanced field solvers for compressible and incompressible flows, acoustics, electromagnetic wave propagation, heat and mass transfer, structural mechanics, and fluid-structure interaction. Key application areas include blast mitigation, ship hydrodynamics, blood flow, contaminant transport, and pedestrian safety. Loehner's recent research output (2020-2024) shows a strong trend toward digital twin technology and adjoint-based methods for structural analysis and optimization. His publications focus on high-fidelity digital twins for detecting structural weaknesses, risk assessment in engineering systems, and optimization of sensor placement. His work bridges computational mechanics with machine learning approaches, particularly in system identification and inverse problems, demonstrating how computational methods can solve complex real-world engineering challenges. 2020: Ranked #15119 in the Stanford List of Most Influential Scientists of the World; #8 in Aerospace and Aeronautics 2010: Distinguished International Career Award, Argentine Association of Computational Mechanics 2008: Fellow, International Association for Computational Mechanics 2006: Associate Fellow, AIAA 2005: Honorary Professor, University of Wales Swansea 2005: Advisory Professor, Shanghai Jiao Tong University 2004: Distinguished Professor of Fluid Dynamics, George Mason University 1999: Computational Mechanics Achievements Award, Japan Society of Mechanical Engineering 1993: Doctor of Science in Civil Engineering, University College of Swansea 1979-1983: Studienstiftung des Deutschen Volkes (Top 1% of German Students) Professor Loehner has mentored numerous students through his work at George Mason University and has supervised research in computational fluid dynamics, structural mechanics, and related fields. His research has been supported by various grants from government agencies and industry partners, enabling the development of advanced simulation tools applied in aerodynamics, hydrodynamics, shock-structure interaction, and medical applications. His codes and methods have been widely adopted in industry and academia for applications ranging from aircraft and ship design to medical simulations and urban pathogen transmission modeling. Loehner leads the Center for Computational Fluid Dynamics at George Mason University, which focuses on developing cutting-edge computational methods for fluid dynamics and related multiphysics problems. The center works on strategic application areas including blast mitigation, ship hydrodynamics, blood flow simulation, and pedestrian movement modeling. As a TUM-IAS Fellow, he collaborates with the Chair of Computational Modeling and Simulation at TUM on adjoint-based system identification of large-scale structures, bringing together expertise in computational mechanics and digital twin technology to address complex engineering challenges.
