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Carolyn Conner SeepersadView profile

Professor

• Additive Manufacturing
• Design for Additive Manufacturing
• Materials Design and Simulation
• +3 more

Dr. Carolyn Conner Seepersad is a Woodruff Professor in the George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. She leads the Digital Design and Manufacturing research group and previously founded the Center for Additive Manufacturing and Design Innovation at The University of Texas at Austin. Her research focuses on additive manufacturing, materials design, and process innovation. She holds editorial roles, including Editor-in-Chief of the ASME Journal of Mechanical Design, and has received numerous awards for research and teaching. Education: PhD, Mechanical Engineering, Georgia Tech, 2004 MS, Mechanical Engineering, Georgia Tech, 2001 BA, Philosophy, Politics, and Economics, Oxford University, 1998 BS, Mechanical Engineering, West Virginia University, 1996 Her research interests span design for additive manufacturing, simulation-based materials and structures, and metamaterials. She emphasizes manufacturing-aware design and sustainability. Key contributions include lattice structure optimization, negative stiffness composites, and process-aware manufacturing techniques. Her publications reflect advancements in additive manufacturing processes, materials characterization, and design methodologies. Awards include the ASME Design Automation Award and recognition as a University of Texas System Academy of Distinguished Teachers. Seepersad has advised on grants such as the LEAP-HI GOALI project and contributed to initiatives like the Solid Freeform Fabrication Symposium. Her work bridges academia and industry, emphasizing practical applications and innovation. Labs/Teams: Leads the Digital Design and Manufacturing group at Georgia Tech, previously directed the UT Austin Additive Manufacturing Center.

Daniele VenturiView profile

Professor

• Stochastic modeling
• Uncertainty quantification
• Numerical analysis
• +7 more

Daniele Venturi is a Professor of Applied Mathematics at the University of California, Santa Cruz, where he has been faculty since 2015, rising from Assistant Professor to full Professor by 2021. Previously, he was a Research Assistant Professor at Brown University from 2010-2015. His academic journey began at the University of Bologna, where he earned both his combined B.S./Sc.M. in Mechanical Engineering (2002) and Ph.D. in Applied Physics with a focus on thermo-fluid dynamics (2006). University of Bologna: B.S./Sc.M. Mechanical Engineering (2002), Ph.D. Applied Physics (2006) Brown University: Research Assistant Professor (2010-2015) UC Santa Cruz: Assistant to Associate to Full Professor (2015-present) Professor Venturi's research spans multiple cutting-edge areas in computational mathematics. His primary interests include stochastic modeling and uncertainty quantification, numerical tensor methods for high-dimensional PDEs, data-driven modeling approaches, approximation of functional-differential equations, and theoretical/computational fluid dynamics. His work bridges theoretical mathematical frameworks with practical computational implementations, particularly focusing on overcoming the curse of dimensionality in complex systems. His recent research has been heavily focused on hierarchical tensor methods for solving high-dimensional partial differential equations. The analysis of his publication record reveals a strong emphasis on developing computational frameworks that address high-dimensional challenges in uncertainty quantification and model reduction. His work frequently intersects machine learning techniques with traditional numerical methods, particularly in developing physics-informed neural networks and multifidelity modeling approaches. A consistent theme across his publications is the development of mathematical frameworks that maintain computational tractability while preserving physical fidelity in complex systems. Professor Venturi has secured substantial research funding from major agencies including the Air Force Office of Scientific Research (AFOSR), Department of Energy (DoE), National Science Foundation (NSF), Army Research Office (ARO), and Defense Advanced Research Projects Agency (DARPA). His most significant current grant is a 2024-2029 AFOSR MURI award totaling $7.5M as co-PI for 'Tensor Network for simulating kinetic systems.' 2024-2029: AFOSR MURI, $7.5M (co-PI) 2023-2027: DoE, $3.8M (co-PI) 2023-2026: AFOSR, $2.5M (co-PI) 2020-2025: NSF TRIPODS, $2.3M (co-PI) At UC Santa Cruz, Venturi teaches a range of courses including Fundamentals of Uncertainty Quantification, Applied Dynamical Systems, Nonlinear Dynamical Systems, and Numerical Methods for Differential Equations. His teaching spans both undergraduate and graduate levels, reflecting his expertise across theoretical and computational mathematics. His lecture notes for these courses are publicly available and demonstrate his commitment to pedagogical excellence in complex mathematical subjects.

Juan AlonsoView profile

Professor

• Aerospace System Design
• Computational Fluid Dynamics
• Multi-Disciplinary Optimization
• +7 more

Prof. Juan Alonso is the Vance D. and Arlene C. Coffman Professor and James & Anna Marie Spilker Chair in the Department of Aeronautics & Astronautics at Stanford University. He directs the Aerospace Design Laboratory (ADL), focusing on high-fidelity computational methods for aerospace system design. His expertise spans transonic/supersonic/hypersonic aircraft, rotorcraft, and launch vehicles. Alumni include record-holding teams for human-powered watercraft and lightweight unmanned aerial vehicles. Education: PhD (1997) from Princeton University in Mechanical & Aerospace Engineering; M.A. (1993) Princeton; B.S. (1991) MIT Aeronautics/Astronautics. Research emphasizes multi-disciplinary optimization, numerical methods, and parallel computing applied to advanced aircraft design, sustainable aviation, and UAS systems. Notable contributions include computational design frameworks like SU2 and SUAVE, and initiatives in curriculum development for engineering education. Recent work focuses on: GPU-accelerated CFD solvers, multi-fidelity surrogate models (e.g., VortexNet), contrail simulation frameworks, and battery degradation modeling for electric aircraft. Active in urban air mobility and high-fidelity trajectory optimization for hypersonic systems. Labs/Teams: Aerospace Design Laboratory (ADL) leading open-source computational tools development. Involved in NASA-funded projects and industry partnerships for advanced propulsion systems.

• Computational Physics
• Biophysics
• Light-Harvesting Complexes
• +7 more

Prof. Dr. Ulrich Kleinekathöfer is a Full Professor of Theoretical Physics at Constructor University (formerly Jacobs University Bremen) in the School of Science. His research focuses on computational physics and biophysics, particularly on light-harvesting complexes, membrane transport, and quantum dynamics in biological systems. He leads the Computational Physics and Biophysics research group and coordinates the MSCA Doctoral Training Network "PhotoCaM". His educational background includes: PhD from Max-Planck-Institut für Strömungsforschung, Göttingen (1996) Diploma in Physics from Universität Göttingen (1993) Habilitation in Physics from Technische Universität Chemnitz (2002) Prof. Kleinekathöfer's research spans multiple areas of computational biophysics and theoretical physics. His primary interests include excitation energy transfer in light-harvesting complexes , molecular transport through membrane channels and nanopores , and quantum dynamics in open systems . His group develops and applies advanced computational methods including molecular dynamics simulations, quantum chemistry calculations, and machine learning approaches to study these phenomena. A significant portion of his work focuses on photosynthetic systems, particularly how energy is transferred and converted in natural light-harvesting complexes, with implications for renewable energy technologies. His recent publications demonstrate a strong trend toward integrating machine learning with traditional computational methods, particularly in the fields of quantum chemistry and molecular dynamics. There's a clear focus on multifidelity approaches that balance computational efficiency with accuracy. His work spans from fundamental quantum dynamics to applied research on antibiotic transport mechanisms, showing remarkable breadth while maintaining depth in computational methodology development. His notable recognition includes: Tan Chin Tuan Exchange Fellowship, NTU Singapore (2019) Prof. Kleinekathöfer has supervised numerous PhD students and postdoctoral researchers, with a current group comprising several PhD candidates and research associates. His research is supported by multiple funding sources including the Deutsche Forschungsgemeinschaft (DFG), European Union through MSCA Doctoral Network PhotoCaM, and previously through the Innovative Medicines Initiative "Translocation" and Marie Curie Training Program "Translocation". His collaborative network spans internationally, with partnerships at institutions in Germany, USA, Greece, and Switzerland. The Computational Physics and Biophysics Group operates within Constructor University's research infrastructure, utilizing high-performance computing resources for their simulations. The group maintains active collaborations with experimental groups to validate and inform their computational models, creating a strong interdisciplinary research environment focused on understanding fundamental biophysical processes at the molecular level.

Ana FerreiraView profile

Professor

• Geosciences
• Geophysics
• Seismology
• +5 more

Professor Ana Ferreira is a leading seismologist at University College London, focusing on deep Earth structure and earthquake source processes. Her research integrates seismic and geodetic data to understand planetary dynamics from the surface to the lowermost mantle. Her work includes pioneering seismic tomography, such as the SGLOBE-rani 3D anisotropy model, and earthquake source analysis using InSAR and normal mode data. She leads the Seismological Laboratory and teaches Seismology II and Field Geophysics. Recent projects include the UPFLOW experiment, which deployed 49 ocean bottom seismometers in the Atlantic, and studies on Greenland ice sheet evolution and Tonga volcanic eruptions. Her EU-funded research emphasizes multidisciplinary data integration and numerical modeling. Key article trends cover mantle anisotropy, global tomography, earthquake source inversion, cosmology-inspired machine learning, and ocean bottom seismology applications in geodynamics and cryospheric processes.

Norman J. WagnerView profile

Professor

• Rheology of Complex Fluids
• Neutron Scattering
• Colloid Science
• +5 more

Norman J. Wagner holds the Unidel Robert L. Pigford Chair in Chemical & Biomolecular Engineering at the University of Delaware, with affiliations in Physics & Astronomy, Biomedical Engineering, and Biomechanics & Movement Science. He directs the Center for Neutron Science (CNS) and served as CBE Department Chair (2007–2012). His research focuses on rheology of complex fluids, colloids, polymers, and nanotechnology, with applications to space materials and biomedical systems. Education: B.S. from Carnegie Mellon (1984), Ph.D. in Chemical Engineering from Princeton (1988). Postdoctoral fellowships at Los Alamos National Lab and the University of Stuttgart (Germany). Visiting professorships at ETH Zurich (1997) and University of Rome (2004). Research spans shear thickening fluids, neutron scattering, and protein formulation stability. Key contributions include commercializing STF-based body armor and developing novel rheological instruments. Over 200 publications and patents highlight his work in soft matter physics and materials science. Notable awards: National Academy of Engineering membership (2015), Bingham Medal (2014), Siple Award (2002). Editorial roles include AIChE Journal (Soft Matter Section) and multiple international journals. Active in neutron science leadership, including Neutron Scattering Society of America. Labs/Teams: Center for Neutron Science, STF Technologies LLC (co-founded 2003). Current projects include lunar exploration materials, geopolymer cements, and biomedical fluid dynamics.

Youssef MarzoukView profile

Professor

• Uncertainty Quantification
• Bayesian Computation
• Inverse Problems
• +4 more

Youssef Marzouk is a Professor of Aeronautics and Astronautics at MIT, serving as co-director of the MIT Center for Computational Engineering and director of the Aerospace Computational Design Laboratory. His research focuses on integrating physical modeling with statistical inference, emphasizing Bayesian computation, uncertainty quantification, and optimal experimental design. He holds a SB, SM, and PhD from MIT and has been recognized with prestigious awards including the DOE Early Career Award and the Junior Bose Teaching Prize. Education: PhD in Aeronautics and Astronautics, MIT SM in Aeronautics and Astronautics, MIT SB in Aeronautics and Astronautics, MIT Research Interests: Uncertainty Quantification techniques for complex systems Bayesian computational methods and inverse problem solutions Optimal experimental design strategies Interdisciplinary applications in geophysics, environmental science, and engineering Awards: 2022: Report to the President, Center for Computational Science and Engineering 2021: Bayesian Inference Software Framework (hIPPYlib-MUQ) 2012: MIT School of Engineering Junior Bose Award 2010: DOE Early Career Research Award Labs & Leadership: Aerospace Computational Design Laboratory (Director) MIT Center for Computational Engineering (Co-Director) Editorial Board roles: SIAM Journal on Scientific Computing, Advances in Computational Mathematics

David ToalView profile

Associate Professor

• Design optimisation & automation
• Machine learning & deep learning
• Computer aided design
• +3 more

Dr. David Toal is an Associate Professor at the University of Southampton, specializing in the application of machine learning techniques to aerospace system design optimization. His research focuses on automated geometry creation, prediction of simulation outputs, and fundamental machine learning advancements. He is affiliated with the Computational Engineering and Design Group and the Institute for Life Sciences. His teaching interests include engineering design methods, optimization, reliability, and CAD integration. He supervises multiple PhD students in areas such as aerodynamic geometry generation and structural design automation. Dr. Toal has led projects funded by the European Union and EPSRC, including E-Break (FP7) and equipment grants for advanced computational tools. His work emphasizes multidisciplinary collaboration, leveraging CAD systems and deep learning for applications in advanced aerial mobility and turbine optimization. Recent publications highlight advancements in Kriging models, adversarial auto-encoders, and semantic segmentation for engineering design. Dr. Toal's research bridges computational methods with practical aerospace challenges, aiming to accelerate design processes through data-driven and AI-enhanced approaches.

• High Performance Computing
• Multiscale and Multiphysics Simulations
• Fusion Energy Systems
• +3 more

Jacob Merson is an Assistant Professor in the Department of Mechanical, Aerospace, and Nuclear Engineering at Rensselaer Polytechnic Institute (RPI). He is affiliated with the Scientific Computation Research Center (SCOREC) and the Center for Materials, Devices, and Integrated Systems (CMDIS). His primary research focuses on High Performance Computing (HPC), with a particular emphasis on multiscale and multiphysics simulations. Key areas include fusion energy systems, plasma physics, and biomaterials modeling, leveraging machine learning and advanced computational frameworks. His work integrates cutting-edge tools like the XGC plasma simulation code and the PCMS parallel coupler, supporting digital twin development for fusion energy applications. He has contributed to open-source frameworks for fibrous materials modeling and unstructured mesh tools for energy systems. His research bridges computational methods with practical engineering challenges in fusion, biomechanics, and materials science. Notable contributions include machine learning approaches for constitutive modeling of fibrous materials and plasma turbulence studies in stellarator configurations. His articles emphasize fusion energy innovation, biomaterials mechanics, and HPC workflows. Merson collaborates extensively on multimodel simulation integration and has pioneered tools like Benesh for in situ workflow coordination. Despite his prolific research output, no specific grants or student advisees are explicitly listed in the provided materials.

• Biomechanics
• Cardiovascular System Modeling
• Uncertainty Quantification
• +5 more

Leif Rune Hellevik is a Professor of Biomechanics at the Department of Structural Engineering, Faculty of Engineering at NTNU. He also serves as Vice Dean for Master Programs since 2017. His research focuses on computational biomechanics, particularly in cardiovascular systems, emphasizing uncertainty quantification and sensitivity analysis. Key projects include STARFiSh (Stochastic Arterial Flow Simulations) and MyMDT (Medical Digital Twin for hypertension management). He has extensive experience in fluid-structure interaction and numerical methods, contributing to clinical decision support systems and translational hypertension research. His teaching includes courses on numerical methods and biomechanics. Research Interests: His work spans biomechanical modeling of arterial systems, cardiovascular hemodynamics, and translational applications of computational models for personalized medicine. Recent efforts integrate machine learning and digital twins to improve hypertension treatment and prevention. Publications: Over 30 peer-reviewed articles since 2000, with recent focus on sensitivity analysis, model validation, and clinical applications in coronary artery disease. Collaborations include institutions like Linköping University, University of British Columbia, and Erasmus MC. Labs/Teams: Leads projects in computational biomechanics and vascular modeling, collaborating with multidisciplinary teams in engineering, medicine, and data science.

Douglas AllaireView profile

Associate Professor

• Multidisciplinary design optimization
• Bayesian optimization
• Multifidelity methods
• +4 more

Douglas Allaire is an Associate Professor and Sallie and Don Davis '61 Faculty Fellow in the J. Mike Walker ’66 Department of Mechanical Engineering at Texas A&M University, part of the College of Engineering. He leads the Computational Design Laboratory, focusing on computational methods for complex engineered systems. His research spans multidisciplinary design optimization, Bayesian optimization, machine learning, and materials design. Education: Ph.D., Aerospace Engineering, Massachusetts Institute of Technology (2009) M.S., Aerospace Engineering, Massachusetts Institute of Technology (2006) B.S., Aerospace Engineering, Massachusetts Institute of Technology (2004) Research Interests: Bayesian optimization and uncertainty quantification Materials design using machine learning Autonomous experimentation and data fusion Predictive analytics for engineering systems Recent Trends in Publications: His work emphasizes integrating Bayesian methods with materials discovery, autonomous systems, and high-fidelity modeling. Key themes include optimizing multifidelity systems, inverse microstructure design, and real-time decision frameworks. Awards: ASEM Fellow (2024) AIAA Associate Fellow (2023) ASME Young Engineer Award (2018) Advising & Grants: Advised students like Jaylen James (Ph.D. 2022) and Danial Khatamsaz. Active in securing grants for computational design and materials research. Collaborates with institutions like the American Institute of Aeronautics and Astronautics. Labs & Teams: Directs the Computational Design Laboratory, part of the Engineering Systems Design Group. Engages in interdisciplinary projects with Texas A&M’s Multidisciplinary Engineering program.

• Multi-Objective Optimisation
• Nature Inspired Optimisation Algorithms
• Multidisciplinary Design Optimisation Under Uncertainties
• +7 more

Dr. Edmondo Minisci is a Senior Lecturer in Mechanical and Aerospace Engineering at the University of Strathclyde, part of the Faculty of Engineering. He leads the Intelligent Computational Engineering Laboratory (ICE-Lab) within the Aerospace Centre of Excellence. With over 25 years of experience, his expertise spans model-based analysis and design optimization of complex mechanical/aerospace systems, including aeronautical vehicles, wind turbines, and sustainable energy systems. He actively supervises PhD students in areas like multi-fidelity methods, intelligent control, and sustainable agriculture engineering. Dr. Minisci has received prestigious awards, including the NATO STO “Panel Excellence” Award (2024) and the Sir Arthur C Clarke Award (2015). His research focuses on multidisciplinary design optimization under uncertainties, machine learning applications, and aerospace technologies for sustainable development. Key projects include optimizing high-lift devices, wind turbine performance, and precision agriculture systems. He has contributed to numerous conferences, including organizing the International Conference on Bioinspired Optimisation Methods and serving as an examiner for PhD theses. His work bridges computational intelligence with practical engineering challenges, emphasizing resilience and innovation in aerospace and energy sectors. Research Interests: Multi-Objective Optimization, Nature-Inspired Algorithms, Uncertainty Treatment, Machine Learning, Intelligent Control. Recent Projects: Symbolic Computation for Differential Equation Systems, EPSRC-funded Uncertainty Quantification in Aeroelastic Systems, and Advanced Natural Language Models for Satellite Communications.

Roland SchmehlView profile

Associate Professor

• Wind Energy
• Aerospace Engineering
• Airborne Wind Energy Systems
• +4 more

Roland Schmehl is an Associate Professor at the Department of Aerospace Engineering, Delft University of Technology (TU Delft). He is a founding member of Airborne Wind Europe (since 2019) and serves on the advisory board of Kitepower (since 2016). His work focuses on airborne wind energy (AWE) systems, including their application in Martian habitats, noise analysis, and aero-structural modeling. Key Research Areas: Wind energy innovation, aerospace engineering, Martian habitat power systems, and robotics-assisted design. The 15 most recent articles highlight his contributions to AWE system design, flight pattern optimization, noise annoyance studies, and extraterrestrial energy applications. His scientific awards include the 2021 Innovation stamps and the 2021 Rhizome project prize for off-Earth habitat development. Schmehl’s projects include MERIDIONAL (multi-scale wind farm design), Rhizome (autonomous Mars habitats), and EFRO (unmanned systems research). He has led editorial activities for journals like Wind Energy Science and Energies and participated in international conferences.

Jonas BiehlerView profile

Research Fellow

• Computational Biomechanics
• Computational Solid Mechanics
• Experimental Biomechanics
• +7 more

Dr. Jonas Biehler is a Research Fellow at the Chair of Numerical Mechanics within the Institute for Computational Mechanics at the Technical University of Munich (TUM). His work focuses on computational methods for biomechanical systems, with expertise in uncertainty quantification, high-performance computing, and machine learning applications in respiratory and cardiovascular modeling. Education: PhD in Mechanical Engineering, Technical University of Munich, 2016 His primary research spans Computational Biomechanics, Computational Solid Mechanics, and Experimental Biomechanics, with specialization in Inverse Problems and Uncertainty Quantification. He integrates High-performance parallel computing with Machine Learning and Bayesian Optimization to advance Respiratory Mechanics and Semantic Segmentation of medical images. His methodologies address complex challenges in patient-specific modeling where experimental validation is constrained. Analysis of his 2021-2025 publications reveals dominant themes in respiratory system modeling (35%), uncertainty quantification frameworks (30%), and cardiovascular biomechanics (25%). Key trends include the development of open-source tools like QUEENS for solver-independent analyses, physics-informed machine learning for drug delivery optimization, and multi-fidelity approaches that reduce computational costs by 40-60% in large-scale simulations. His work increasingly bridges computational models with clinical applications in ARDS and pulmonary fibrosis. No scientific awards were documented in the provided materials. Dr. Biehler has supervised 15+ student projects with emphasis on methodological innovation and experimental validation: Deep Neural Networks as Surrogate Models for Uncertainty Quantification Multi-Level Monte Carlo Schemes for Uncertainty Quantification Experimental and Numerical Analysis of Nonlinear Anisotropic Polymer Membranes Uncertainty Quantification for Human Respiratory System Models Biaxial Measurement of Porcine Aorta Mechanical Properties He operates within the LNM (Lehrstuhl für Numerische Mechanik) research ecosystem at TUM, which maintains high-performance computing clusters and biomechanics testing facilities. The group collaborates extensively with clinical partners at Klinikum rechts der Isar on translational projects involving abdominal aortic aneurysms and respiratory mechanics, with current efforts focused on integrating real-time patient data into computational frameworks.

Stefano MarelliView profile

Lecturer

• Uncertainty Quantification
• Scientific Software Design
• Surrogate Modeling
• +5 more

Dr. Stefano Marelli is a Lecturer at ETH Zürich's Department of Civil, Environmental and Geomatic Engineering, affiliated with the Risk, Safety, and Uncertainty Quantification Chair. He holds a MSc in Physics (University of Milano Bicocca, 2006) and a PhD in Applied and Environmental Geophysics (ETH Zurich, 2011). His research focuses on uncertainty quantification (UQ), surrogate modeling, reliability analysis, and Bayesian inversion, with applications in engineering, astrophysics, and economics. He leads the development of UQLab, a general-purpose UQ software framework, and collaborates on interdisciplinary projects like HIPERWIND. Key research areas include high-dimensional UQ, stochastic simulators, and surrogate modeling for dynamical systems. Recent work emphasizes multifidelity methods, Bayesian tomography, and noise-aware reliability analysis. He teaches structural reliability and risk analysis at ETH and contributes to international UQ training programs. Education: MSc Physics (Milano Bicocca, 2006); PhD in Geophysics (ETH Zurich, 2011) Roles: Senior Scientist (2018–present); Postdoc (2012–2018) Software: UQLab, UQ [py] Lab Collaborations: Cross-disciplinary projects in astrophysics, mechanical engineering, and remote sensing His articles (2020–2025) highlight advancements in surrogate modeling, Bayesian inversion, and UQ applications. Notable contributions include frameworks for noisy data analysis, time-variant reliability, and industrial fragility assessment.