Dr. Stefan Klus is a Lecturer at the School of Mathematics and Physics, University of Surrey. His research focuses on data-driven model reduction, transfer operator approximation, and kernel-based machine learning applied to dynamical systems. He specializes in interdisciplinary applications across quantum physics, fluid dynamics, and computational biology. Education: PhD in Industrial Mathematics (2011, Paderborn University) and Habilitation (2020, Freie Universität Berlin). Research Interests : Data-driven modeling and reduced-order methods Koopman operator theory and transfer operators Machine learning for dynamical systems (e.g., Deeptime library) Tensor decompositions and quantum systems analysis Graph-based analysis (e.g., microbiome dynamics) Publications : Klus has contributed to over 50 peer-reviewed articles, with recent work emphasizing: Kernel methods for quantum chemistry and physics Tensor-based approaches for high-dimensional systems Applications in climate science (e.g., Pacific SST modeling) Agent-based modeling and social systems Technical Contributions : Co-developer of the Deeptime Python library for dynamical modeling Pioneer in Koopman operator-based model reduction Advanced graph kernel methods for microbiome analysis
Domniki Asimaki is a Professor of Mechanical and Civil Engineering at the California Institute of Technology (Caltech), part of the Division of Engineering and Applied Science. Her research focuses on geotechnical engineering, computational mechanics, and structural dynamics, with an emphasis on understanding ground motion effects on natural and engineered systems such as dams, tunnels, and urban infrastructure. She holds a Dipl. from the National Technical University of Athens (1998), an M.S. (2000) and Ph.D. (2004) from MIT, joining Caltech in 2014. Key research interests include soil dynamics, wave propagation, regional ground deformation, and soil-foundation-structure interaction. She has pioneered data-driven approaches to integrate numerical simulations with field observations for resilient infrastructure design. Notable achievements include developing the open-source Seismo-VLAB software for seismic analysis and receiving prestigious awards like the Bodossaki Award of Scientific Excellence and the Geotechnical Earthquake Engineering Award. Her work addresses seismic hazards at urban and regional scales, with recent studies on the 2023 Türkiye earthquake, the 2019 Ridgecrest earthquake, and Kathmandu Basin dynamics. She leads initiatives to enhance ground motion prediction, landslide hazard assessment, and infrastructure resilience through advanced modeling and AI-driven methods. Education: Dipl., National Technical University of Athens, 1998 M.S., Massachusetts Institute of Technology, 2000 Ph.D., Massachusetts Institute of Technology, 2004 Awards: Bodossaki Award of Scientific Excellence Geotechnical Earthquake Engineering Award Labs/Teams: Leads research groups focusing on seismic hazard modeling, open-source software development, and geotechnical data assimilation techniques.
Christopher Bailey is a Professor of Advanced Semiconductor Packaging and Director of the Centre for Advanced Semiconductor Packaging at Arizona State University (ASU). He previously served as Professor of Computational Mechanics & Reliability and Associate Dean for Research at the University of Greenwich, UK. At ASU, he leads research on advanced semiconductor packaging, including roles as Principal Investigator (PI) and Co-Investigator (Co-I) on major projects such as the SRC-funded Thermo-Mechanical Modelling and US Chips Act initiatives (e.g., SWAP-Hub, SHIELD, ITSI). His research focuses on semiconductor packaging reliability, thermal management, co-design methodologies, and multiphysics modeling. Education: MBA (Technology Management), Open University, UK PhD, Thames Polytechnic, UK Research Interests: Advanced Semiconductor Packaging Thermal Management Solutions Co-Design and Multiphysics Modeling Reliability of Electronic Components His work integrates computational mechanics, materials science, and engineering to address challenges in high-reliability electronics. Recent projects emphasize predictive modeling for semiconductor packaging failures under thermal-mechanical stress. Awards: IEEE Region 8 Europe Award (2024) IEEE David Feldman Award (2022) Visiting Professorships at IIT Kharagpur (2018/2022) and Hong Kong (2018) Service & Leadership: Former President of IEEE Electronics Packaging Society (2020–2021) Associate Editor for IEEE Transactions on Components, Packaging, and Manufacturing Technology Conference Leadership (e.g., Program Chair for IEEE PAINE 2024) He has secured over $40M in research funding and authored 400+ archival papers, with expertise spanning industry collaborations (e.g., BAe Systems, Rolls Royce) and government advisory roles (EPSRC Peer Review College, UK Research Excellence Framework).
Michael Everett is an Assistant Professor at Northeastern University with a joint appointment in the Department of Electrical & Computer Engineering and the Khoury College of Computer Sciences. He directs the Autonomy & Intelligence Laboratory, focusing on certifiable learning machines at the intersection of robotics, deep learning, and control theory. His research emphasizes safety, reliability, and efficiency in robotics applications like off-road navigation and social environments. Education: PhD in Mechanical Engineering, Massachusetts Institute of Technology (2020) SM in Mechanical Engineering, MIT (2017) SB in Mechanical Engineering, MIT (2015) Research Interests: Robotics and motion planning Control theory and neural network verification Reinforcement learning applications Certifiable safety guarantees for autonomous systems Navigation in dynamic/human environments Awards: Runner-Up: Best Paper Award (ICML 2022) Winner: Best Student Paper (IROS 2017/2023) Editors’ Top 5 Published Articles (IEEE Access 2021) Lab & Contributions: The Autonomy & Intelligence Lab develops algorithms for high-speed off-road autonomy, socially aware navigation, and neural feedback verification. His work includes the RAMP planning pipeline and Evora traversability learning framework. He collaborates with Google’s PAIR team on trustworthy AI.
Professor Aris Syntetos is a Distinguished Research Professor and DSV Chair of Logistics and Manufacturing at Cardiff Business School, Cardiff University. He is the founder and Director of the PARC Institute of Manufacturing, Logistics and Inventory, which includes the RemakerSpace, and leads the university’s strategic partnership with DSV. Previously, he held faculty positions at the University of Salford and Copenhagen Business School. His research focuses on the integration of forecasting and inventory optimization, particularly in the context of intermittent demand, spare parts, closed-loop supply chains, and additive manufacturing. He is renowned for the Syntetos-Boylan Approximation and the Syntetos-Boylan-Croston classification method. His work is driven by sustainability and social impact, aiming to reduce inventory obsolescence and support circular economies. The 15 most recent publications highlight a strong trend toward integrating forecasting with inventory and maintenance decisions, with increasing emphasis on sustainability, social good, and advanced analytics. His work spans healthcare, automotive, retail, and humanitarian logistics, often employing machine learning and empirical validation. 2024 Goodeve Medal (Operational Research Society) 2016 Cardiff University Outstanding Doctoral Supervisor Award 2016 & 2019 Cardiff University Innovation and Impact Awards He has secured over £5 million in research funding as Principal Investigator from EPSRC, Innovate UK, and the Welsh Government, leading projects on remanufacturing, 3D printing, and sustainable supply chains. He advises major firms like Ocado, BT, and DSV, and his methods are used in commercial software. He supervises PhD students and actively promotes knowledge transfer. He is Editor-in-Chief of the IMA Journal of Management Mathematics and serves as Vice-President of the International Society for Inventory Research (ISIR). He has taught in the UK, China, Colombia, Denmark, France, Greece, Italy, and Latvia, primarily in Operations Management and Applied Statistics.
Sairaj Dhople is the Oscar A. Schott Professor in the Department of Electrical and Computer Engineering at the University of Minnesota. His research focuses on renewable energy systems, particularly modeling and control of grid-connected inverters, power-system reliability, and distributed energy resources. University: University of Minnesota Department: Electrical and Computer Engineering Academic Rank: Professor His work spans power systems, power electronics, and control theory, with recent publications examining grid-forming inverters, stability analysis, and hybrid computing solutions for optimization problems. Key research themes include: Equivalent-circuit modeling for renewable systems Large-signal stability assessment inverter-based resources Grey-box system identification of power networks Interoperability standards for grid-forming technologies Scientific awards include the Institute for Advanced Study Faculty Fellowship (2018). Current projects funded by the National Science Foundation and U.S. Department of Energy explore analog/hybrid computing and universal interoperability for grid-forming inverters (UNIFI Consortium). His Dhople Research Group investigates power-system architecture and sustainability challenges.
Tobias Neckel is an Associate Professor at the Institute for Informatics at the Technical University of Munich (TUM), where he leads research projects and coordinates academic programs. He has been the project team leader of the IGGSE Project ExaNIML since 2018, main coordinator of the Ferienakademie since 2014, and Program Coordinator of the Bavarian Graduate School of Computational Engineering (BGCE) since 2009. Diploma in Technomathematik from TU München (2005) Dr. rer. nat. in Informatics from TU München (2009) Neckel's research focuses on Uncertainty Quantification, Random Differential Equations, and High Performance Computing. His work develops efficient numerical algorithms using hierarchic and adaptive methods such as octrees/spacetrees and sparse grids, with applications in fluid-structure interactions and incompressible fluid flow simulation. His research bridges theoretical mathematics with practical computational science, emphasizing robust and efficient implementations. His recent publications demonstrate a strong trajectory in multi-fidelity modeling, uncertainty quantification, and high-performance computing. Neckel has made significant contributions to scalable hierarchical approximation methods, dynamic resource management in HPC, and the application of machine learning techniques to computational science problems. His work spans diverse application domains including plasma physics, hydrology, and computational engineering. Lehrfonds prize of the TUM (2014) Ernst Otto Fischer prize of the TUM (2011) Promotionspreis des Bunds der Freunde der TU München (2009) Neckel has supervised numerous graduate students and has been actively involved in curriculum development and teaching innovation. His book "Bits and Bugs: A Scientific and Historical Review of Software Failures in Computational Science" (2019) represents a significant contribution to understanding software reliability in scientific computing. He has organized minisymposia at major conferences including SIAM CSE and SIAM UQ, and serves on program committees for various computational science conferences. As coordinator of the Ferienakademie and the BGCE, Neckel plays a central role in advanced computational engineering education in Bavaria. His research group develops software for exascale computing and contributes to the Transregional Collaborative Research Centre 89 on Invasive Computing. Neckel also maintains international collaborations, with research stays at institutions including the Australian National University and Tokyo Institute of Technology.
Patrick Dallasega is an Associate Professor in the Department of Industrial Plants at the Faculty of Science and Technology of the Free University of Bolzano (Italy). He holds a PhD from the University of Stuttgart and has been a Visiting Scholar at Chiang Mai University (Thailand) and Worcester Polytechnic Institute (USA). His expertise spans supply chain management, Industry 4.0 integration in SMEs, lean construction methodologies, and sustainable production planning in ETO/MTO environments. He teaches Project Management and Industrial Plants courses in Industrial Mechanical Engineering programs. His research focuses on digital transformation in manufacturing, including smart mobile factories, augmented reality applications for training, and synchronization of production and on-site assembly processes. Collaborative projects like the AR-enhanced industrial training initiative with Memc aim to reduce errors and costs in complex industrial setups. His work emphasizes human-centered technology integration, sustainability, and real-time data utilization for adaptive production strategies. Education Bachelor/Master: Free University of Bolzano (Italy) MSc: Polytechnic University of Turin (Italy) PhD: University of Stuttgart (Germany) Research Interests Professor Dallasega’s research explores the intersection of Industry 4.0 technologies with lean manufacturing principles, particularly in complex Engineer-to-Order (ETO) and Make-to-Order (MTO) sectors. He investigates how digital twin frameworks, augmented reality (AR), and real-time data analytics can enhance supply chain resilience, reduce operational losses, and improve workforce training efficiency. His work also addresses sustainability challenges in mobile and distributed manufacturing systems, emphasizing eco-friendly logistics and smart factory design. Key Projects Recent collaborations include: Development of an AR-based training module to boost procedural knowledge retention in machinery setups Comparative studies on Industry 4.0 adoption in SMEs across Europe and Asia Framework for digital twin-driven quality control in precast manufacturing Grants & Advising No specific grants or student advisees are listed in the provided data. His focus remains on collaborative industry projects and institutional teaching responsibilities. Labs & Teams Involved in cross-disciplinary teams at the Free University of Bolzano, particularly in the NOI Techpark innovation hub. Leads initiatives on smart mobile factories and human-centered robotics applications in manufacturing environments.
Yannis Kevrekidis is a Professor at Princeton University with a distinguished career in computational mathematics and chemical engineering. He is currently a Hans Fischer Senior Fellow at the Technical University of Munich (TUM-IAS) and has held visiting positions at institutions like the Zuse Institute Berlin and Caltech. Education : National Technical University of Athens (Chemical Engineering) University of Minnesota (PhD in dynamical systems) Research Interests : Equation-Free and Variable-Free Modeling Complex Systems Dynamics Multiscale Computation Integration of Machine Learning with Scientific Computing Pattern Formation & Instability Analysis Key Article Trends : Advanced data-driven modeling of dynamical systems Manifold learning for reaction coordinates Projective integration methods Coarse-grained modeling across disciplines Applications in epidemiology, neuroscience, and fluid dynamics Scientific Awards : Guggenheim Fellowship Humboldt Research Award Computing in Chemical Engineering Award (AIChE) Bodossaki Academic Award Allan P. Colburn Award Collaborations : Extensive international collaborations with institutions in Germany, Austria, and the UK Key role in the Complex Systems Modeling and Computation focus group at TUM-IAS
Dr. Stephan Rave is a Researcher in the Institute for Analysis and Numerics at the University of Münster. He is affiliated with the Applied Mathematics Münster cluster and serves as an Investigator in Mathematics Münster. His work focuses on numerical analysis, scientific computing, and machine learning, with a strong emphasis on model reduction techniques for complex systems. Education : PhD in Mathematics (2012), University of Münster, thesis on finitely summable K-homology. Master's and Bachelor's degrees in Mathematics from the University of Münster. Research Interests : Dr. Rave specializes in model order reduction (MOR) methods, including reduced basis techniques, localized orthogonal decomposition (LOD), and nonlinear approximation strategies. His work addresses challenges in multiscale modeling, domain decomposition, and parametrized partial differential equations. He also develops open-source software tools like pyMOR for MOR and contributes to initiatives like the MaRDI (Mathematical Research Data Initiative) to enhance interoperability in scientific computing. Projects : Key initiatives include the MaRDI project (2021–2026), EXC 2044 Cluster of Excellence (Geometry-based modeling), and MULTIBAT (lithium-ion battery simulation). His research bridges theoretical developments with practical applications in battery modeling, electrochemistry, and computational fluid dynamics. Grants & Awards : Funded by DFG, the German Federal Ministry of Research, and internal university grants, his work addresses strategic areas like sustainable research software and energy storage systems. He leads projects on distributed model reduction and communication-avoiding algorithms. Teaching : Dr. Rave teaches advanced numerical methods courses, including Model Order Reduction, Numerical Methods for PDEs, and Python-based computational labs. He co-organizes seminars and workshops on MOR and scientific software engineering.
Kevin Clarno is a tenured Associate Professor in the Department of Nuclear and Radiation Engineering at the University of Texas at Austin, holding the Charlotte Maer Patton Centennial Fellowship in Engineering. His research focuses on computational nuclear energy, multiphysics reactor simulation, and high-performance computing (HPC). Previously, he spent 15 years at Oak Ridge National Laboratory (ORNL), where he led major initiatives such as the Consortium for Advanced Simulation of Light Water Reactors (CASL) and contributed to the development of software tools like SCALE, CTF, and VERA. Education and Career: Assistant Professor at University of Tennessee-Knoxville (2010–2016) Senior Research Scientist at ORNL (2006–2021) Research Interests: Multiphysics coupling methods for reactor simulation Multiscale neutronics and thermal-hydraulics modeling Advanced reactor design (e.g., molten salt reactors) HPC-driven software integration for nuclear analysis Uncertainty quantification in coupled simulations Grants and Projects: Lead of CASL’s Physics Integration Focus Area Development of the Advanced Multi-Physics (AMP) fuel code ORNL-led strategic research projects in reactor simulation Labs and Tools: VERA: Virtual Environment for Reactor Applications CTF: Thermal-hydraulic solver for PWR analysis MPACT: Neutronics simulation tool within SCALE
Dr. Lateef Akanji is a Senior Lecturer in the Department of Petroleum Engineering at the School of Engineering, University of Aberdeen, where he has been contributing since 2014. He previously served as Lecturer and Head of the Petroleum Technology Research Group at the University of Salford, Assistant Professor at King Saud University, and Visiting Lecturer at the University of Leoben. His academic journey includes a PhD from Imperial College London and degrees from the University of Ibadan. University: University of Aberdeen School: School of Engineering Position: Senior Lecturer, Petroleum Engineering Email: l.akanji@abdn.ac.uk Education: PhD, Petroleum Engineering, Imperial College London M.Sc., Petroleum Engineering, University of Ibadan B.Sc. (Honours), Petroleum Engineering, University of Ibadan DIC (Diploma of Imperial College) Research Interests: Dr. Akanji's research centers on multiphase flow in porous and permeable media, with applications in enhanced oil recovery (EOR) in clastic, carbonate, and unconventional shale reservoirs. His work integrates theoretical, experimental, and computational fluid dynamics, utilizing platforms like Python, C++, and Fortran. He is pioneering the application of artificial intelligence in petroleum engineering, particularly in EOR screening and production optimization. His research includes pore-scale modeling, gas-lift systems, and nuclear reactor flow dynamics. Publication Trends: His recent publications (2025–2021) reflect a strong focus on fluid displacement in porous media, shale reservoir characterization, AI applications in energy, and nuclear safety. Notable themes include computational modeling of multiphase flow, biosurfactant EOR, and advanced numerical methods for reservoir simulation. Scientific Awards and Honors: Fellow of the Higher Education Academy (FHEA) Chartered Engineer (CEng) Chartered Petroleum Engineer European Engineer (Eur Ing) Member of the Energy Institute (MEI) Advising and Grants: Dr. Akanji supervises numerous PhD students in areas such as AI-based production optimization, permeability upscaling, and biosurfactant EOR. He leads research funded by PTDF, TETFUND, Sonangol, and Elphinstone, focusing on high-pressure high-temperature flow loops, gas-lift pilot rigs, and neuro-fuzzy screening systems. His collaborative projects involve institutions in the UK, Austria, and Australia. Laboratories and Research Platforms: He contributes to the development of the Complex System Modelling Platform (CSMP++), a C++-based API for simulating multi-physics flow in porous systems, co-developed with ETH Zurich and Montanuniversität Leoben. He also leads a technology innovation platform for EOR, including experimental rigs for biosurfactant screening and gas-lift stability testing.
Alan Kaptanoglu is a Professor leading the Plasma Physics Group at New York University's Courant Institute. His research focuses on the intersection of applied mathematics, scientific machine learning, and nuclear fusion. He develops theoretical and computational tools for plasma modeling, control, and optimization in complex dynamical systems. Key areas include stellarator design optimization, machine learning-driven MHD simulations, and reactor-scale plasma confinement solutions. His work advances fusion energy research through innovations in magnetic field shaping, coil optimization, and data-driven modeling of plasma behavior. Research interests span plasma turbulence analysis, magnetohydrodynamics, and the application of physics-informed neural networks (PINNs). He explores how magnetic geometry influences plasma stability and turbulence using machine learning techniques. His team addresses challenges in fusion reactor design, such as minimizing Lorentz forces in electromagnetic coils and optimizing permanent magnet configurations for stellarators. Recent work emphasizes data-driven methods for discovering interpretable models in fluids and plasmas, including reduced-order modeling and sparse regression approaches. He collaborates on projects like the DIII-D tokamak and ITER, advancing fusion energy solutions through interdisciplinary computational and experimental efforts. His group's contributions bridge plasma physics with advanced mathematics and machine learning to tackle grand challenges in energy and astrophysical systems. Advising and grants are not explicitly detailed in the provided texts, but his leadership role suggests active mentorship in graduate research. The NYU Plasma Physics Group serves as a hub for cutting-edge research, hosting internships, summer schools, and collaborations with industry/academic partners.
Eduardo Gildin is a Professor of Petroleum Engineering and Associate Department Head for Graduate Studies at Texas A&M University's College of Engineering. He holds the L.F. Peterson '36 Professorship and directs the university's graduate studies in petroleum engineering. His research focuses on reservoir modeling, control optimization, model reduction techniques, and CO2 sequestration. Gildin has pioneered data-driven approaches for reservoir simulation, integrating machine learning and physics-based models to enhance efficiency and accuracy. Education: Ph.D. in Aerospace Engineering, University of Texas at Austin (2006) M.S. in Mechanical Engineering, University of São Paulo, Brazil (1998) B.S. in Mechanical Engineering, Faculdade de Engenharia Industrial, Brazil (1995) Research Interests: Model reduction of large-scale dynamical systems Control and optimization of reservoir operations CO2 storage and geological carbon sequestration Machine learning applications in reservoir engineering and drilling automation Geomechanics and compaction damage evaluation Key Awards: 2020: William O. and Montine P. Head Memorial Research Award 2017-2018: Dean of Engineering Excellence Award 2013-2019: Energi Simulation Chair in Robust Reduced Complexity Modeling 2021: Distinguished Membership in Society of Petroleum Engineers Grants and Advising: Gildin has secured major funding for projects on reservoir simulation, drilling automation, and CO2 storage. He advises graduate students on topics such as surrogate modeling and reinforcement learning applications in petroleum systems. His lab collaborates with industry partners to translate research into practical tools for reservoir management and subsurface operations. Labs and Teams: He leads the Reservoir Simulation and Control Lab, focusing on advanced computational methods for reservoir optimization. His team develops open-source drilling models and collaborates globally on projects like the DREAMS (Drilling and Extraction Automated System) initiative.
Johann Guilleminot is an Associate Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University. He joined Duke in 2017 after a Maître de Conférences position at Université Paris-Est. His research bridges computational mechanics, materials science, and uncertainty quantification, with applications in additive manufacturing, biomedical implants, and naval systems. Education: M.S. in Theoretical Mechanics, Lille University of Science and Technology (2005) Ph.D. in Theoretical Mechanics, Lille University of Science and Technology (2008) Habilitation in Mechanics, Université Paris-Est (2014) His work focuses on probabilistic methods for heterogeneous materials, stochastic solvers, and scientific machine learning. Recent projects include data-driven uncertainty quantification in molecular dynamics and additive manufacturing simulations, funded by the Army Research Office, NSF, and national laboratories. Scientific Awards: No specific awards listed in the provided text. Lab & Collaborations: Leads the Guilleminot Lab at Duke, collaborating with Sandia National Laboratories and the U.S. Naval Research Laboratory. Research spans atomistic-to-continuum coupling, inverse problems, and stochastic modeling for predictive simulations.
