Dr. Deniz Bezgin is a Researcher at the Department of Aerodynamics and Fluid Mechanics of the Technische Universität München (TUM) . Her work focuses on computational fluid dynamics (CFD), machine learning integration in numerical methods, and high-order differentiable solvers for compressible flows. Research specialties include shock-capturing methods, multi-phase flow modeling, and data-driven shape optimization. Developed JAX-Fluids, a fully-differentiable framework for compressible two-phase flows. Key contributions to ENO/WENO schemes and thermodynamically consistent interface models. Current projects explore machine-learned discretizations and GPU-based high-performance computing. Her recent publications address differentiable simulations, data assimilation, and turbulence modeling. She has not received any explicitly listed scientific awards.
Najmeh Abiri is a researcher with multiple affiliations at Lund University, holding positions as a Visiting Research Fellow at the Centre for Environmental and Climate Science (CEC), a Visiting Research Fellow at Computational Science for Health and Environment, a Postdoc at the Department of Statistics, and a Researcher at eSSENCE: The e-Science Collaboration. Her research focuses on the intersection of statistics and machine learning with practical applications. Key areas include: Variational inference Bayesian inference Deep learning architectures Missing data imputation techniques Computational biology applications Abiri's publication record shows a clear trajectory toward increasingly sophisticated applications of machine learning in scientific domains. Her recent work demonstrates expertise in diffusion models for time series analysis, AI-driven disease outbreak prediction, and quantum mechanical applications of deep learning. Her 2019 paper on denoising autoencoders for missing data problems has been particularly influential with 62 citation indexes. She has received notable attention for her interdisciplinary work, with publications featured in high-impact journals like The Lancet Regional Health - Europe and covered by multiple news outlets. Her research on West Nile virus prediction has garnered policy citations, indicating real-world impact. Abiri actively participates in the academic community through workshops and seminars, particularly those focused on AI technologies and their applications in scientific research. Her collaborative work spans multiple institutions and research domains, reflecting the interdisciplinary nature of her expertise.
Jasper Marijn Everink is a Postdoc researcher at the Department of Applied Mathematics and Computer Science, Technical University of Denmark, specializing in computational uncertainty quantification and inverse problems. His work bridges statistical learning, regularization techniques, and imaging applications. Research Interests: Everink focuses on Bayesian inference, sparse modeling, and spatial regularization methods. His research develops hierarchical frameworks for uncertainty quantification, leverages level-set techniques in electrical impedance tomography, and explores implicit prior modeling for inverse problems. Publication Trends: Recent work emphasizes conformal prediction for imaging (2025), level-set regularization in tomography (2024), and sparsity-promoting priors (2024). Earlier contributions include projected density Bayesian methods (2023) and regularized Gaussian distributions for sparse inference (2023). Collaborations: Collaborates across mathematics, computer science, and engineering disciplines, with applications in imaging and computational physics.
Roberto Morales is a postdoctoral researcher affiliated with the University of Chile , Department of Engineering Sciences. His academic journey includes a PhD in Engineering Sciences (Mathematical Modelling) from the University of Chile (2019) and a Bachelor in Mathematics from the University of Santiago de Chile (2011). Research Interests Control and stabilization of PDEs Inverse problems Dynamic boundary conditions Mathematical modelling His work focuses on controllability of partial differential equations (PDEs) with dynamic boundary conditions, including Schrödinger and parabolic equations. Recent studies involve discrete Carleman estimates, multi-objective optimization for decentralized learning, and numerical methods for PDE control. Publications His publications span top journals like SIAM Journal on Control and Optimization and Journal of Differential Equations , with a trend toward controllability analysis and inverse problems in PDEs. Key collaborations include U. Biccari and J. Dardé.
Matt Tranter serves as Principal Lecturer in the School of Science & Technology at Nottingham Trent University, specializing in Physics and Mathematics. He teaches core modules including Calculus, Partial Differential Equations, and Professional Development while leading postgraduate course management since 2024. He earned his PhD from Loughborough University in 2018, focusing on nonlinear wave propagation in layered waveguides, followed by postdoctoral work on droplet dynamics using diffuse-interface models applied to solar panel efficiency. His research expertise spans two interconnected domains: (1) Applied mathematics involving nonlinear wave propagation, soliton dynamics in elastic structures, fluid-structure interaction, and numerical methods for partial differential equations; (2) Mathematics education with emphasis on Learning for Mastery assessments, post-pandemic attendance-attainment relationships, and work placement impacts on student progression. His work on delamination detection using Ostrovsky wave packets demonstrates practical engineering applications. Publication analysis reveals consistent contributions to wave theory (2015-2025) with strategic expansion into educational research since 2021, maintaining rigorous mathematical modeling approaches across both domains while addressing real-world engineering and pedagogical challenges. Dr Tranter has no documented scientific awards in the provided information. He actively mentors students through undergraduate placements (2020-2024 Mathematics Undergraduate Research Studentships coordinator) and currently supervises MRes/PhD candidates in mathematical sciences. His placement management role involved summer internships and professional development, while his current postgraduate leadership shapes advanced curriculum design. His research operates within NTU's Imaging, Materials and Engineering Research Centre and Computation and Simulation group, facilitating cross-disciplinary collaboration on mathematical modeling applications from material science to educational innovation.
Youssef Diouane is an Associate Professor in the Department of Mathematical and Industrial Engineering at Polytechnique Montréal, Canada. Previously, he was a professor in the Department of Complex Systems and Engineering (DISC) at ISAE-SUPAERO in Toulouse, France. He is a member of the Research Group in Decision Analysis (GERAD) and serves as an associate editor for the journal "Computational Optimization and Applications" as well as a guest co-editor for a special issue of "Mathematical Programming" related to the 25th International Symposium on Mathematical Programming. Dr. Diouane's research focuses on numerical optimization and its applications to complex systems, design, and data sciences. His work targets the development of efficient optimization algorithms with optimal guarantees to solve engineering optimization problems. His primary research areas include: Numerical Optimization Surrogate Modeling Data Science and Machine Learning Computational Science and Engineering His recent publication record (57 total publications) demonstrates a strong focus on optimization techniques applicable to aerospace engineering, particularly aircraft design. There's a clear trend toward addressing high-dimensional optimization problems with mixed and categorical variables, often using Bayesian optimization approaches. His work bridges theoretical optimization methods with practical engineering applications, especially in sustainable transport and green aircraft design, reflecting his secondary spheres of excellence in Sustainable Transport and Infrastructures and Industry of the Future and Digital Society. Dr. Diouane has successfully supervised multiple graduate students, including two Master's theses at Polytechnique Montréal completed in 2022 and 2024. His teaching responsibilities include courses on scientific computing for engineers, operational research foundations, and derivative-free optimization, reflecting his commitment to both theoretical and applied aspects of optimization.
Yiming Zhou is a researcher at Saarland University of Applied Sciences (htw saar) in Saarbrücken, Germany. Their work spans multiple engineering domains with emphasis on artificial intelligence integration, 3D scene reconstruction, and advanced imaging techniques. Contact: yiming.zhou@htwsaar.de Location: Goebenstraße 40, 66117 Saarbrücken Research Focus Zhou's research explores cutting-edge technologies in: AI applications for non-destructive evaluation Dynamic SLAM systems for robotics Next-generation 3D reconstruction methods Multimodal deception detection frameworks Novel Gaussian splatting techniques Semantic encoding for spatial data Recent Publications Trends Their scholarly output reveals a trajectory toward Real-time spatial mapping solutions Hybrid neural-implicit representations CAD-integrated building documentation AI-enhanced image translation pipelines Signal processing innovations Cross-modal data fusion Laboratory Affiliation Zhou contributes to the faculty laboratories at htw saar, focusing on engineering research through practical implementations and algorithm development.
Abhishake Abhishake is a Postdoctoral Researcher at the Department of Computational Engineering, LUT School of Engineering Sciences, focusing on inverse problems and statistical learning. His research spans mathematics, machine learning, and computational optimization. 2023–present: Postdoctoral Researcher, LUT University, Finland 2021–2022: Postdoctoral Researcher, Technische Universität Berlin, Germany 2018–2021: Postdoctoral Researcher, University of Potsdam, Germany His research interests include inverse problems, regularization methods, and machine learning. He has published extensively on Tikhonov regularization, nonparametric testing, and multi-penalty strategies. His work bridges mathematical theory with applications in pharmacometrics and statistical learning. Key trends in his publications (16 total) include inverse problems in Hilbert scales, regularization techniques for nonlinear statistical learning, and multi-task learning frameworks. Subfields cover oversmoothing penalties, manifold regularization, and convergence analysis.
Sándor Szénási is a Professor at the Department of Informatics within the Faculty of Economics and Informatics at J. Selye University, where he serves as the person responsible for the Applied Informatics study program. With over two decades of academic experience, he has established himself as a leading researcher in parallel programming, GPU programming, and image processing, with recent expansion into machine learning applications. Eötvös Loránd University, Faculty of Science and Informatics (2001-2004): Information technology teacher Budapest Polytechnic, John von Neumann Faculty of Information Technology (1997-2001): B. Engineer in Information Technology Óbuda University (2010-2013): PhD in Applied Informatics Habilitation at Óbuda University (2019): Information Science and Technology Professor inauguration at Óbuda University (2022) Szénási's research spans computational methods with practical applications across multiple domains. His early work focused on parallel and GPU programming for image segmentation and heat transfer simulation. More recently, he has integrated machine learning techniques with traditional computational approaches, particularly in metaheuristic optimization, speech processing, and inverse problem solving. His interdisciplinary research bridges computer science with transportation safety, manufacturing, and medical applications. His recent publications reveal a clear evolution toward hybrid computational approaches that combine machine learning with traditional algorithms. There is a strong emphasis on optimization techniques, particularly metaheuristics enhanced with machine learning components. His work spans diverse application areas including speech emotion recognition, autonomous vehicle control, additive manufacturing, and heat transfer simulation, while maintaining a core focus on computational efficiency and parallel processing. Szénási has been actively involved in multiple EFOP-funded research projects including 'Improvement of higher education institutes for better teaching quality and accessibility,' 'Dynamics and control of autonomous vehicles,' and 'Solving the Inverse Heat Conduction Problem with Machine Learning.' His collaborative work with researchers like Gábor Kertész, Zoltán Vámossy, and Imre Felde demonstrates his commitment to interdisciplinary research.
Simon Bartels is a researcher affiliated with the Department of Computer Science at the University of Copenhagen , contributing to the Machine Learning section. His work spans interdisciplinary applications of artificial intelligence, including quantum computing, healthcare diagnostics, and environmental modeling. Research activities at the department cover both theoretical and applied machine learning, with participation in the SCIENCE AI Centre . Key domains include medical data analysis , remote sensing , sustainability , and biological data modeling . Recent publications highlight contributions to quantum-inspired neural networks , geospatial biodiversity analysis , and energy-aware AI systems . Collaborations include rare disease research (e.g., MOSAIC framework ) and quantum computing optimizations.
Václav Snásel is a Professor at the Department of Informatics, VSB - Technical University of Ostrava, Czech Republic. He holds a PhD from Masaryk University (Brno, Czech Republic). His research focuses on optimization algorithms, machine learning, metaheuristics, data mining, and their applications in engineering and computational intelligence. Key research interests include developing novel metaheuristic algorithms (e.g., Walrus Optimizer, Artificial Protozoa Optimizer), optimization frameworks for engineering problems, and applications in wireless sensor networks, power systems, and medical diagnostics. He also explores computational methods for data analysis, including graph-based techniques and surrogate-assisted evolutionary algorithms. His recent work emphasizes multi-objective optimization, algorithm design for high-dimensional problems, and interdisciplinary applications in agriculture, energy systems, and bioinformatics. Collaborations span institutions globally, with frequent co-authorship on topics like swarm intelligence and evolutionary computation.
Dr Mark Puttock-Brown serves as Senior Lecturer in Mechanical Engineering within the School of Engineering and Informatics at the University of Sussex, where he also holds the position of Associate Dean for the Sussex-Surrey Institute of Technology. As a member of the Thermo-Fluid Mechanics Research Centre, his work bridges fundamental turbomachinery research with practical applications in renewable energy and biomedical devices. His educational background includes: BSc in Theoretical Physics (University of Sussex, 2010) MSc in Advanced Mechanical Engineering (University of Sussex, 2011) PhD in Mechanical Engineering focusing on gas turbine secondary air systems (University of Sussex, 2018) PgCert in Higher Education (University of Sussex, 2020) Puttock-Brown's research centers on experimental and numerical analysis of rotating cavity flows, with particular expertise in buoyancy-driven phenomena within gas turbine engines. His work increasingly integrates AI methodologies like physics-informed neural networks for inverse heat transfer problems while expanding applications to sustainable systems and net-zero technologies. Recent publications demonstrate a clear trajectory from fundamental fluid dynamics studies toward applied research with industrial partners like GE Aviation. His publication portfolio reveals consistent focus on rotating cavity aerodynamics with evolving methodological sophistication - from traditional experimental measurements (2016-2018) to hybrid AI-experimental approaches (2023-2025). Key thematic clusters include buoyancy effects in compressor rotors, thermal wake characterization, and metamaterial applications for acoustic management. Award recognition includes: Fellowship of the Higher Education Academy (2021) He actively supervises graduate projects while securing significant research funding, including a £4M BP grant for cryogenic fluid research (2023-2027) and Innovate UK funding for renewable refrigeration systems. His teaching portfolio spans numerical modeling, computational fluid dynamics, and vehicle technology courses at multiple levels. Current research activities center on the Thermo-Fluid Mechanics Research Centre where he leads projects connecting turbomachinery fundamentals with biomedical device innovation and sustainable energy systems through collaborations with industrial partners including Beko PLC and GE Aviation.
Arthur Filoche is a Research Fellow at the UWA Oceans Institute (School of Earth and Oceans), The University of Western Australia. He holds a PhD in electrical engineering with specialization in applied mathematics and data science, focusing on variational data assimilation and deep learning for numerical weather forecasting. His research bridges physics-based inverse problems and machine learning, particularly in improving ocean wave forecasts in Western Australia under Prof. Jeff Hansen's supervision. He volunteers as a science teacher at Currambine Primary School. Education: French engineering degree + PhD in applied mathematics/data science Research interests include machine learning applications for ocean/atmospheric systems, data assimilation techniques, and developing algorithms for undersampled measurement challenges. Recent work emphasizes sea surface height interpolation from satellite data and spatiotemporal wind forecasting models. His publications span environmental science, computer vision, and meteorology domains. Currently supervising PhD projects applying machine learning to correct spectral ocean wave forecasts. Active in collaborative research across Western Australia and international partnerships. No listed scientific awards but maintains an h-index of 5 with 16 total citations.
Heng Xiao is a Professor of Data-Driven Fluid Dynamics at the University of Stuttgart, affiliated with the Institute of Aerospace Thermodynamics (ITLR) and the Cluster of Excellence EXC 2075 'Data-Integrated Simulation Science' in the Stuttgart Center for Simulation Science (SC SimTech). He previously served as Associate Professor (2020-2022) and Assistant Professor (2013-2020) at Virginia Tech, USA, and was a Postdoctoral Researcher/Lecturer at ETH Zürich (2009-2012). Ph.D., Civil Engineering, Princeton University, 2009 M.S., Scientific Computing, Royal Institute of Technology (KTH), 2005 B.S., Civil Engineering, Zhejiang University, 2003 His research focuses on integrating data science (machine learning, uncertainty quantification, data assimilation) with traditional physical models to advance predictive capabilities in multi-scale fluid systems. Key areas include Data-Driven Turbulence Modeling , Laminar-Turbulent Transition , Subsurface Flows , and Particle-Laden Flows . His work addresses turbulence modeling through neural operators, Bayesian inference, and physics-informed machine learning, with applications in aerospace, ocean engineering, and geosciences. Recent publications highlight trends in Neural Operators for Nonlocal Models , Ensemble Kalman Methods for Turbulence Inference , and Machine Learning for Permeability Prediction . Collaborative projects, such as the DFG-funded development of coupled turbulence and heat-flux models for film cooling, underscore his focus on real-world impact. Fellowship, Center of Turbulence Research Summer Program, Stanford University (2016) Finalist, Undergraduate Research Advisor Award, Virginia Tech (2014) Advisor to doctoral students including Jian-Xun Wang, Rui Sun, Jin-Long Wu, and Carlos Michelén-Ströfer, he leads the 'Data-Driven Fluid Dynamics' group at Stuttgart. The team collaborates with academia and industry, emphasizing high-performance computing and open-source tools like SediFoam for sediment transport simulations.
Arthi Jayaraman is a full professor at the University of Delaware, holding dual appointments in the Departments of Chemical & Biomolecular Engineering and Materials Science and Engineering. She directs an NSF-funded NRT traineeship program on computing and data science for materials innovation. Her research focuses on computational design of soft materials using machine learning, polymer physics, and molecular simulations. Education: Ph.D. in Chemical Engineering from North Carolina State University (2000s), postdoctoral research at University of Illinois-Urbana Champaign in Materials Science and Engineering. Her work integrates AI with experimental techniques to solve challenges in materials characterization and industrial applications. Research interests include ML-driven analysis of materials data, polymer composites design, and bioinspired materials. Key projects involve developing algorithms like CREASE (Computational Reverse-Engineering Analysis for Scattering Experiments) to interpret nanoparticle structures. She has pioneered methods for predicting phase behavior in polymer blends and nanocomposites. Her awards span ACS PMSE Fellowship (2025), APS Fellow (2020), AIChE IMPACT Award (2021), and DOE Early Career Award (2010). Over 100 peer-reviewed publications address topics like polymer solution structures, nanoparticle self-assembly, and collagen-mimetic materials. Leadership includes advising graduate students through the NRT program, collaborating with industry on material design problems, and serving as an editorial board member for ACS journals. Her lab employs multiscale modeling strategies to bridge molecular-scale phenomena with macroscale material properties.
