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Juliette ChabassierView profile

Researcher

• Acoustics
• Mathematical Modeling
• Numerical Analysis
• +3 more

Juliette Chabassier is a Researcher at the MAGIQUE-3D project team within Inria Bordeaux Sud-Ouest and affiliated with Université de Pau et des Pays de l'Adour . She holds a Ph.D. in Applied Mathematics from École Polytechnique (2012), advised by Patrick Joly and Antoine Chaigne. Roles: Junior Research Scientist (since 2013), Post-Doc at University of Pau (2012-2013) Research Focus: Acoustics of musical instruments, wave propagation modeling, numerical simulation of piano and wind instruments, energy-preserving discretization schemes, and viscothermal effects in ducts. Her work emphasizes physical modeling and computational acoustics , particularly in piano acoustics, wind instrument design, and heritage instrument digitization. She develops tools like OpenWind for simulating instrument responses and collaborates on projects involving piano soundboard analysis and historical instrument reconstruction. Her recent articles explore topics such as: Simulation of brass instruments using finite element models Thermoviscous effects in conical tubes Energy-conserving discretization for nonlinear wave equations Digital twins for heritage instruments like Besson trumpets She advises doctoral students including Elvira Shishenina and Izar Azpiroz , focusing on applied mathematics and acoustics. Her contributions include advanced numerical methods for acoustic-elastic coupling and boundary layer phenomena in exponential media. Current projects involve homogenization techniques for porous materials, real-time instrument simulation tools, and interdisciplinary work on acoustics education through open-source software.

Olivier ChadebecView profile

Research Professor

• Computational Electromagnetics
• Electrical Energy Conversion
• Numerical Modeling
• +6 more

Olivier Chadebec is a CNRS Research Director at G2Elab, the power electrical engineering research department of Université Grenoble Alpes in France. He leads the 'Models, Methods and Methodologies Applied to Electrical Engineering' research team (MAGE group) and the ERT-CMF (Low Magnetic Fields Technological Research Group) at G2Elab. He was involved in creating the International Laboratory 'James Clerk Maxwell' in collaboration with the University of Lyon and Brazilian universities. Chadebec received his engineer and Ph.D. degrees in Electrical Engineering from the Grenoble Institute of Technology in 1997 and 2001. After a post-doctorate with Schneider Electric, he joined CNRS in 2003 as a Research Associate. He received his 'Habilitation à Diriger les Recherches' in 2011 and became a Research Director in 2015. He also spent a year in 2012 as a research associate at the Federal University of Santa Catarina in Brazil. His research focuses on computational electromagnetics applied to electrical energy conversion, developing numerical models, algorithms, and simulation tools for electromagnetic device analysis. His key research areas include finite element methods, integral methods, inverse problems, and low magnetic field metrology. He actively contributes to the development of the MIPSE platform commercialized by Altair Engineering via Flux software. His recent publications (2023-2025) show a strong focus on advanced computational methods for electromagnetic problems, including multiscale modeling, tensor compression techniques, FEM-BEM coupling for magnetoelectric effects, and optimization algorithms for electrical machine design and fuel cell diagnostics. His work demonstrates a consistent progression toward more efficient computational approaches for complex electromagnetic problems. Chadebec has supervised over 30 PhD students since 2006, with thesis topics spanning computational electromagnetics, inverse problems, fuel cell diagnostics, and submarine magnetic signature analysis. His research has significant applications in electrical machine design, fuel cell technology, submarine degaussing, and electromagnetic compatibility. He leads the MAGE research team and the ERT-CMF (Low Magnetic Fields Technological Research Group) at G2Elab, and has been instrumental in developing the MIPSE simulation platform used in industry through collaboration with Altair Engineering.

Jo KennedyView profile

Senior Lecturer

• Mathematics Education
• Realistic Mathematics Education (RME)
• Teacher Professional Development
• +5 more

Jo Kennedy is a Senior Lecturer in Mathematics Education at Manchester Metropolitan University's School of Education. She works part-time and has been at the university since 2009, having completed her Master's qualification during that period. Kennedy is part of the Realistic Mathematics Education (RME) team for Key Stage 3 professional development and serves as a tutor for the PGCE Secondary Mathematics, BSc (Hons) Secondary Mathematics with QTS, and Mathematics Enhancement courses. Specializes in secondary mathematics teacher development NCETM Work Group Lead for professional development programs Strategic Board Member for Turing NW Mathematics Hub UK representative for the International Mathematical Modelling Challenge (IMMC) Her research interests span both Mathematics Education and Coastal Ecology . While her educational work focuses on pedagogical strategies for secondary educators, her publication record reveals significant contributions to understanding mangrove ecology, genetic diversity, and climate change impacts on coastal ecosystems. This interdisciplinary background informs her approach to mathematics curriculum design and teacher training. The articles in her portfolio indicate trends in mangrove range dynamics, climate adaptation, and genetic studies, with keywords spanning environmental science, evolutionary biology, and coastal ecosystem management. These publications demonstrate expertise in analyzing ecological responses to extreme weather events and climate shifts across multiple geographical regions. As a part-time faculty member, Kennedy maintains active involvement in professional development programs while balancing research contributions across disciplines. She can be contacted at j.kennedy@mmu.ac.uk or 0161 247 2296.

• Food Science and Technology
• 3D Food Printing
• Food Biotechnology
• +4 more

Sorour Barekat is a Research Fellow in the Department of Food Science at the University of Arkansas. She holds affiliations with the Director Experiment Station (DREX) and the Food Science Center (FDSC). Her research focuses on advancing food technology through innovative processes such as 3D food printing for bioactive compound encapsulation, protein-based material design, and sustainable utilization of agricultural byproducts. Dr. Barekat also investigates meat tenderization via non-thermal technologies like ultrasonic radiation and enzymatic treatments. Her work bridges food chemistry, biotechnology, and engineering to address functional food development, ingredient optimization, and process innovation. Her research portfolio includes optimizing hydrocolloid systems for gluten-free products, developing extraction methods for natural antioxidants from walnut green husks, and simulating essential oil standardization for industrial applications. She has explored rheological properties of gums and proteins under various processing conditions, contributing to food texture and stability solutions. Dr. Barekat’s interdisciplinary approach integrates analytical techniques with process engineering to enhance food quality and safety. While no academic awards are listed, her extensive publication record (2017–2025) highlights sustained contributions to food science innovation. Her advising and grants remain unspecified in available records. Dr. Barekat’s work is centered in the Food Science Department, contributing to research initiatives at the University of Arkansas.

Nenad N. CvetkovicView profile

Professor

• Theoretical Electrical Engineering
• Electromagnetics
• Bioelectromagnetics
• +3 more

Nenad N. Cvetkovic is a Full Professor at the Faculty of Electronics, University of Niš, specializing in Theoretical Electrical Engineering. He earned his PhD (2009), MSc (2002), and BSc (1995) from the same institution. His research focuses on electromagnetic field interactions with biological tissues, grounding system design, and numerical modeling of geoelectrical phenomena. Key areas include mobile phone radiation effects on human tissues, SAR distribution in anatomical models, and innovative approaches to multi-layered soil grounding analysis. He has contributed over 19 peer-reviewed articles in high-impact journals, addressing topics like dental implant EM interactions, orthodontic brace SAR profiles, and optimizations for grounding systems. His work bridges theoretical rigor with practical applications in electrical safety, biomedical engineering, and environmental geoelectrics. Prof. Cvetkovic is actively engaged in national/international research projects and speaks English and German fluently. His laboratory activities include finite element method (FEM) simulations, experimental validation of grounding models, and collaborations on standards for non-ionizing radiation exposure assessment.

• Time Series Analysis
• Blockchain Technologies
• Business Process Management
• +8 more

Roman Vaculín is a Researcher at IBM Research, focusing on interdisciplinary domains where artificial intelligence, blockchain technologies, and data-centric workflows converge. His work spans automated machine learning, time series analysis, and secure computation via cryptographic methods like homomorphic encryption. Affiliation: IBM Research Key research areas: Time Series Analysis, Blockchain, AI Explainability, Business Process Management Across his publications, Vaculín explores: Time Series Modeling: Developing robust frameworks like TsSHAP and end-to-end architectures for forecasting and imputation. Blockchain Applications: Designing trusted AI systems, secure multi-party computation, and verifiable simulations. Automated Machine Learning: Creating toolkits for industrial AI explainability and automation. Privacy-preserving Techniques: Optimizing encrypted inference and secure decision tree protocols. His methodology often integrates formal verification with practical implementations, emphasizing efficiency and interpretability in complex systems. While no formal awards or students are documented in the provided data, his collaborative publications with institutions like IBM Research and academic partners highlight his role in advancing applied AI research.

• Nonlinear Analysis
• Partial Differential Equations
• Mathematical Physics
• +3 more

Yanyan Li is a Distinguished Professor of Mathematics at Rutgers, The State University of New Jersey, affiliated with the Department of Mathematics. His research focuses on nonlinear analysis, partial differential equations (PDEs), and their applications to fluid dynamics, geometric analysis, and conformal geometry. He has contributed extensively to topics including Navier-Stokes equations, Monge-Ampère equations, and the Nirenberg problem. His work bridges theoretical mathematics and applied problems, such as conductivity in composite materials and microRNA's role in glioma cell behavior. Recent research emphasizes singularity analysis in PDEs and the development of comparison principles for degenerate elliptic equations. Notable contributions include studies on compactness theorems for conformal metrics and the classification of axisymmetric solutions to Navier-Stokes equations. Prof. Li’s articles frequently address boundary value problems, geometric PDEs, and the interplay between nonlinear operators and physical phenomena. His methodologies span variational principles, viscosity solutions, and geometric techniques, reflecting a synthesis of analytical rigor and interdisciplinary application. While no specific awards are listed here, his tenure as a Distinguished Professor underscores his scholarly impact. He advises students in mathematical analysis and related fields, though explicit student listings are not provided in the source text.

Sophie de BuylView profile

Associate Professor

• Theoretical Physics
• Mathematical Physics
• Biophysics
• +7 more

Sophie de Buyl is an Associate Professor at the Vrije Universiteit Brussel (VUB), actively contributing to interdisciplinary research bridging theoretical physics and biological systems. She is affiliated with the Department of Bio-engineering Sciences and the VUB Data Lab, focusing on mathematical modeling of biological processes, synthetic biology, and biophysics. PhD in Theoretical Physics (2006, Université Libre de Bruxelles) Postdoctoral experience at IHES, UC Santa Barbara, Harvard University Her research spans cosmological singularities, gauge/gravity correspondence, and black hole entropy in her early career, evolving to integrate theoretical physics with experimental biology. Current projects include synthetic gene regulatory circuits, microbial community dynamics, and embryonic development precision. Key publication trends (2025–2019) reflect dual expertise in mathematical physics (Kac-Moody algebras, black holes) and biological systems (microbial ecology, synthetic biology, circadian clocks). Awards highlight early-career recognition by the Belgian Physics Society and Belspo. Supervised FWO PhD projects on dynamic pathway regulation and microbial systems Director of the Interuniversity Institute of Bioinformatics in Brussels since 2020 Her work emphasizes data-driven discovery of general laws in biological systems, with collaborations spanning bioinformatics, microbiology, and computational modeling.

Shukai DuView profile

Assistant Professor

• Numerical Analysis
• Scientific Machine Learning
• Inverse Problems
• +6 more

Shukai Du is an Assistant Professor of Mathematics at Syracuse University , within the College of Arts and Sciences . Previously, he served as a Visiting Assistant Professor at the University of Wisconsin-Madison (2020–2024) and earned his Ph.D. in Applied Mathematics from the University of Delaware (2020). His research focuses on numerical analysis , scientific machine learning , and inverse problems , with expertise in discontinuous Galerkin methods , radiative transfer , and adaptive mesh refinement . He is also affiliated with his research website . Education: Ph.D., Applied Mathematics, University of Delaware (2020) M.S., Computational Mathematics, Wuhan University (2015) B.S., Pure Mathematics, Wuhan University (2012) Research Highlights: Dr. Du’s work emphasizes accelerating numerical methods via machine learning (element learning), developing goal-oriented hp-adaptive mesh refinement for inverse radiative transfer, and creating symplectic Hamiltonian finite element methods for electromagnetics. His methods prioritize accuracy, computational efficiency, and preservation of physical invariants. Recent Contributions: Recent publications address operator learning for finite element acceleration, adaptive-mesh inversion for inverse problems, and energy-conserving discretizations for Hamiltonian systems. His work bridges classical numerical methods and modern data-driven techniques. Awards: Wenbo Li Prize (2020, University of Delaware) Grants: "Breaking the 1D Barrier in Radiative Transfer: Fast, Low-Memory Numerical Methods for Enabling Inverse Problems and Machine Learning Emulators" (NSF, 2023–2026) "Convective Processes in the Tropics Across Scales" (NSF, 2024–2026) Labs/Teams: His research collaborates with computational scientists and applied mathematicians, focusing on interdisciplinary challenges in computational physics and data-driven modeling.

• General Relativity
• Cosmology
• Mathematical Physics
• +3 more

Hans Ringström is a Professor of Mathematics at KTH Royal Institute of Technology, affiliated with the department of Analysis, Dynamics, Geometry, PDE and Number Theory. His research focuses on mathematical aspects of Einstein's general theory of relativity, particularly cosmological models and the BKL conjecture. He investigates the robustness of the standard cosmological model under perturbations, exploring singularity formation and spacetime behavior near the Big Bang. Ringström's work emphasizes analytical methods for partial differential equations and dynamical systems in general relativity. He has contributed to understanding cosmic censorship, Gowdy spacetime dynamics, and the stability of cosmological solutions. His research bridges theoretical physics and pure mathematics, addressing foundational questions about the universe's structure and evolution. Prominent themes include the analysis of Einstein's equations under symmetry assumptions (e.g., Bianchi and Gowdy models), asymptotic behavior of spacetimes, and the interplay between curvature and cosmic expansion. His findings have advanced our understanding of gravitational singularities and the mathematical foundations of cosmology. Ringström teaches courses such as Foundations of Analysis, Analytical and Numerical Methods for Differential Equations, and Differential Equations I at KTH. He has authored influential books, including *The Cauchy Problem in General Relativity* (2009) and *On the Topology and Future Stability of the Universe* (2013).

• Gene Editing
• CRISPR-Cas Systems
• Induced Pluripotent Stem Cells
• +5 more

Pranam Chatterjee is an Assistant Professor at the RNA Therapeutics Institute, UMass Chan Medical School, affiliated with the T.H. Chan School of Medicine. He holds a PhD in Media Arts and Sciences from MIT, with prior degrees in Computer Science and Molecular Biology. His research focuses on CRISPR-Cas systems, gene editing tools, and stem cell differentiation, particularly in reproductive medicine contexts. Chatterjee’s work includes developing novel genome editing technologies like PTM-Mamba and engineered Cas9 variants, as well as advancing methods for human oogonia-like cell specification and ovarian support cell co-cultures. His publications span topics in molecular biology, biotechnology, and computational biology, with contributions to improving CRISPR specificity, SARS-CoV-2 detection, and stem cell culture techniques. Chatterjee collaborates widely, including with institutions like Harvard and MIT, and his research has been highlighted in multiple news outlets and patents. His lab at the RNA Therapeutics Institute emphasizes interdisciplinary approaches, combining computational modeling with experimental biology to address challenges in genome editing, regenerative medicine, and disease modeling. While no formal awards are listed, his work has garnered significant citation impact, especially in CRISPR-Cas9 engineering and stem cell differentiation studies.

• Differential Geometry
• Mathematical Physics
• Symplectic Geometry
• +5 more

Prof. Vicente Cortés is a faculty member in the Department of Mathematics at the University of Hamburg, affiliated with the MIN Faculty and the Center for Mathematical Physics. He holds the academic rank of Professor and specializes in differential geometry, mathematical physics, and geometric analysis. His research focuses on topics such as quaternionic Kähler manifolds, Einstein metrics, generalized complex structures, and geometric flows. He is involved in major research initiatives including the Cluster of Excellence Quantum Universe and Collaborative Research Centre (SFB) 1624. His work bridges geometry with theoretical physics, particularly in areas related to supersymmetry, supergravity, and string theory. Notable contributions include studies on homogeneous spaces, conformal transformations, and geometric structures on Lie groups. He actively organizes conferences and workshops, such as the Northern German Differential Geometry Days and events under the SFB 1624. Prof. Cortés teaches advanced courses in differential geometry and mathematical physics, including 'Differential Geometry' and 'Generalized Geometry' at the master's level. His research is supported by grants from the Deutsche Forschungsgemeinschaft (DFG) and other institutions. He collaborates internationally, with co-authors from institutions like the University of Edinburgh, DESY, and KU Leuven.

Ahmet ÇETİNView profile

Professor

• Organic Chemistry
• Heterocyclic Compounds Synthesis
• Antioxidant Activity Research
• +3 more

Prof. Dr. Ahmet ÇETİN is a Professor of Chemistry at Bingöl University's Faculty of Science and Letters, Department of Chemistry. He holds a PhD from Fırat University (2005) and has been affiliated with Bingöl University since 2009, advancing to full professorship in 2015. His academic journey includes roles as a research assistant at Fırat University (1999–2005) and associate professor at Bingöl University (2009–2015). He has led over 12 research projects, focusing on organic synthesis, heterocyclic compounds, and bioactive materials. His work includes collaborations on antioxidant agents, anticancer molecules, and sensor materials. Research Interests: - Synthesis of heterocyclic compounds (triazoles, thiadiazoles) - Bioactive molecules and drug design - Antioxidant and anticancer activity studies - Material characterization and sensor development - Microwave-assisted organic synthesis Notable Publications: - Over 40 peer-reviewed articles in journals like Journal of Molecular Structure , ChemistrySelect , and Bioorganic & Medicinal Chemistry Letters . - Focus areas include cinnamic acid conjugates, phosphinic acid derivatives, and phthalocyanine sensors. Professional Contributions: - Supervised 11 PhD and 8 MSc students. - Served as a reviewer for journals like Phosphorus, Sulfur, and Silicon and Applied Organometallic Chemistry . - Conducted research on Bingöl's endemic plants and their pharmacological potential.

Mia ViermannView profile

Researcher

• Inclusion-oriented teacher education
• Difference practices in educational contexts
• Mathematics learning in inclusive settings
• +3 more

Dr. Mia Viermann is a Researcher at the University of Hamburg's Faculty of Education, Department of Didactics of Social Sciences and Mathematics-Natural Science. Her work focuses on inclusion-oriented teacher education, difference practices in educational contexts, and mathematics learning in inclusive settings. She holds a M. Ed. and B.A. in Special Education from Leibniz University Hannover, with a focus on mathematics and child development. Educational Background: B.A. Sonderpädagogik mit Zweitfach Mathematik (2011-2014), Leibniz Universität Hannover M. Ed. Lehramt für Sonderpädagogik (2014-2016), Leibniz Universität Hannover PhD Defense 2022: 'Konjunktives Erfahrungswissen Lehramtsstudierender zu Inklusion' (Leibniz Universität Hannover) Research Interests: Explores intersections of inclusion, digitalization, and mathematics education through reconstructive social research methodologies. Current projects examine teacher orientations toward disability, inclusive pedagogy in digital environments, and community-oriented educational frameworks. Awards: 2023 DGfE Poster Prize (3rd place) 2016 Excellence in Teaching Award (Leibniz University Hannover) Professional Activities: Co-edited the journal 'Gemeinsam Leben' (2021), presented at international conferences including CERME12, and actively contributes to interdisciplinary research networks like the Erfurter Nachwuchsnetzwerk Dokumentarische Methode. Labs/Teams: Collaborates with the Graduiertenschule Martha-Muchow-Bibliothek and participates in university-wide Wissenstransfer initiatives focusing on educational policy and practice.

Ali OzelView profile

Assistant Professor

• Fluid Mechanics
• Multi-scale Modeling
• Gas-Particle Flows
• +5 more

Dr. Ali Ozel is an Assistant Professor at Heriot-Watt University's School of Engineering & Physical Sciences, specializing in fluid-particle flows and multi-scale modeling. His work bridges academic institutions and industry collaborations, including INEOS, Princeton University, and Toulouse's Fluid Mechanics Institute. PhD in Fluid Mechanics (Toulouse, 2011) B.Sc. & M.Sc. in Aerospace Engineering (Istanbul Technical University) Research Master in Environmental & Applied Fluid Dynamics (von Karman Institute) His research focuses on: Multi-scale modeling of fluid-particle flows (micro/meso/macro) Solvent-based carbon capture technologies (Volume of Fluid simulations) Rheology of dense granular suspensions (shear thickening/thinning) Development of xFlow - Complex Flow Technologies for decarbonization Hybrid digital twins combining physics and machine learning Using data-driven approaches and advanced computational tools, Dr. Ozel formulates constitutive models for industrial applications in energy, pharmaceuticals, and manufacturing. His recent publications emphasize neural networks for fluid dynamics, particle stress modeling, and decarbonization strategies. Scientific Awards: Marie Curie Fellows Association Grant Dr. Ozel actively supervises PhD students and collaborates with international institutions on computational modeling projects. His xFlow initiative investigates clean energy integration and device optimization through rigorous multi-scale methodologies.