جست‌وجوی استاد و پژوهشگر

• Numerical Methods
• Scientific Computing
• Cardiac Electrophysiology Modeling
• +۳ مورد دیگر

Ngoc Mai Monica Huynh is a Researcher in the Department of Mathematics at the University of Pavia, specializing in Scientific Computing with a focus on Numerical Methods and Applications. Her work centers on developing advanced numerical techniques for simulating cardiac electrophysiology, particularly through high-performance computing approaches. Her research interests include parallel algorithms, domain decomposition methods (BDDC/FETI-DP), and virtual element discretizations applied to cardiac models like the Bidomain equations. She has contributed to scalable solvers for reaction-diffusion systems and implicit time discretization techniques in heterogeneous media. Recent work emphasizes robust preconditioners for discontinuous Galerkin discretizations, algebraic multigrid solvers, and cellular-resolution simulations of cardiac tissue. Her publications reflect a strong focus on computational efficiency and mathematical rigor in biomedical modeling. Dr. Huynh's research is supported by collaborations within the Department's Scientific Computing group and leverages high-performance computing resources. Her expertise bridges numerical analysis, parallel computing, and biophysical modeling for cardiac electrophysiology applications.

• Nonlinear Physics
• Reaction-Diffusion Systems
• Fluid Dynamics
• +۷ مورد دیگر

Prof. Vicente Perez Muñuzuri is a University Professor at the Department of Particle Physics in the Faculty of Physics at the University of Santiago de Compostela. He holds a PhD from the same institution (1992) with a thesis on reaction-diffusion systems and super-spiral waves. His research focuses on nonlinear physics, fluid dynamics, and interdisciplinary environmental technologies. Key affiliations include the GFNL Nonlinear Physics Group and the Center for Interdisciplinary Research in Environmental Technologies (CRETUS). He has published extensively on topics like wave energy resource assessment using HF radars, spatiotemporal chaos, chemical reaction dynamics, and atmospheric modeling. Notable works include studies on spiral wave behavior in Chua's circuits, noise-enhanced wave propagation, and advective transport impacting plankton blooms. His research integrates experimental, numerical, and theoretical methods across physics, chemistry, and engineering. Vicente has collaborated on projects involving oceanographic modeling, pollutant transport forecasting (e.g., Prestige oil spill), and atmospheric studies using cosmic-ray detectors. His work bridges fundamental physics with applied challenges in renewable energy, environmental monitoring, and biophysical systems.

• Calculus of Variations
• Solid Mechanics
• Partial Differential Equations
• +۳ مورد دیگر

Elisa Davoli is a Full Professor at TU Wien's Institute for Analysis and Scientific Computing, leading the Multiscale Calculus of Variations research group. Her work focuses on materials science, calculus of variations, and partial differential equations, with applications in solid mechanics, plasticity, and magnetoelasticity. She has been awarded the Richard von Mises Prize (2020) and the FWF START Prize (2020). Her research bridges theoretical analysis with practical applications, including homogenization theory, phase transitions, and optimal control in material systems. Key research interests include multiscale modeling, variational methods for material failure, and mathematical aspects of metamaterials. Recent projects address high-contrast materials, nonlocal denoising models, and magnetic skyrmions. Davoli has organized workshops on fracture mechanics, calculus of variations, and imaging challenges. She teaches courses in mathematical analysis, partial differential equations, and elasticity theory, and has mentored students in applied mathematics and mechanics. Grants: FWF START Project (Tunable materials), Elise Richter Grant (Plasticity and magnetoelasticity), GAČR Joint Project (Large Strain Challenges). Events: Organized Bio-PDE Days (2024), 3rd Austrian Calculus of Variations Day (2023), GAMM Microstructure Seminar (2023).

• Mathematical Biology
• Reaction-Diffusion Equations
• Ecological Modeling
• +۳ مورد دیگر

Peter van Heijster is a Full Professor at Wageningen University's Mathematical and Statistical Methods group (Biometris). His research focuses on mathematical biology, reaction-diffusion equations, and nonlinear dynamics. He explores topics such as predator-prey systems, tumor invasion models, ecological stability, and pattern formation in biological systems. Key projects include analyzing cyanobacterial bloom control mechanisms, optimizing linear time-invariant systems, and understanding stochastic climate models. He supervises doctoral candidates in areas like AI-driven ecological resilience and soil microbial communities. With over 42 publications, his work spans theoretical and applied mathematics, contributing to both fundamental science and practical applications in agriculture and environmental systems. His research integrates mathematical modeling, stability analysis, and numerical simulations, addressing challenges in ecology, oncology, and systems biology. Collaborations span global institutions, emphasizing interdisciplinary approaches to complex biological and environmental problems.

Emmanuelle AUGERAUD-VERONمشاهده پروفایل

استاد

• Ecosystem Management
• Environmental Economics
• Epidemiological Modeling
• +۳ مورد دیگر

Emmanuelle Augeraud-Véron is a professor at the Bordeaux School of Economics (BSE) at the University of Bordeaux. She serves as head of the research team 'Economics of soci-ecosystems,' focusing on interdisciplinary studies linking environmental, behavioral, and economic dimensions of disease outbreak dynamics. Her work explores the conditions for disease emergence in socio-ecological systems and the influence of disease perception on prevention efforts, particularly regarding Lyme disease. She holds editorial roles at the Journal of Mathematical Economics and Mathematical Modelling of Natural Phenomena , and is vice-dean overseeing international mobility in the faculty of economics. Her research interests span ecosystem management (pollution, biodiversity, epidemics), nested generation models, and mathematical tools such as dynamic systems in economics and epidemiology, alongside optimal control theory. She has contributed extensively to understanding optimal control strategies for environmental and public health challenges through applied mathematical frameworks. Her publications reflect a strong focus on interdisciplinary applications of mathematical economics to real-world problems, including groundwater pollution control, epidemic modeling with seasonal variations, and the impact of trade frequency on economic equilibria. She has also addressed conservation challenges like dolphin population dynamics affected by fisheries bycatch.

• Computational Biology
• Machine Learning
• Network Analysis
• +۳ مورد دیگر

Preetam Ghosh, Ph.D. , is a Research Professor in the Department of Computer Science at Virginia Commonwealth University (VCU) , with cross-disciplinary focus in computational biology, machine learning, and network science. His work bridges engineering and biomedical research , particularly in pandemic modeling, multiomics data integration, and bioinformatics tool development. Academic Role: Research Professor, VCU School of Engineering Key Research Areas: Computational Biology, Network Analysis, Pandemic Modeling, Multiomics Data, Bioinformatics Algorithms Recent Research Trends include: Application of machine learning to biomedical data (e.g., drug-target affinity prediction, breast cancer subgroup classification) Development of physics-informed models for pandemic propagation and chemical reaction networks Innovations in network science , such as link prediction and vulnerability analysis for biological and IoT systems Advancing single-cell genomics through consensus algorithms (COFFEE, CHAI, CORTADO) Design of adaptive routing protocols for disaster-resilient IoT networks (ADRIN, ADRIN2.0)

Christopher Greenmanمشاهده پروفایل

مدرس

• Statistical Physics
• Stochastic Processes
• Combinatorics
• +۵ مورد دیگر

Christopher Greenman is a Lecturer in the School of Computing Sciences at the University of East Anglia. His academic background includes a mathematics degree from Durham University and a PhD from the University of Edinburgh, focusing on number theory and quantized harmonic oscillators. He has held roles at Bolton University, industry consultancy, and the Sanger Institute before joining UEA in 2011. Education: Bachelor's in Mathematics, Durham University PhD in Mathematics, University of Edinburgh Research Interests: Statistical physics applied to age-dependent branching processes Quantum field theory methods in biological systems Combinatorial approaches to genomic rearrangements Cancer and viral evolutionary processes Machine learning applications in drug discovery and genomics Teaching: Advanced Statistics, Computing Principles, and Natural Science Project Coordination. His work contributes to UN Sustainable Development Goals related to health and innovation. Research Groups: Computational Biology, Machine Learning and Statistics at UEA. Active in 56 peer-reviewed publications focusing on stochastic processes, mathematical biology, and genomic analysis.

Tushar Suhasariaمشاهده پروفایل

پژوهشگر ارشد

• Astrophysics
• Astrochemistry
• Protoplanetary Disks
• +۳ مورد دیگر

Dr. Tushar Suhasaria is a Research Fellow at the Max Planck Institute for Astronomy in Heidelberg, Germany. His primary affiliation is with the Planet and Star Formation Department, where he contributes to the ERC Origins and Laboratory Astrophysics research groups. His work focuses on understanding the chemical and physical processes in protoplanetary disks and interstellar environments, employing advanced laboratory simulations and observational data analysis. Research Interests: Suhasaria's research spans astrochemistry, interstellar ice chemistry, and the formation of complex organic molecules in space. He investigates how molecular species evolve under cosmic radiation, thermal processes, and catalytic surface reactions on interstellar grains. His studies are critical for understanding prebiotic chemistry's origins and the conditions for planetary system formation. Laboratory and Observational Work: He uses cutting-edge experimental setups to simulate space environments, analyzing molecules like formamide, ethanolamine, and carbon dioxide under conditions mimicking protoplanetary disks and cometary surfaces. His collaborations with the Origins Lab and Jena-based Laboratory Astrophysics group enhance interdisciplinary approaches to decoding cosmic chemistry. Contact: Located at Königstuhl 17, Heidelberg, he can be reached via email at suhasaria@mpi-astronomy.de or phone +49 6221 528-202.

Anindita Ghoshمشاهده پروفایل

پژوهشگر

• Generative AI
• Computer Vision
• Deep Learning
• +۵ مورد دیگر

Anindita Ghosh is a fourth-year Ph.D. student in Computer Science at Saarland University, Germany, under the supervision of Prof. Philipp Slusallek and Prof. Christian Theobalt. She holds the role of AI Researcher at the German Research Center for Artificial Intelligence (DFKI) since August 2021. Her research focuses on advanced deep learning models for 3D human motion synthesis, multi-agent interaction, and generative AI techniques, with applications in computer vision and text-driven motion generation. Education includes a M.Sc. in Visual Computing (Saarland University, 2018-2021), M.Sc. in Computer Science (St. Xavier's College, Kolkata, 2014-2016), and B.Sc. in Computer Science (St. Xavier's College, 2011-2014). Notable achievements include the Best Master's Project Award (2016) and Vidyasagar Scholarship (2009). Key research contributions include DuetGen (SIGGRAPH 2025), ReMoS (ECCV 2024), and IMoS (Eurographics 2023). Her work addresses challenges in motion prediction, multi-agent systems, and text-based generation, with codebases released for public use. Collaborations include Snap Research, Google, and Purdue University. Professional experience includes research internships at Snap Research (2024), Google (2023), and DFKI. Teaching assistant roles and conference reviewing (CVPR, ECCV, AAAI) further highlight her academic engagement. Her lab contributions focus on generative AI and human motion modeling.

• Chemical Engineering
• Catalysis
• Green Chemistry
• +۳ مورد دیگر

Adriana Freites is a researcher specializing in chemical engineering processes, particularly focusing on fatty acid epoxidation and reaction modeling. She has contributed to multiple projects and publications since 2014, with a notable emphasis on enzymatic catalysis, green chemistry, and process intensification. Her work includes collaborations on projects such as the epoxidation of vegetable oils using microwaves and ultrasound, highlighting innovative approaches in sustainable chemical processes. Research Interests: Development of catalytic systems for fatty acid epoxidation Mathematical modeling of reaction-diffusion systems Process intensification via ultrasound and microwave technologies Enzymatic and chemo-enzymatic reaction pathways Collaborations and Projects: Co-Principal Investigator in the 2015 project 'Epoxidation of vegetable oils assisted by microwaves and ultrasound', emphasizing interdisciplinary research in chemical engineering. Labs/Teams: Involved in research groups focused on sustainable chemical processes and reaction engineering at collaborating institutions.

• Lithium-ion Batteries
• Electrochemical Materials
• Nanowire Technology
• +۴ مورد دیگر

Dr. Michela Ottaviani is an Assistant Lecturer in the Department of Applied Science at the Faculty of Applied Sciences and Technology . Her research focuses on advanced energy storage systems, particularly lithium-ion and lithium-metal batteries, leveraging nanowire technologies and machine learning for optimizing battery performance. Her work contributes to UN Sustainable Development Goals 7 (Affordable and Clean Energy) and 9 (Industry, Innovation, and Infrastructure) . Key research areas include nanomaterial synthesis (e.g., silicon nanowires), electrolyte compatibility, and machine learning-driven modeling of battery state-of-charge. She has collaborated internationally on electrochemical and material science projects. Recent trends in her articles (2023–2025) highlight advancements in: Machine learning algorithms for battery management systems, Nanowire architectures to stabilize lithium metal anodes, Electrolyte optimization for high-performance batteries. No scientific awards or grants are explicitly listed. Her research emphasizes interdisciplinary approaches to sustainable energy solutions.

Thanh Trung Nguyenمشاهده پروفایل

دانشیار

• Inverse Problems
• Numerical Analysis
• Optimization
• +۶ مورد دیگر

Thanh Trung Nguyen is an Associate Professor and Graduate Program Coordinator in the Department of Mathematics at Rowan University's College of Science & Mathematics. He holds a PhD in Engineering Mathematics from Vrije Universiteit Brussel and completed postdoctoral research at the Austrian Academy of Sciences, UNC Charlotte, and Iowa State University. Dr. Nguyen's research focuses on inverse problems for partial differential equations with applications in subsurface detection, through-wall imaging, water quality modeling, and smart farming. His work combines mathematical theory with computational methods to solve practical engineering challenges, including infrared thermography for buried object detection, electromagnetic induction for subsurface imaging, and AI-driven agricultural monitoring. Dr. Nguyen has secured research funding from NSF, US Army Corps of Engineers, and NJ Department of Agriculture, and serves as a member of the Society for Industrial and Applied Mathematics. Nguyen's publication record demonstrates consistent contributions to inverse problem methodologies applied to environmental monitoring, nondestructive testing, and advanced material analysis. His current projects include developing globally convergent methods for coefficient identification and applying artificial intelligence to agricultural challenges.

• Interfacial dynamics
• Multiphase flow
• Pattern formation
• +۲ مورد دیگر

Dr.-Ing. Karin Schwarzenberger is Group Leader for Interfacial Phenomena at Helmholtz-Zentrum Dresden-Rossendorf (HZDR) and since 2020 deputy group leader for the Chair of Transport Processes at Interfaces at TU Dresden. She has been continuously affiliated with TU Dresden’s Faculty of Mechanical Engineering since 2003, advancing from diploma studies through PhD to post-doctoral and leadership roles. Education: Doctoral studies (Dr.-Ing.), TU Dresden, Faculty of Mechanical Engineering, 2010–2015 Thesis: “Experimental Investigations on Stationary Solutal Marangoni Instability” Diploma in Process Engineering, TU Dresden, 2003–2010 Diploma Thesis: “Calculation of Interaction Energies between Fractal Aggregates” Research Interests Karin Schwarzenberger’s work centers on interfacial transport phenomena, pattern formation, and multiphase flows. Key areas include: Interfacial dynamics at liquid/liquid and liquid/gas boundaries Particle–particle and particle–interface interactions Solutal Marangoni instabilities and convective structures Coacervation and complexation of polymers and surfactants Microgravity effects on buoyancy-driven instabilities Her experimental approach combines high-resolution optical diagnostics with numerical simulations to unravel the coupling between mass transfer, surface tension gradients, and hydrodynamic instabilities. Publication Trends Between 2012 and 2025 Schwarzenberger has published more than 30 peer-reviewed articles. Recent work (2022–2025) demonstrates a clear shift toward applied interfacial engineering—surface functionalization of Ti64 alloys, foam fractionation for protein purification, and coacervation-based membrane formation—while maintaining fundamental studies on Marangoni convection and buoyancy-driven instabilities under microgravity. Scientific Awards No specific awards are mentioned in the provided materials. Advising & Funding While the text does not list individual students, her role as group leader at HZDR and deputy chair leader at TU Dresden implies supervision of doctoral researchers and technical staff. Funding is implicitly provided through Helmholtz Association programmes and collaborative projects with TU Dresden. Labs & Teams She heads the Interfacial Phenomena group at HZDR, located in building 310 at Bautzner Landstrasse 400, Dresden. The group operates state-of-the-art optical measurement techniques (PIV, LIF, high-speed imaging) and performs experiments both in the laboratory and under microgravity conditions (parabolic flights).

Riccardo MUOLOمشاهده پروفایل

پژوهشگر

• Nonlinear Dynamics
• Network Science
• Pattern Formation
• +۴ مورد دیگر

Riccardo MUOLO is a Researcher at Tokyo Institute of Technology, holding a Post-doctoral position since October 2023. He has held visiting roles at institutions including Linköping University, Universidade Federal de Rio de Janeiro, and others. His academic journey includes a Doctor of Science from naXys - Department of Mathematics (2023), a Master of Mathematics from the University of Florence (2018), and a Bachelor of Science from the University of Florence (2015). His research focuses on nonlinear dynamics, network science, and pattern formation, particularly exploring Turing patterns on networks and higher-order structures. Key interests include synchronization phenomena, higher-order interactions, and topological dynamics. Recent work emphasizes the impact of network topology and directionality on system behavior. Notable publications include studies on topology-driven dynamics in higher-order networks and synchronization mechanisms in directed hypergraphs. His work has been recognized with awards such as the 2022 Fonds Agathon de Potter and 2018 Graduation Prize. MUOLO actively collaborates across global institutions and participates in conferences on quantum structures, nonlinear dynamics, and complex systems. He has organized workshops like Cameroon's first school on nonlinear dynamics and contributed to editorial projects like the European Physical Society's 2024 Calendar promoting gender representation in science.

Martin Heidaمشاهده پروفایل

پژوهشگر

• Homogenization
• Partial Differential Equations
• Stochastic Homogenization
• +۶ مورد دیگر

Dr. Martin Heida is a researcher at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS), Berlin, where he has been a member of the Partial Differential Equations research group since 2015. His work focuses on homogenization theory, stochastic processes, and numerical methods for complex systems. He holds a PhD from the Universities of Heidelberg and Prague (2011), and prior to WIAS, he was a PostDoc at TU Dortmund (2011–2015) and a graduate student at the Heidelberg Graduate School of Mathematical and Computational Methods (2008–2011). Education : PhD in Mathematics and Physics, cotutelle between Heidelberg University (Prof. Willi Jäger) and Charles University Prague (Prof. Josef Málek), 2011 Diploma in Physics and Mathematics from Heidelberg University, 2008 Research Interests : His research spans stochastic homogenization, partial differential equations (PDEs), numerical analysis, and material modeling. He develops methods for multiscale problems in porous media, plasticity, and fracture mechanics, with applications to battery aging, geology, and phase separation. Grants & Projects : Third-party funded projects: SPP 2256 (Fractal and Stochastic Homogenization), Math+ project on battery aging dynamics Teaching : Recent courses include Nonlinear Analysis at TU Munich, Homogenization Theory, and Analysis for Engineers. Labs & Collaborations : Leads the HighVoronoi.jl project for high-dimensional Voronoi diagram computations and collaborates with researchers in applied mathematics and computational science.