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Tsvetan Dimitrov HristovView profile

Associate Professor

• Partial differential equations
• Mixed-type equations
• Boundary value problems
• +7 more

Tsvetan Dimitrov Hristov is an Associate Professor at the Faculty of Mathematics and Informatics, Sofia University "St. Kliment Ohridski", specializing in differential equations. His academic career spans theoretical research in partial differential equations and applied mathematical modeling, with recent focus on epidemiological applications during the COVID-19 pandemic. Education: 1998: Master degree, Sofia University 2006: Ph.D., Sofia University Hristov's research primarily focuses on Partial Differential Equations (PDEs), particularly mixed-type equations, boundary value problems, and singular solutions to Protter problems for Keldysh-type equations. His work bridges theoretical mathematical analysis with practical applications, especially evident in his recent shift toward mathematical epidemiology. Since 2020, he has been actively developing SEIRS models incorporating vaccination dynamics to analyze the COVID-19 pandemic in Bulgaria, a country with the lowest vaccination rate in the European Union and the second-highest mortality rate globally. His expertise also extends to mathematical modeling, computer simulation, and visualization techniques using Maple and MATLAB. Analysis of Hristov's publication history reveals a clear evolution from purely theoretical work on PDEs (2014-2018) to applied mathematical epidemiology (2020-2024). His earlier publications focused on singular solutions to Protter problems for Keldysh-type equations in multiple dimensions, while his recent work demonstrates adaptability in applying mathematical expertise to address urgent public health challenges. This transition showcases his ability to leverage deep theoretical knowledge for practical problem-solving in real-world contexts. Hristov teaches various courses including Partial differential equations and applications, Equations of mathematical physics, Differential equations, and Visualization with Maple and MATLAB for PDE. His interest in educational technology is reflected in his 2018 publication on electronic assessment in mathematics education. He maintains active collaborations with researchers including Nedyu Popivanov, Svetozar Margenov, and Iva Ugrinova, with his 25 publications accumulating 1,906 reads and 211 citations according to ResearchGate. Hristov holds office hours on Wednesdays from 14-16 in room FMI-327 and continues active research as evidenced by his 2024 publication on computing COVID-19 reproduction numbers using actual data. His work exemplifies the relevance of mathematical analysis to contemporary global challenges, bridging pure theory with practical applications.

Boyan KostadinovView profile

Associate Professor

• Computational Thinking
• Data Science Education
• Interactive Visualizations
• +3 more

Boyan Kostadinov is an Associate Professor in the Mathematics Department of the School of Arts & Sciences at New York City College of Technology, City University of New York (CUNY). He joined the department in 2009 after industry experience at Credit Suisse Securities and academic roles at UCLA, where he earned his Ph.D. in Mathematics in 2005. His educational background includes: Ph.D. in Mathematics, University of California, Los Angeles Master in Finance, Bendheim Center for Finance, Princeton University M.A. in Mathematics, University of California, Los Angeles M.S. in Physics, Sofia University Professor Kostadinov specializes in computational thinking and data science applications across STEM education, with emphasis on creating interactive visualizations for mathematics and physics concepts. His research bridges theoretical mathematics with real-world problem-solving through R, Python, and Markdown-based dynamic documents, fostering intuitive understanding via simulations and animations. He actively develops innovative teaching materials for diverse educational levels from K-12 to graduate studies. His recent publications reveal interdisciplinary trends spanning medical imaging analysis, cybersecurity forensics, circadian rhythm modeling, and election forecasting, unified by computational approaches to complex systems. This work demonstrates consistent application of mathematical modeling to biological, social, and educational challenges. His scientific recognition includes: Robert Sorgenfrey Distinguished Teaching Award (UCLA) Professor Kostadinov has mentored over 40 undergraduate researchers whose work has been presented at national conferences. He secured significant funding including PSC-CUNY Research Grants (Cycles 48-49), served as Co-PI on two Department of Education MSEIP grants totaling $1.486 million, and contributed to NEH and NSF proposals developing interactive educational modules for GIS and statistics curriculum. His travel grants from MAA and PDAC support active participation in national STEM education conferences. He leads initiatives in open-source computational framework development for educational applications, with current projects focusing on data-driven approaches to biological systems and political modeling through collaborative student research teams.

Cristobal BertoglioView profile

Professor

• Biomedical Engineering
• Computational Mathematics
• Fluid Dynamics
• +5 more

Cristobal Bertoglio is a Professor at the Faculty of Medical Sciences and an Associate Professor at the Faculty of Science and Engineering of the University of Groningen, specializing in Vascular Medicine and Computational and Numerical Mathematics . Research Interests Biomedical Engineering Computational Mechanics Fluid Dynamics in Cardiovascular Systems Parameter Estimation for Medical Imaging Cardiac Elastodynamics Numerical Analysis Research Trends His recent work focuses on computational benchmarks in cardiac mechanics, fluid-structure interaction models, and parameter estimation from undersampled MRI data. Key areas include patient-specific modeling, denoising techniques, and numerical methods for cardiovascular applications. Collaborations & Contributions Active in international collaborations, Bertoglio contributes to SDGs related to health and innovation. His research output includes 40 publications, datasets in cardiac modeling, and methodological advancements in fluid dynamics and imaging.

Pablo LemosView profile

Research Fellow

• Cosmology
• Machine Learning
• Astrophysics
• +3 more

Pablo Lemos is a postdoctoral Research Fellow at the Montrel Institute for Learning Algorithms (MILA) and the CIELA institute at the University of Montreal, where he is affiliated with the Department of Physics. He co-leads the accelerated forward modelling group at the Simons Collaboration in Learning the Universe and is a member of the Dark Energy Survey (DES) collaboration. Research Focus Lemos specializes in applying machine learning techniques to astrophysics and cosmology problems. His core research areas include: Graph neural networks for cosmological simulations Symbolic regression methods for data analysis Simulation-based inference techniques Bayesian inverse problem solving Large-scale structure analysis Publication Analysis Lemos's recent publications (2024-2025) demonstrate strong interdisciplinary work combining cosmology, astrophysics, and machine learning. Major themes include cosmological constraints from galaxy clustering and weak lensing, advanced Bayesian inference methods using diffusion models, and applications of neural networks to astrophysical data. Approximately 60% of recent publications focus on machine learning applications in cosmology, while 25% address astrophysical inverse problems, and 15% explore cometary science and planetary physics. Collaborations and Groups Co-leader of accelerated forward modelling group at Simons Collaboration in Learning the Universe Active member of Dark Energy Survey (DES) collaboration

• Seismology
• Global Seismology
• Earth's Deep Interior
• +7 more

Dr. Su Talavera Soza is a Researcher at the Faculty of Geosciences, University of Utrecht, specializing in the DES - Seismology group. Their work bridges seismology, machine learning, and geophysical inversion to study Earth's crust, mantle, and inner core. They employ seismic normal modes and surface waves with advanced instruments like superconducting gravimeters and ocean bottom seismometers. Key Expertise Areas: Seismology, Global Seismology, Earth's Deep Interior, Machine Learning, Inverse Problems, Tomography Skills: Python, Fortran, MATLAB, Data Analysis, Visualization Current research focuses on physics-informed machine learning for full-waveform inversion and planetary-scale structure analysis. They collaborate internationally across the USA and Europe, contributing to 15+ publications between 2020-2025, including high-impact journals like Nature and Nature Geoscience . Notably, they developed the FrosPy Python toolbox for normal mode seismology. While no formal awards are listed, their work on mantle attenuation models and inner core dynamics has been widely shared (214+ X posts) and discussed in Wikipedia. They hold a 2021 PhD from Utrecht University titled Observing seismic attenuation in the Earth’s mantle and inner core using normal modes , supervised by A. Deuss.

Matthew J. BergView profile

Professor

• Condensed Matter Physics
• Atmospheric Aerosol Characterization
• Digital Holography
• +3 more

Matthew J. Berg is a Professor in the Department of Physics at Kansas State University, specializing in experimental and theoretical electromagnetic scattering for aerosol particle characterization. His research bridges atmospheric science, condensed matter physics, and optical engineering, focusing on improving climate models and developing defense/public health applications. Ph.D. (2008) and B.S. (2003) in Physics Recipient of NSF CAREER, US Army YIP, and DURIP grants His work leverages digital holography to solve the inverse problem in light scattering, enabling in situ 3D imaging of aerosols without sample distortion. Recent projects include UAV-based atmospheric monitoring and modifying gold optical properties with ultrafast lasers. Publications span Science Reports , Applied Optics , and Journal of Quantitative Spectroscopy and Radiative Transfer . Scientific awards include: National Science Foundation CAREER Award US Army Young Investigator Program (YIP) Award Defense University Research Instrumentation Program (DURIP) Grant He has advised multiple PhD students and postdocs in aerosol physics, holography, and light scattering. His lab develops innovative techniques for solar energy applications and ferrofluid energy harvesting systems, with patents on drone-based holographic imaging.

Balázs KovácsView profile

Professor

• Numerical Analysis
• Partial Differential Equations
• Geometric Surface Flows
• +8 more

Professor Balázs Kovács is a full Professor (W3) of Mathematics and its Applications at the University of Paderborn since July 2023, where he leads research in the Numerical Analysis of Partial Differential Equations group within the Institute of Mathematics, part of the Faculty of Electrical Engineering, Computer Science and Mathematics. University of Paderborn (2023-present): Full Professor (W3) University of Regensburg (2020-2023): DFG Heisenberg Fellow Technical University of Munich (2022): Substitute Professor (W2) Eberhard Karls University of Tübingen (2015-2020): PostDoc ELTE Eötvös Loránd University (2006-2011): Bachelor and Master in Mathematics Prof. Kovács specializes in numerical analysis of algorithms for geometric surface flows and evolving surface partial differential equations, with particular expertise in mean curvature flow, Willmore flow, and inverse mean curvature flow. His research focuses on time discretization methods, numerical approaches for parabolic and wave-type problems with dynamic boundary conditions, and numerical analysis for Maxwell's equations. His work bridges theoretical mathematics with practical computational methods for solving complex geometric evolution problems. His recent publications demonstrate consistent output in top computational mathematics journals, with a focus on evolving surface PDEs, tumor growth modeling, and error analysis for numerical methods. The publications reveal a strong emphasis on theoretical numerical analysis combined with practical computational implementations. DFG Heisenberg Fellowship (2020-2026): Numerical analysis of geometric flows and evolving surface partial differential equations (Project ID: 446431602) DFG Research Training Group 2339 (2022-2023): Interfaces, Complex Structures, and Singular Limits (Project ID: 321821685) DFG Research Group 3013 (2023-2025): Vector- and Tensor-Valued Surface PDEs (Project ID: 417223351) Prof. Kovács actively contributes to academic service, having served on the Faculty Council at the University of Regensburg from 2021-2023. His teaching portfolio includes advanced courses in numerical methods for stationary equations and specialized seminars like the Coding Challenge. His research program represents a significant contribution to the field of numerical analysis for geometric evolution equations, with ongoing projects funded through prestigious German Research Foundation programs.

• Control Engineering
• Robotics
• Mechanical Engineering
• +3 more

Lingchong Gao is a researcher at the Chair of Material Handling, Material Flow, Logistics (fml) at Technical University of Munich, specializing in dynamics and control of hydraulic crane systems. His work integrates port-Hamiltonian formulations with optimal control strategies to address flexible boom manipulator challenges in material handling. His research focuses on control engineering for heavy machinery , particularly anti-sway control of double-pendulum cranes, trajectory optimization for knuckle boom systems, and wear analysis in bulk transport equipment. Key methodologies include neural network controllers based on Lyapunov stability, geometrically exact beam theory for flexible structures, and symplectic approaches for nonlinear optimal control problems. Analysis of his 15 most recent publications (2019-2025) reveals a concentrated research trajectory: early work established foundations in port-Hamiltonian modeling of telescopic booms (2019), evolving through flexible dynamics simulation (2020-2021) to advanced quasi-static control strategies (2023) and differential dynamic programming for trajectory planning (2025). Over 80% of his publications directly address crane system dynamics, with emerging emphasis on computational efficiency in hydraulic actuation. Dr. Gao actively supervises student theses, seeking candidates with expertise in Mechanics, Hydraulic Systems, and Optimal Control Theory for bachelor/master projects. He leads the project Dynamic Structure-preserving Analysis and Control of Flexible Long Boom Manipulator Based on Port-Hamiltonian System , securing research continuity in this niche. As core personnel in Prof. Johannes Fottner's research group, he contributes to TUM's logistics innovation ecosystem including 5G applications in material flow . The fml chair maintains strong industry partnerships for translating theoretical advances into port logistics and construction equipment solutions.

• Variational inference
• Bayesian inference
• Deep learning
• +4 more

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.

Chao ZhangView profile

Research Fellow

• Scientific Computing
• Numerical Analysis
• Geophysics
• +5 more

Chao Zhang is a Research Fellow at the Department of Applied Mathematics and Computer Science , Technical University of Denmark. His work focuses on computational methods for geophysical and engineering applications. Research Areas: Inverse problems, uncertainty quantification, fluid dynamics, numerical analysis, and mathematical modeling Key Techniques: Level-set methods, regularization, finite element analysis, and simulation-based optimization Recent research highlights include novel approaches for rock permeability determination and uncertainty propagation in groundwater flow models. Publications span thermo-hydro-mechanical simulations, electrical impedance tomography, and thermal analysis of microelectronics. No formal academic awards or student mentoring information was found in the provided data.

Roberto MoralesView profile

Researcher

• Control Theory
• Inverse Problems
• Partial Differential Equations
• +1 more

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 TranterView profile

Senior Lecturer

• Nonlinear Waves
• Fluid Dynamics
• Mathematical Modelling
• +3 more

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.

• Artificial Intelligence
• 3D Scene Reconstruction
• Simultaneous Localization and Mapping (SLAM)
• +3 more

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.

• Parallel Programming
• GPU Programming
• Image Processing
• +5 more

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.

Eduard Campillo-FunolletView profile

Lecturer

• Bayesian Statistics
• Inverse Problems
• Statistical Ecology
• +3 more

Dr. Eduard Campillo-Funollet is a Lecturer in Statistics at the School of Mathematical Sciences , Lancaster University. His research focuses on inverse problems and inference in biology, ecology, epidemiology, and industrial applications, using Bayesian methods for parameter estimation in differential equations. Research Interests include: Bayesian inference for dynamical systems Inverse problems in biological and industrial contexts Statistical ecology and epidemiological modeling Computational methods for ordinary/partial differential equations Publication Trends show expertise in epidemiological modeling (e.g., COVID-19 transmission dynamics ), computational biology (e.g., DNA replication studies ), and industrial applications (e.g., gas quality prediction ), with a consistent emphasis on robust Bayesian methodologies. Projects include collaborations on Nigeria-UK epidemiological research , Finland-Lancaster synthetic gas analysis , and Diabetic Foot Ulcer measurement accuracy , highlighting interdisciplinary applications of statistical methods. Contact: e.campillo-funollet@lancaster.ac.uk