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Matthew JuniperView profile

Professor

• Flow Instability
• Thermoacoustic Systems
• Adjoint Methods
• +4 more

Professor Matthew Juniper holds a Chair in Thermofluid Dynamics at the University of Cambridge's Department of Engineering and serves as Principal Investigator for the UK Fluids Network. He is partially seconded to the Alan Turing Institute as lead on the 'Adjoint-accelerated Programmable Inference for large PDEs' project. His academic journey includes a PhD from École Centrale Paris (2001), consultancy at McKinsey & Co. (2002-2003), and progressive appointments at Cambridge culminating in full Professorship (2015). His research centers on flow instability with dual focus on hydrodynamic and thermoacoustic systems. Key methodologies include adjoint-based sensitivity analysis and Bayesian inference for integrating physics-based models with data. This approach addresses industrial challenges in gas turbine combustion (emissions reduction), cyclone separator noise control, and laminar flow retention for advanced materials fabrication. Strategic alignment spans Energy Systems, Manufacturing Innovation, and Intelligent Algorithms. Notable scientific awards include ERC Advanced Grant (2025), Alan Turing Institute Fellowship (2024), and American Physical Society Fellowship (2021). His group actively collaborates with industrial partners through software deployment and participates in international networks like the Marie Curie ANADE project. ERC Advanced Grant (2025) Fellow, Alan Turing Institute (2024) Best Paper Award, Symposium on Thermoacoustics (2023) Fellow, American Physical Society (2021) PI, UK Fluids Network (2016-present) Juniper leads the UK Fluids Network, fostering national collaboration in fluid dynamics research while directing multiple industrial projects. His group develops algorithms applicable to uncertainty quantification and complex system resilience, with current focus on merging machine learning with physics-based modeling to achieve data efficiency and interpretability. Future work emphasizes programmable inference for large-scale PDE systems through his Turing Institute project.

CLEMENTE COBOS SANCHEZView profile

Professor

• Transcranial Magnetic Stimulation (TMS)
• Biomedical Imaging
• Biosignal Processing
• +3 more

Clemente Cobos Sánchez is a Professor at the University of Cádiz, Spain, affiliated with the Department of Automation, Electronics, Architecture and Computer Networks Engineering. His research focuses on Transcranial Magnetic Stimulation (TMS) coil design , Biomedical Imaging , and Biosignal Processing . He leads projects related to electromagnetic design at the TIC138 research group. Primary Affiliation : University of Cádiz Department : Automation, Electronics, Architecture and Computer Networks Engineering Research Group : TIC138 Electronic and Electromagnetic Design Research Highlights: His work spans from theoretical mathematical optimization (e.g., nonconvex set minimization, operator norm analysis) to applied biomedical engineering (TMS depth optimization, concurrent TMS-fMRI systems). Recent publications also touch on space engineering applications, including magnetoresistive sensors for CubeSats and low-noise magnetic measurement systems. Scientific Trends: Combines computational electromagnetics with medical device innovation, applying advanced mathematical frameworks to both neurostimulation and space instrumentation challenges. His articles show interdisciplinary expertise in boundary element methods , multi-objective optimization , and inverse problem solving .

Iveta HnetynkovaView profile

Associate Professor

• Numerical Mathematics
• Linear Algebra
• Inverse Problems
• +5 more

Iveta Hnetynkova is an Associate Professor at the Department of Numerical Mathematics, Faculty of Mathematics and Physics, Charles University in Prague. She specializes in numerical linear algebra, inverse problems, and regularization methods, with applications in image processing and scientific computing. Education: Doctor of Natural Sciences (RNDr.) from Charles University (2003) Ph.D. in Scientific Computations from Charles University (2006) Habilitation thesis on error-contaminated linear approximation (2019) Research Interests: Krylov subspace methods Total Least Squares (TLS) formulations Noise revealing in discrete inverse problems Tensor generalizations and structured matrices Applications in image processing and jewelry defect analysis Scientific Awards: Visegrad Group Young Researcher Award (2014) J. Jirsa Prize for textbook excellence (2013) I. Babuska Prize (2nd place) (2007) SVOČ Prize in mathematics (2003)

David HammondView profile

Professor

• Signal and Image Processing
• Wavelet Analysis
• Graph Signal Processing
• +2 more

David Hammond is a Professor of Mathematics at Oregon Institute of Technology's Portland-Metro campus, where he has held a faculty position since 2013. His academic trajectory includes Peace Corps service teaching mathematics in Malawi, doctoral studies at NYU's Courant Institute, and postdoctoral research at École Polytechnique Fédérale de Lausanne and the University of Oregon. His educational background features: B.S. in Mathematics/Chemistry from California Institute of Technology (1999) Ph.D. in Mathematics from New York University (2007) Dr. Hammond's research centers on signal and image processing with specialized expertise in wavelet analysis and graph signal processing . His theoretical work on spectral graph wavelets enables advanced data analysis on complex networks, while practical applications span biomedical imaging, microscopy enhancement, and computational neuroscience. This dual focus bridges abstract mathematical frameworks with tangible engineering solutions. Analysis of his recent publications reveals three dominant research streams: (1) foundational contributions to graph signal processing including the spectral graph wavelet transform, (2) biomedical applications in EEG source estimation and head tissue modeling, and (3) interdisciplinary policy work on sugary drink taxation and electricity market design. His 2018 paper on the spectral graph wavelet transform represents a cornerstone contribution with over 500 citations. No scientific awards were documented in the source material. As a full-time professor, Dr. Hammond maintains active research and teaching responsibilities. While specific advisees aren't listed, his publications indicate mentorship through collaborative research. His work has been supported by institutional affiliations rather than explicitly cited grants. Though no dedicated laboratory is described, his research methodology suggests utilization of computational facilities for signal processing tasks, with collaborations spanning biomedical engineering, astronomy, and public health domains.

Brendy Rincon TroconisView profile

Associate Professor

• Corrosion Engineering
• Adhesion Science
• Materials Science
• +3 more

Brendy Rincon Troconis, Ph.D., serves as an Associate Professor in the Department of Mechanical Engineering at The University of Texas at San Antonio's Margie and Bill Klesse College of Engineering and Integrated Design. Holding a Ph.D. from The Ohio State University, she directs research in corrosion engineering, adhesion science, and materials degradation with applications in infrastructure and energy systems. Her educational foundation includes doctoral training at The Ohio State University, establishing expertise in materials characterization and failure analysis. Dr. Rincon Troconis's scholarly work focuses on corrosion inhibition mechanisms, advanced adhesion measurement techniques (particularly blister test innovations), and computational modeling of material degradation. Her investigations span hydrogen embrittlement of high-strength alloys, green corrosion inhibitors for concrete reinforcement, and stress corrosion cracking in oilfield environments, bridging fundamental electrochemistry with practical engineering solutions for marine and energy infrastructure. Analysis of her 2020-2025 publications reveals dominant themes in computational adhesion modeling (38% of recent work), marine corrosion protection (29%), and sustainable materials development (22%). Her research consistently integrates experimental methods like digital image correlation with finite element analysis and machine learning, demonstrating methodological innovation in predicting long-term material performance under environmental stressors. She received the prestigious Gulf Research Program Early-Career Research Fellowship in Offshore Energy Safety from the National Academies in 2021, recognizing her contributions to energy infrastructure safety. Dr. Rincon Troconis has secured competitive research funding through the Gulf Research Program fellowship. While specific student advisees aren't listed in available materials, her active publication record with graduate co-authors indicates ongoing mentorship of master's and doctoral students in mechanical engineering and materials science. Her laboratory likely focuses on electrochemical characterization, adhesion testing, and computational materials modeling within UTSA's engineering facilities.

• Computational number theory
• Cryptography

Konstantinos Draziotis is an Associate Professor at the Department of Informatics, School of Informatics, Aristotle University of Thessaloniki. His academic career at the university began as a Lecturer from 2013 to 2019, and he has been serving as an Associate Professor since 2019. His educational background includes a PhD in Mathematics from Aristotle University of Thessaloniki (2005) and a Bachelor's Degree in Mathematics from the same institution (1998). Draziotis specializes in Computational Number Theory and Cryptography , with a particular focus on Diophantine equations, elliptic curves, and cryptanalysis of digital signature schemes. His research bridges theoretical mathematics with practical cryptographic applications, especially in analyzing the security of cryptographic protocols and developing mathematical methods for solving complex number-theoretic problems. He has developed a Greek-language textbook on Introduction to Cryptography with Creative Commons licensing, demonstrating his commitment to accessible education in this field. His recent publications demonstrate a strong trend toward cryptanalysis of digital signature schemes (particularly DSA and ECDSA), lattice-based attacks , and number-theoretic approaches to cryptographic problems . His work spans both theoretical aspects of number theory (Diophantine equations, integer points on curves) and practical applications in cryptography (cryptosystem attacks, knapsack problems). The interdisciplinary nature of his research connects deep mathematical theory with real-world security applications, with publications consistently appearing in reputable journals like Information and Computation, Advances in Mathematics of Communications, and Theoretical Computer Science. Draziotis leads a crypto research group at the Aristotle University of Thessaloniki and actively contributes to open-source cryptographic and mathematical software ecosystems. His academic service includes participation in departmental administrative functions as indicated by various announcements on examination schedules, graduation ceremonies, and departmental operations visible on his website. He maintains collaborations with researchers including Dimitrios Poulakis, Marios Adamoudis, and Anastasia Papadopoulou, among others, reflecting an active research network in both Greek and international academic communities.

Angelo Noè CarusoView profile

Researcher

• Mathematical Analysis
• Functional Analysis
• Approximation Theory
• +4 more

Angelo Noè Caruso is a tenure track researcher (RTT) at Link Campus University in Rome, specializing in mathematical analysis (SSD MAT/05). He holds this position since February 2024, following postdoctoral research at the Silesian University in Opava (February 2022-January 2024) and Gran Sasso Science Institute (GSSI). He obtained his PhD in Mathematical Analysis, Models and Applications from the International School for Advanced Studies (SISSA) in 2019. His research focuses on: Operator theory and functional analysis Abstract approximation theory Applications to linear inverse problems and differential systems Numerical methods with physical applications He has co-edited a monograph with Prof. Alessandro Michelangeli synthesizing his doctoral work on Krylov spaces/projection methods and recent developments. No awards, grants, or supervised students are mentioned in available records.

Gregor KemperView profile

Professor

• Invariant Theory
• Commutative Algebra
• Algebraic Geometry
• +4 more

Prof. Dr. Gregor Kemper is a Full Professor holding the Chair of Algorithmic Algebra at the TUM School of Computation, Information and Technology, Technical University of Munich (TUM), a position he has held since 2002. His primary research focuses on algorithmic aspects of mathematics, particularly in invariant theory, commutative algebra, algebraic geometry, group theory, and representation theory, with applications in areas like image processing and geometric positioning. Education & Career: Kemper studied mathematics in Karlsruhe and Heidelberg, completing his doctorate (1994) and habilitation (1999) at Heidelberg University under Prof. Matzat. Prior to joining TUM, he held academic positions internationally, including at Berkeley, Sydney, Singapore, Ann Arbor, and Paris. Research Focus: Kemper's work bridges pure mathematics and computational applications. Key areas include: Algorithmic invariant theory for reductive and nonreductive groups Computational methods in commutative algebra and algebraic geometry Geometric reconstruction using acoustic signals and invariants Development of algorithms for solving fundamental problems in algebra Applications of invariant theory in signal processing and sensor networks Publications: His extensive publication record demonstrates sustained contributions across algebraic disciplines, with recent work emphasizing applied algorithmic solutions for geometric positioning and acoustic sensing. Publications frequently appear in top journals like SIAM Journal on Applied Algebra and Geometry, Advances in Mathematics, and Journal of Algebra. Awards & Recognition: Award for Good Teaching of the Free State of Bavaria (2007) Multiple teaching awards from the TUM student council for courses including Linear Algebra, Commutative Algebra, and Computer Algebra Teaching & Advising: Kemper is a dedicated educator, consistently recognized for teaching excellence. He teaches core and advanced courses in Linear Algebra, Algebra, Commutative Algebra, Invariant Theory, and Computer Algebra. He actively advises doctoral and master's students, with his research group focusing on algebraic computation and applications. Activities: He co-organizes workshops like the Workshop on Geometric Delicacies and contributes to seminars within the algebra and geometry group at TUM.

Nadia Peycheva ZlatevaView profile

Professor

• Variational Analysis
• Nonlinear Analysis
• Nonsmooth Analysis
• +2 more

Nadia Peycheva Zlateva is a Professor at the Department of Probability, Operations Research and Statistics within the Faculty of Mathematics and Informatics at Sofia University. She holds a DSc degree and maintains an active research profile in mathematical optimization and variational analysis, with office FMI-513 and contact details zlateva@fmi.uni-sofia.bg / +359 2 8161-504. Education: PhD in Mathematics (1997, Sofia University) DSc in Mathematics (2017, Sofia University) Research Focus: Professor Zlateva specializes in Variational Analysis, Nonlinear and Nonsmooth Analysis, Subdifferential Calculus, and Optimization. Her work establishes foundational connections between generalized differentiation and optimization theory, particularly through Clarke-Ledyaev inequalities and metric regularity frameworks. She integrates functional analysis with optimization to solve complex nonsmooth problems. Publication Trends: Recent work (2020-2025) demonstrates sustained focus on metric regularity in Fréchet spaces, inverse function theorems, and perturbation methods. Collaborations with Ivanov, Quincampoix, and Topalova dominate her output, appearing in top-tier journals like Journal of Convex Analysis and SIAM Journal on Optimization. Her research consistently bridges abstract variational principles with computational applications. Research Funding: EuroCC2 project (2023-2026, Grant 101101903) EUMaster4HPC (2022-2025, Project 101051997) EuroCC project (2020-2022, Grant 951732) Big Data for Smart Society GATE (2017-2018, Project 763566) Evolution equations project (2002-2006, Project HPRN-CT-2002-00281) Academic Leadership: She teaches core courses including Operations Research, Linear Programming, and Nonlinear Optimization while directing major European computational science initiatives. Her methodology combines theoretical rigor in subdifferential calculus with practical high-performance computing applications through EuroHPC projects.

Russi Georgiev YordanovView profile

Assistant Professor

• Complex Analysis
• Topology
• Mathematical Physics
• +4 more

Russi Georgiev Yordanov is an Assistant Professor in the Department of Complex Analysis and Topology at the Faculty of Mathematics and Informatics, Sofia University "St. Kliment Ohridski". His office is located in room FzF-25, and he can be contacted via email at yordanov@fmi.uni-sofia.bg or by phone at +359 2 8161-546. His research primarily spans mathematical physics and applied materials science, with core interests including: Complex analysis and spectral properties of differential operators Integrable systems and soliton theory, particularly hierarchies of nonlinear equations Inverse scattering problems for Schrödinger-type operators Nonlinear wave phenomena and evolution equations Optical limiting properties of fullerenes and nanomaterials Yordanov's publication record demonstrates two distinct phases: foundational work (1984-1993) on mathematical physics (spectral theory, solitons, inverse scattering), and applied collaborations (1995-1998) characterizing optical limiting in fullerenes. His later interdisciplinary research involved experiments measuring nonlinear optical responses across visible and infrared spectra. No scientific awards, grants, student advisories, or lab affiliations are mentioned in the source material.

• Mathematical Logic
• Computability Theory
• Computable Structure Theory

Stefan Volodev Vatev serves as Assistant Professor in the Department of Mathematical Logic and its Applications at the Faculty of Mathematics and Informatics, University of Sofia "St. Kliment Ohridski". His teaching portfolio includes core theoretical computer science courses: Languages, Automata, and Computability; Computability and Complexity; Semantics of Programming Languages; and Discrete Mathematics and Algorithms. His research centers on Mathematical Logic with specialized focus in Computability Theory and Computable Structure Theory. He investigates effective properties of mathematical structures, embeddings between structures, and algorithmic aspects of algebraic systems. Analysis of his 13 publications (2011-2024) reveals consistent exploration of computability in linear orders, cohesive powers, jump inversion phenomena, and structural embeddings. His work demonstrates strong collaborative patterns with researchers including Bazhenov, Soskova, and Harizanov, primarily published in top venues like Journal of Symbolic Logic and CiE proceedings. No scientific awards were documented in the source materials. Available information lacks details regarding student advising activities, research grants, or laboratory affiliations. No mention of future research directions or ongoing projects was provided.

Georg RegensburgerView profile

Professor

• Applied algebra
• Symbolic computation
• Computer algebra systems
• +8 more

Prof. Dr. Georg Regensburger is a full Professor at the Institute of Mathematics, University of Kassel, specializing in Algorithmic Algebra and Discrete Mathematics. His research combines algebraic and geometric methods with symbolic computation to address problems from analysis and applications. He has held faculty positions at University of Kassel and previously at Johannes Kepler University Linz, where he completed his habilitation in 2019. Regensburger's research focuses on the intersection of pure mathematics and computational applications. His work spans several interconnected areas including integro-differential algebras and boundary problems, operator algebras and algebraic systems theory, positive solutions of generalized polynomial equations, and applications of polyhedral geometry to metabolic networks. His approach ranges from purely algebraic considerations to constructive and algorithmic methods, culminating in the development of software for computer algebra systems. His publication record shows consistent output with significant recent contributions (2020-2025) demonstrating expertise in symbolic computation for operator algebras, integro-differential equations, and applications to biological systems. The publications reveal a strong interdisciplinary approach connecting abstract algebra with practical computational methods and biological applications, particularly in chemical reaction networks and metabolic pathway analysis. Regensburger has been actively involved in organizing the AADIOS (Algebraic and Algorithmic Aspects of Differential and Integral Operators) sessions at ACA (Applications of Computer Algebra) conferences since 2016, including future sessions scheduled for 2025. He has also co-edited several volumes on algebraic methods and symbolic computation. His academic journey includes a habilitation thesis from Johannes Kepler University Linz (2019), a PhD from University of Innsbruck (2004), and a Master's thesis from University of Innsbruck (1999). He maintains active research profiles on ORCID, Google Scholar, and Scopus, and his work is regularly presented at major international conferences in symbolic computation and algebraic methods.

Irina MegheaView profile

Associate Professor

• Variational methods in mechanics
• Mathematical analysis
• Functional analysis
• +5 more

Irina Meghea serves as Associate Professor in the Department of Mathematics II at the Faculty of Applied Sciences, University POLITEHNICA of Bucharest. With a strong academic foundation from the University of Bucharest where she graduated with distinction (final studies mean: 9.97), she has established herself as a prominent figure in mathematical analysis and its applications. Her educational background includes: Faculty of Mathematics, Mechanics Section, University of Bucharest (1985-1990) Ph.D. in Variational Methods in Mechanics (1999) Master's in Sustainable Development Dr. Meghea's research spans multiple domains of mathematical analysis with particular emphasis on variational methods. Her work bridges theoretical mathematics with practical applications, especially in fluid mechanics and environmental monitoring. She has developed significant expertise in the Ekeland variational principle and its applications to inverse function theorems, while also contributing to statistical methods for analyzing atmospheric pollutants. Her research demonstrates a consistent trajectory from pure mathematical theory toward increasingly applied environmental and industrial problems. Her most notable publications form a comprehensive trilogy on Differential and Integral Calculus, along with specialized monographs on the Ekeland principle and environmental statistics. These works reflect her dual focus on foundational mathematical theory and practical applications in environmental science. Dr. Meghea has secured research funding from prestigious sources including the World Bank and EU programs, participating in projects such as KnowEnTech and environmental monitoring initiatives. Her international collaborations include partnerships with institutions in France and Morocco, where she has helped establish academic exchange programs. As an educator, she teaches Advanced Mathematics, Equations of Mathematical Physics, and Geometry courses while supervising seminar activities across multiple mathematical disciplines. She has initiated scientific collaboration programs between University POLITEHNICA of Bucharest and Moroccan universities, demonstrating her commitment to international academic cooperation.

• Control Theory
• Optimal Transport
• Machine Learning
• +6 more

Anqi Dong is a Postdoctoral Fellow at KTH Royal Institute of Technology. They hold a BSc in Mechanical and Automation Engineering from Harbin Institute of Technology (2017) and a PhD in Mechanical and Aerospace Engineering from the University of California, Irvine (2023), supervised by Prof. Tryphon T. Georgiou. They were a visiting scholar with Prof. Li Qiu from 2023 to 2024 and are now co-supervised by Prof. Karl H. Johansson and Prof. Johan Karlsson at KTH. Education: BSc: Mechanical and Automation Engineering, Harbin Institute of Technology (2017) PhD: Mechanical and Aerospace Engineering, University of California, Irvine (2023) Research Interests: Anqi Dong's work focuses on control theory, optimal transport, and data-driven modeling of dynamical systems. Their research applies advanced mathematical frameworks to problems in multi-agent coordination, 3D scene understanding, and biomedical imaging. Key areas include tensor-based analysis, network learning, and computational methods for system identification. Recent Publications: Their 2025 studies explore dynamic optimal transport, neural network interpretability, and vessel segmentation, while 2024 contributions address temporal hypergraphs and toll station optimization. All work emphasizes interdisciplinary applications of mathematical and computational tools.

Matteo IcardiView profile

Associate Professor

• Applied Mathematics
• Computational Fluid Dynamics
• Multiphase Flows
• +6 more

Matteo Icardi is an Associate Professor in Applied Mathematics at the University of Nottingham's School of Mathematical Sciences, appointed in October 2017. His research bridges numerical methods, mathematical modeling, and engineering applications with emphasis on complex flow systems and computational frameworks. Education: BSc and MSc in Engineering Mathematics from Politecnico di Torino PhD in Chemical Engineering from Politecnico di Torino (2012) with thesis on 'Computational models for turbulent poly-dispersed flows: LES and QBMM' Research Interests: Professor Icardi specializes in multiphase flow modeling, porous media transport, multiscale methods, uncertainty quantification, and computational fluid dynamics. His work leverages OpenFOAM-based solvers for applications in environmental fluid dynamics, lithium-ion battery simulation, and industrial transport processes. Current research focuses on model reduction techniques and physics-informed neural networks for complex systems. Publication Trends: Recent work (2023-2025) demonstrates strong focus on homogenization methods (HiPhom), topological data analysis for infrastructure resilience, and multiscale modeling of peatlands, bubble dynamics, and electrochemical transport. The research consistently addresses heterogeneous porous media using advanced computational frameworks with applications spanning environmental systems and energy storage. Scientific Awards: No scientific awards mentioned in source material Advising and Grants: Professor Icardi advises PhD students and collaborates with postdoctoral researchers. He participates in the MultiForm research group and Multiscale Modelling Research Theme, though specific grant details are not provided in the source material. Labs and Teams: Co-founder of the MultiForm research group (Multiscale Fluid Dynamics and Porous Media) and initiator of the Multiscale Modelling and Heterogeneous Media Research Theme at Nottingham, fostering interdisciplinary collaboration between Mathematics and Engineering departments.