جست‌وجوی استاد و پژوهشگر

• Partial Differential Equations
• Quantum Physics
• Mathematical Physics
• +۳ مورد دیگر

Norbert J. Mauser is a Professor of Mathematics at the University of Vienna 's Faculty of Mathematics. He serves as Director of the Wolfgang Pauli Institute and Co-Director of the Jungen Kurie at the Austrian Academy of Sciences. Mauser coordinates major EU projects like HYKE and DEASE , focusing on hyperbolic and kinetic equations, differential equations, and their applications in science and engineering. Research Interests : Mauser specializes in Partial Differential Equations (PDEs) , particularly Nonlinear Schrödinger Equations (NLS) , with applications in Quantum Physics and Computational Materials Science . His work spans mathematical modeling , numerical methods , and asymptotic analysis , addressing problems in micromagnetism , quantum correlation , and nonrelativistic limits . He explores Wigner transforms , stochastic NLS , and multi-configuration Hartree-Fock equations for quantum systems. Scientific Contributions : Grants & Projects : Principal Investigator for START Award , FWF , WWTF , and ANR-FWF projects on quantum dynamics, correlation, and nano-sensors. Collaborations : Active in international networks including CNRS Groupement de Recherche on kinetic equations and quantum physics. Publishing : Senior editor for journals like Asymptotic Analysis and Communications in Mathematical Sciences . Labs & Teams : Leads projects under Vienna Computational Materials Laboratory (ViCoM) and contributes to multi-scale modeling of nano-biosensors. His research integrates computational methods with theoretical physics, emphasizing quantum systems and nonlinear PDEs.

Bayram Bozhüyükمشاهده پروفایل

دانشیار

• Graphic Design
• Visual Communication Design
• Illustration
• +۶ مورد دیگر

Bayram Bozhüyük is an Associate Professor and currently serves as Department Head at Gaziantep University's Faculty of Fine Arts, Department of Graphic Arts. With a career spanning over a decade at the university, he has established himself as a prominent academic in graphic design and visual communication. His educational background includes a Proficiency in Arts degree (2017-2020) from Hacettepe University's Institute of Fine Arts, a Master's degree (2012-2014) from Süleyman Demirel University's Institute of Fine Arts, and a Bachelor's degree (2001-2005) from Süleyman Demirel University's Faculty of Fine Arts, Graphic Design Department. Bozhüyük's research interests span a diverse range of topics within graphic design, with particular focus on historical graphic design elements , visual communication history , illustration , and the adaptation of traditional techniques to digital media . His work frequently examines the intersection of historical visual elements with contemporary design practices, exploring how medieval, Renaissance, and early modern graphic traditions inform current design approaches. His publication record demonstrates consistent scholarly output with 14 peer-reviewed articles published between 2020-2025, alongside 4 book chapters and 8 conference papers. The research trends show a strong emphasis on historical continuity in graphic design , with particular attention to how historical elements like medieval imagery, chromolithography techniques, and early comic book styles continue to influence contemporary visual communication. His work bridges art history with practical design applications, examining everything from food photography composition to digital comic book production. Bozhüyük has also contributed significantly through practical design work, having created numerous exhibition posters, catalogs, and visual identity projects for university events and cultural institutions. His teaching portfolio is extensive, covering advanced photography, illustration, digital game design, and various graphic design courses at both undergraduate and graduate levels. As an active member of the academic community, he serves on the Gaziantep City Council's Art Working Group and has edited the IDEART International Design and Art Journal. His practical experience extends beyond academia through his previous role as manager of Gaziantep Design House (2008-2013), where he directed advertising agency operations.

Gui-Qiang G. Chenمشاهده پروفایل

استاد

• Partial Differential Equations
• Nonlinear Analysis
• Hyperbolic Conservation Laws
• +۱۲ مورد دیگر

Gui-Qiang G. Chen is a Professor at the Mathematical Institute , University of Oxford, and a Professorial Fellow of Keble College. He serves as Director of the Oxford Centre for Nonlinear Partial Differential Equations (OxPDE) , focusing on nonlinear PDEs, hyperbolic conservation laws, and their applications to fluid dynamics, geometry, and mathematical physics. His research spans Partial Differential Equations , Nonlinear Analysis , Shock Wave Theory , and Free Boundary Problems , with recent work on stochastic PDEs , geometric PDEs , and numerical analysis . His publications cover topics like transonic shocks , hypersonic flow , and compressible fluid dynamics . Gui-Qiang Chen has co-authored 15+ major publications since 2007, including research monographs on shock reflection-diffraction and Prandtl-Meyer reflection configurations , and his work appears in leading journals like Annals of Mathematics and Communications on Pure and Applied Mathematics . Awards and fellowships include: 2024 Polya Prize (London Mathematical Society) Doctor of Science (Oxford, 2024) Member of Academia Europaea (2022) Member of the European Academy of Sciences (2020) Fellow of the American Mathematical Society (2017) SIAM Fellow (2013) Chinese National Prize of Sciences (1990) He supervises DPhil/PhD students in nonlinear PDEs and related fields, and his research is supported by Oxford's mathematical infrastructure and international collaborations.

• Spatial data analysis
• Time series data
• Machine learning
• +۴ مورد دیگر

Maria Sinziiana Astefanoaei is an Assistant Professor at the IT University of Copenhagen , affiliated with the Data, Systems, and Robotics department. Her research focuses on spatiotemporal data analysis, urban computing, and graph algorithms using machine learning techniques. Research Interests: Spatial data analysis, Time series data processing, Large-scale visualizations, Machine learning, Human mobility modeling, and Embeddings. Projects: Principal Investigator for CCAI: Towards greener last-mile operations (2022-2023), contributing to cargo-bike logistics optimization, and Co-Investigator for the Pilot Hub project (2020-2022) funded by the Danish Agency for Research and Education. Publications: 2021 conference paper at CIKM '21 on spatiotemporal signal processing frameworks with neural machine learning models. Her work intersects computer science , urban logistics , and environmental sustainability , with applications in smart city technologies and multi-modal transportation systems.

Austin Tarantaمشاهده پروفایل

استاد

• Hollow core antiresonant fibres
• Hollow core optical fibre sensors
• Fibre-optic gyroscopes
• +۴ مورد دیگر

Austin Taranta serves as a Professor at the University of Southampton's Optoelectronics Research Centre (ORC), where he leads research and enterprise initiatives within the Hollow Core Fibres Group. Since joining the ORC in 2016, he has driven advancements in hollow core fibre technology for defence and space applications, authored over 50 peer-reviewed publications, secured significant research funding, and manages key industrial partnerships including a Microsoft research contract. His work bridges fundamental fibre physics with practical sensor development. His academic background includes a BSc in Chemical Engineering from the Massachusetts Institute of Technology (2006) and a PhD in Optoelectronics from the University of Southampton (2024). Prior to academia, he gained industry experience as Senior Optical Engineer at Honeywell Aerospace's Strategic Sensors Group developing fibre-optic gyroscopes. Taranta's research focuses on exploiting the unique properties of hollow core fibres for next-generation sensing systems. Key areas include: Hollow core antiresonant fibre guidance physics Gas pressure and temperature dynamics in hollow fibres Polarization behavior in nodeless antiresonant structures Novel sensor architectures for defence applications Bend-insensitive fibre designs for telecom integration Non-destructive characterization methodologies Analysis of his 2023-2024 publications reveals concentrated efforts on solving manufacturing challenges, environmental stability issues, and sensor integration barriers in hollow core fibre technology. His work demonstrates strong industry alignment through Microsoft collaborations and defence-focused applications, particularly in distributed sensing systems and next-generation gyroscopes. No specific scientific awards or medals are listed in the provided text, though his profile notes responsibility for multiple research awards and patent holdings including optical time domain reflectometry innovations. Taranta actively supervises PhD candidates Amalie Gjelsvik and Chiang Ping Saw (part-time), while managing industrial research contracts and academic partnerships. His funding portfolio includes strategic defence projects through Honeywell Aerospace and Microsoft collaborations, supporting the Hollow Core Fibres Group's translational research pipeline. He leads the Hollow Core Fibres Group within the ORC, focusing on commercializing hollow core antiresonant fibre technology through the Hollow Core Fibres Group. Current work targets polarization control, environmental resilience, and sensor integration with industry partners to develop deployable systems for aerospace and defence sectors.

Roman Franz Froschauerمشاهده پروفایل

استاد

• Software Engineering for Intelligent Automation Systems
• Human-Robot Interaction
• Control Systems
• +۷ مورد دیگر

Roman Franz Froschauer is a Professor of Production Informatics at the Upper Austria University of Applied Sciences, Research Center Wels. Since 2018, he has served as Director of Studies for the Master's program in Robotic Systems Engineering and leads the Smart Automation & Robotics research group. His career spans academic and industrial roles, including senior software development and project management at AlpinaTec Technical Products GmbH (2010-2016). Education: Ph.D. in Computer Science (2010) from Johannes Kepler University Linz; Master's in Industrial Informatics (2005) from Upper Austria University of Applied Sciences. Research Areas: Software engineering for intelligent automation systems, human-robot interaction (HRI), control systems, and applications of IEC 61499 standards. His work focuses on proactive collaboration, trajectory planning, and user-centered design for assistive robots in office and industrial settings. Scientific Activities: Active in peer-review, conference organization, and technology development. Projects include VRoboCoop (human-robot trust), MARIE (office robotics), and Autility (automated utility vehicles). Key Contributions: Frameworks for modular manufacturing (PlugBot), skill-based engineering, and intralogistics automation (ATLAS).

Nikos Giatrakosمشاهده پروفایل

استادیار

• Big Data Management
• Internet of Things (IoT)
• Distributed Systems
• +۳ مورد دیگر

Nikos Giatrakos is an Assistant Professor at the School of Electronic & Computer Engineering, Technical University of Crete, and a core member of the Software Technology and Network Applications Lab (SoftNet) . His work bridges Big Data systems, IoT, and advanced analytics, with a focus on real-time processing and scalable architectures. Previously, he served as a postdoctoral researcher at the same laboratory. Education PhD in Computer Science, University of Piraeus (2012) Postgraduate Diploma in Information Systems, Athens University of Economics and Business (2008) BSc in Computer Science, University of Piraeus (2006) Research Focus : Nikos specializes in software architectures for Big Data streaming, including Distributed Big Data Processing , Federated Machine Learning , Cloud-to-Edge Data Management , and Approximate Query Processing . His work has also advanced Complex Event Processing and Outlier Detection in decentralized environments. Scientific Contributions : His research has led to the DAG* workflow optimizer for IoT, the SuBiTO framework for real-time neural learning, and the INFORE approach for cross-platform analytics. He received the Best System Demonstration Award at ACM CIKM 2020 for INforE. Academic Leadership : Nikos teaches Object-Oriented Programming, Data Science, and Distributed Systems. He has supervised numerous European and national grants as Principal Investigator and served on program committees for top-tier conferences like SIGMOD, VLDB, and DEBS.

Ellen Eischenمشاهده پروفایل

استاد

• automorphic forms
• L-functions
• number theory
• +۵ مورد دیگر

Ellen Eischen is a Professor of Mathematics at the University of Oregon in the College of Arts and Sciences. She is currently on leave until fall 2025 while serving as a von Neumann Fellow at the Institute for Advanced Study (2024-2025). She serves on the editorial boards of Essential Number Theory and Research in Number Theory. Dr. Eischen earned her PhD in Mathematics from the University of Michigan in 2009 with a dissertation titled "p-adic Differential Operators on Automorphic Forms and Applications" advised by Christopher Skinner and Nicholas Ramsey. Her educational background includes substantial training in improvisational theater, which has significantly influenced her professional development. Her primary research focuses on automorphic forms and L-functions, with particular emphasis on algebraic, p-adic, and geometric aspects of number theory. She approaches her research with a collaborative spirit, noting that number theory "relies on many flavors of math." Beyond traditional research, she is deeply committed to mathematical creativity and public engagement, having created the "Creativity Counts" exhibit at the Jordan Schnitzer Museum of Art and serving on the Advisory Board of the Seattle Universal Math Museum since 2021. She has developed innovative teaching methods through improvisational theater techniques, documented in publications like "Improv-ing a Mathematician's Professional Skills" and "Seeds for Building Community" with C. Hsu. Dr. Eischen's scholarly output demonstrates a consistent focus on advancing the theory of automorphic forms and L-functions, with recent work extending to mathematical creativity and educational innovation. Her publications span technical mathematical research in number theory to articles exploring the creative process in mathematics and improvisational techniques for teaching, reflecting her dual commitment to mathematical rigor and accessibility. von Neumann Fellow at the Institute for Advanced Study (2024-2025) NSF research grant DMS-2302011 NSF CAREER grant DMS-1751281 Williams Fund support for educational initiatives Dr. Eischen has advised numerous graduate students including Cathy Hsu (now Assistant Professor at Swarthmore College), Jon Aycock (now Visiting Assistant Professor at UCSD), Samantha Platt (now Assistant Professor at Augsburg University), and Sean Haight (now Adjunct at Seattle University). Her current PhD students include Francis Dunn and Sidney Washburn. She has also mentored undergraduate researchers such as Abby Lewis and Nat Milnes, whose work resulted in published outreach projects and an Apple App Store application. She has received significant grant support including an NSF CAREER award and current NSF research funding for her work in number theory and mathematical education. Through her "Math and the Creative Process" course and improv workshops for mathematicians, Dr. Eischen has built interdisciplinary teams connecting mathematics with theater arts. She co-organizes major workshops including the "Integrating Research and Illustration in Number Theory" at Institut Henri Poincaré (2026) and has led sessions at the Institute for Advanced Study, MSRI, and other prestigious institutions. Her Creativity Counts project has created a lasting impact through both physical and virtual museum exhibits that showcase the creative side of mathematics.

Jan Ratajمشاهده پروفایل

استاد

• Integral Geometry
• Geometric Measure Theory
• Stochastic Geometry
• +۲ مورد دیگر

Jan Rataj is a Professor at the Faculty of Mathematics and Physics , Charles University , specializing in Integral Geometry , Geometric Measure Theory , and Stochastic Geometry . He has been affiliated with Charles University since 1991 and has held visiting positions at universities in Jena, Karlsruhe, Ulm, and Aarhus. Research interests focus on geometric probability, convex geometry, and measure theory Teaches courses on Measure and Integration Theory , Geometry , and advanced topics in geometric measure theory Scientific contributions include coauthoring 2 monographs and ~50 research papers. His work explores: Random measurable sets and perimeter analysis Stochastic modeling of microstructures Geometric functionals and invariants Applications to spatial statistics and probability theory He supervises PhD students and leads the Seminar of Stochastic Geometry , which examines topics like: Max-stable random fields Convex body reconstruction Point process analysis

• Dynamical systems and gradient flows
• Analysis of partial differential equations
• Stochastic analysis
• +۲ مورد دیگر

Arnulf Jentzen is a distinguished mathematician holding dual positions as Presidential Chair Professor at the School of Data Science and Shenzhen Research Institute of Big Data at The Chinese University of Hong Kong, Shenzhen, and as Full Professor at the Faculty of Mathematics and Computer Science at the University of Münster, Germany. His research spans multiple institutions with significant contributions across mathematical disciplines. His primary research interests include dynamical systems and gradient flows (particularly geometric properties, domains of attractions, blow-up phenomena), analysis of partial differential equations, stochastic analysis (including stochastic calculus and well-posedness analysis), machine learning (with focus on mathematics for deep learning and stochastic gradient descent methods), and numerical analysis (particularly computational stochastics and computational finance). His work demonstrates a strong interdisciplinary approach bridging pure mathematics with practical computational applications. Jentzen's publication record shows a clear trend toward machine learning applications in solving complex mathematical problems, particularly in overcoming the curse of dimensionality in high-dimensional PDEs through deep neural networks. His research group actively publishes on optimization methods like Adam, convergence analysis, and applications of deep learning to partial differential equations and optimal control problems. ICBS Frontier of Science Award in Mathematics (2024) Fellow, Lamarr Institute (2023) ERC Consolidator Grant (2022) Joseph F. Traub Prize for Achievement in Information-Based Complexity (2022) Felix Klein Prize, European Mathematical Society (EMS) (2020) Professor Jentzen advises numerous PhD students across both institutions and serves on multiple editorial boards including SIAM Journal on Numerical Analysis, Journal of Complexity, and Communications in Computational Physics. His research group at Münster and CUHK-Shenzhen focuses on developing mathematical foundations for machine learning with applications to scientific computing problems. He has received significant research funding including an ERC Consolidator Grant, supporting his interdisciplinary work at the intersection of mathematics and artificial intelligence.

Paul Kotyczkaمشاهده پروفایل

استاد

• Control Engineering
• Nonlinear Control
• Port-Hamiltonian Systems
• +۸ مورد دیگر

Paul Kotyczka is a Professor at the Technical University of Munich (TUM) in the School of Engineering and Design , Department of Automatic Control Engineering. He leads the Energy-based Modeling and Control Working Group and focuses on modeling, geometric discretization, and control of multi-physical systems, with expertise in nonlinear and passivity-based control. His work spans applications in robotics, mechatronics, and process engineering. Education : Dipl.-Ing. in Electrical Engineering (TUM, 2005), Dr.-Ing. (TUM, 2010), Habilitation (Dr.-Ing. habil., TUM, 2019). Research : Core areas include port-Hamiltonian systems, predictive control of active chassis, structural mechanics modeling, and numerical methods for control. His projects address autonomous driving, distributed parametric systems, and passivity-based control of switching nonlinear systems. Awards : Held a Marie Sklodowska-Curie Fellowship (2015–2017). Grants : Leads DFG projects (e.g., HermInE, INFIDHEM), MSCA-IF, and Franco-German Doctoral Program on port-Hamiltonian systems. Labs : Head of the Energy-based Modeling and Control group at TUM, collaborating internationally (e.g., IIT Bombay, Grenoble INP, LCIS France).

Roie Levinمشاهده پروفایل

استادیار

• Approximation Algorithms
• Online Algorithms
• Submodular Functions
• +۵ مورد دیگر

Roie Levin is an Assistant Professor at Rutgers University's Department of Computer Science. He received his PhD in Algorithms, Combinatorics and Optimization from Carnegie Mellon University in 2022, advised by Anupam Gupta. Prior to that, he worked at the Allen Institute for Artificial Intelligence (2015-2017) and earned dual BSc degrees in Computer Science/Applied Mathematics and Mathematics from Brown University (2015). Before joining Rutgers, he was a Fulbright Postdoctoral Fellow at Tel Aviv University under Niv Buchbinder. Current Role: Assistant Professor in Computer Science Academic Training: PhD (2022) CMU, BSc (2015) Brown University Postdoctoral: Fulbright Fellow at Tel Aviv University Levin's research focuses on approximation algorithms for uncertain environments (online/dynamic/streaming models) and submodular function optimization. His work spans theoretical foundations and practical implementations across distributed systems, geometric constraints, and reinforcement learning paradigms. Teaching includes graduate and undergraduate algorithms courses (CS 344, CS 513) with emphasis on problem-solving techniques, computational complexity, and modern algorithmic trends. His publications showcase expertise in online algorithms, submodular optimization, and approximation theory with applications in clustering, caching, and machine learning. The 2025 articles demonstrate continued exploration of online consistency and contention resolution, while 2023-2024 works focus on submodular optimization under uncertainty and dynamic environments. Earlier publications (2015-2017) cover semantic parsing, geometric approximation, and planar graph optimization. Fulbright Postdoctoral Fellow Levin's research connects theoretical guarantees with practical implementations, bridging classical algorithm design with modern machine learning applications. His recent work explores primal-dual methods in online settings and robust subspace approximation techniques for streaming data environments.

Uri Hassonمشاهده پروفایل

استاد

• Neuroscience
• Natural Language Processing
• Cognitive Science
• +۷ مورد دیگر

Uri Hasson is a Professor at the Princeton Neuroscience Institute, Princeton University, focusing on the neural basis of brain-to-brain human communication, natural language processing, and language acquisition in real-world contexts. His lab challenges traditional experimental models by using deep learning to decode cognition during natural stimuli. Research Highlights: Investigates neural alignment in speaker-listener interactions. Develops computational tools to model language processing in everyday conversations. Explores shared principles between biological and artificial neural networks. Recent Article Trends: 2025–2023 publications emphasize deep language models matching neural responses, event segmentation via Bayesian surprise, and multibrain convergence during storytelling. Key keywords: Neuroscience, Natural Language Processing, Deep Learning, Cognitive Modeling . Awards: NIH Pioneer Award Lab & Collaborations: The Hasson Lab integrates neuroimaging (ECoG, fMRI) with large-scale datasets and AI to study real-life cognition. Collaborations span psychology, computational neuroscience, and machine learning.

• Optimal Transport
• Martingale Theory
• Mathematical Finance
• +۳ مورد دیگر

Mathias Beiglböck is a full Professor at the Department of Mathematics within the Faculty of Mathematics at the University of Vienna. His research spans multiple areas of mathematical analysis with a strong focus on probability theory and its applications to finance and other fields. With over 60 publications spanning from 2009 to 2024, Beiglböck has established himself as a leading researcher in his field. Beiglböck's primary research interests center around optimal transport theory, martingale theory, and their applications to mathematical finance. His work explores the deep connections between probability theory and financial mathematics, particularly in areas such as option pricing, risk management, and stochastic processes. His research also extends to epidemiological modeling, as evidenced by his contributions to SARS-CoV-2 research during the pandemic. Analysis of his recent publications (2022-2024) reveals a strong focus on advancing the theoretical foundations of optimal transport and martingale theory while finding novel applications in finance and data science. His work often bridges pure mathematical theory with practical applications, particularly in financial modeling and risk assessment. The high citation counts across his publications (some exceeding 100 citations) indicate significant impact in his field. Beiglböck has collaborated extensively with researchers across Europe, particularly with scholars from France, Austria, and the UK. His work on the COVID-19 pandemic demonstrates his ability to apply mathematical expertise to pressing real-world problems, contributing to public health policy through rigorous quantitative analysis.

• Computational Physics
• Density Functional Theory (DFT)
• Atomistic Simulations
• +۹ مورد دیگر

Jaakko Akola is a Professor in the Department of Physics at the Norwegian University of Science and Technology (NTNU). His research focuses on computational materials science, particularly density functional theory (DFT) and atomistic simulations of materials, nanoparticles, molecules, and interfaces. He leads significant projects such as "SIDI" (inoculation in cast iron), "Infinity-RETIS" (chemical rare events), and "AllDesign" (rational alloy design), alongside coordinating EU-funded initiatives like "CritCat" for catalyst development. The Materials Theory group under Akola employs DFT, molecular mechanics, and Monte Carlo methods to explore atomic-scale structures and functions in technological applications. Key research areas include platinum-free catalysts for hydrogen energy, amorphous semiconductors for memory devices, noble metal nanoparticles in biological environments, and alloy design for cast iron and aluminum. Recent work integrates machine learning to advance theory-driven material design, reducing reliance on experimental trial-and-error. Akola's publications highlight advancements in hydrogen evolution catalysis, phase-change memory materials, and alloy precipitation. His projects often involve interdisciplinary collaborations with experimental teams. He teaches Quantum Physics 1 (FY2045) and Computational Physics (TFY4235) at NTNU, reflecting his commitment to education alongside research.