جست‌وجوی استاد و پژوهشگر

• Quantum Transport
• Topological Insulators
• Superconductivity Theory
• +۳ مورد دیگر

Kajetan Fijalkowski is a Researcher at the University of Würzburg, affiliated with the Chair of Experimental Physics III within the Faculty of Physics and Astronomy. His work focuses on quantum transport phenomena, topological insulators, and superconductivity theory. He is part of the Institute for Topological Insulators and contributes to advanced research in molecular beam epitaxy for material synthesis. His research explores quantum anomalous Hall effects, topological magnetic devices, and superconductivity under varying conditions. Dr. Fijalkowski’s articles emphasize metrology applications, quantum standards, and the interplay between magnetism and topology in materials. His recent work includes studies on voltage-balanced devices for resistance metrology and macroscopic quantum tunneling in topological ferromagnets. While no awards or grants are explicitly listed, his contributions to precision measurements of quantized resistance and axion insulator theory highlight his expertise in topological quantum systems.

Lukas Paul Achatius Galkeمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing
• Deep Learning
• +۱۰ مورد دیگر

Lukas Paul Achatius Galke is an Assistant Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he conducts research in machine learning, natural language processing, and cognitive modeling. He is affiliated with the IMADA research group and holds an ORCID identifier (0000-0001-6124-1092), indicating active academic engagement. His research interests lie at the intersection of deep learning and linguistic cognition, with a focus on emergent communication, language modeling, and graph neural networks. Key areas include compositional language structure, quantization-aware training, and the generalization abilities of large language models across languages. His work combines computational methods with insights from cognitive science and neuroscience. The recent publications (2024–2025) highlight a strong trend in analyzing how neural networks mimic human language learning, assess scientific convergence through bibliographic data, and optimize model efficiency. His work spans both theoretical and applied domains, including automated analysis of research fields and cognitive modeling of language acquisition. Lukas collaborates with a diverse group of researchers and co-authors, indicating active supervision and mentorship. While no formal awards are listed, his publications in high-impact journals like Nature Communications and IEEE Transactions on Engineering Management reflect significant scholarly contributions. He is involved in research teams focusing on data science and AI, particularly within the IMADA group. His work on graph-structured metadata and neural network behavior suggests ongoing projects in scientific intelligence and cognitive AI systems.

Andreas Fringمشاهده پروفایل

استاد

• Mathematical Physics
• Integrable Quantum Field Theories
• Non-Hermitian Hamiltonian Systems
• +۹ مورد دیگر

Professor Andreas Fring is a faculty member at the Department of Mathematics, City, University of London, holding the title of Professor of Mathematical Physics since 2008. He completed his PhD in Theoretical Physics at Imperial College London (1992) and held postdoctoral positions in São Paulo, Swansea, and Berlin before joining City University London. His research spans integrable quantum field theories, non-Hermitian Hamiltonian systems, PT-symmetry, and noncommutative space-time structures. PhD: Imperial College London, 1992 MSc: Imperial College London, 1989 BSc: University of London, 1988 His work focuses on mathematical physics , particularly PT-symmetric quantum mechanics , non-Hermitian Hamiltonians , and integrable systems . He has contributed to understanding complex solitons, quantum brachistochrones, and noncommutative spaces with minimal uncertainties. His recent research explores time-dependent non-Hermitian systems and their applications in gauge theories. The 15 most recent articles demonstrate expertise in non-Hermitian quantum systems , integrable field theories , and noncommutative geometry . Key subfields include PT-symmetry , soliton dynamics , time-dependent Hamiltonians , Goldstone bosons , minimal length uncertainty , and Kac-Moody algebras . He has supervised PhD students such as Takanobu Taira, Rebecca Tenney, and Julia Cen, focusing on topics like non-Hermitian quantum field theories and PT-symmetric integrable systems. No scientific awards are explicitly mentioned in the provided texts.

Christophe Goléمشاهده پروفایل

استاد

• Dynamical Systems
• Mathematical Biology
• Symplectic Geometry
• +۵ مورد دیگر

Christophe Golé is a full-time Professor in the Department of Mathematical Sciences at Smith College . He has held academic positions at the University of Minnesota, ETH Zurich, SUNY Stony Brook, and the University of California, Santa Cruz. His research bridges Dynamical Systems and Mathematical Biology , with a focus on Phyllotaxis and Fibonacci Spirals in plant patterns. Golé co-founded the NSF-funded 4 College Biomath Consortium , which evolved into the Five College Biomathematical Sciences Certificate Program . He directed the Smith Geneva and Paris Junior Year Abroad programs in 2003-04 and 2014-15, respectively. Ph.D. , Boston University M.A. , University of California, Santa Cruz B.A. , Université Paris Golé's work on Phyllotaxis explores the mathematical underpinn'tion of Fibonacci spirals in plants and deviations from them. His earlier research in Hamiltonian Systems and Symplectic Twist Maps advanced understanding of periodic orbits and ghost tori. Articles span Mathematical Biology , Dynamical Systems , and Sub-Riemannian Geometry , with trends in Pattern Formation , Biological Modeling , and Hamiltonian Dynamics . Collaborations include the Smith Botanic Garden , where he co-created the exhibit Plant Spiral: Beauty You Can Count On . His courses include Introduction to Real Analysis , Topics in Continuous Applied Math: Dynamical Systems , and Topology . Golé is active in advising students through projects like zebrafish neurogenesis and crab-snail antagonism . He also participates in the Smith French and Spanish Tables and is involved in cross-disciplinary initiatives at the intersection of mathematics and biology.

• Semantic Web
• Knowledge Graphs
• Machine Learning
• +۳ مورد دیگر

Ahmet Soylu is a researcher at Oslo Metropolitan University , Norway, with a focus on Semantic Web , Knowledge Graphs , and Machine Learning applications in Cloud Computing and Smart Manufacturing . His work bridges semantic technologies with data-driven solutions for industrial contexts, particularly in collaboration with Bosch . Key research themes: Knowledge Graph Embeddings, Cloud Cost Optimization, Industrial Data Analytics Collaborations: Dumitru Roman, Evgeny Kharlamov, Radu Prodan Recent publications (2024–2025) explore hyperbolic knowledge graph embeddings, sustainable LLM inference, and cloud storage optimization. These works integrate semantic modeling with ML for edge-cloud systems and industrial data extrapolation. His methodology emphasizes graph-based approaches for cloud cost modeling, microservice scheduling, and AI innovation discovery in open-source repositories. Applications include welding quality monitoring, maritime supply chain optimization, and semantic ML pipelines.

• Deterministic Cauchy theory for nonlinear dispersive PDEs
• Stability and asymptotic behavior of solutions
• Invariant measures and probabilistic well-posedness
• +۲ مورد دیگر

Tristan Robert is a Lecturer at the University of Lorraine, Faculty of Science and Technology, within the IUT Nancy Charlemagne. He is affiliated with the Institut Élie Cartan de Lorraine (IECL) and the Partial Differential Equations research team. His research centers on nonlinear dispersive partial differential equations, with emphasis on deterministic Cauchy theory, stability of solutions, invariant measures, and probabilistic well-posedness. He also investigates singular stochastic PDEs and their connections to Euclidean quantum field theory through probabilistic methods. Analysis of his 2021-2025 publications reveals a concentrated focus on stochastic and deterministic dispersive PDEs, particularly Schrödinger and Ginzburg-Landau equations. His work demonstrates increasing use of probabilistic frameworks to address well-posedness and invariant measures in singular settings, with strong connections to quantum field theory applications. Scientific Awards: None mentioned in source material. Advising and Grants: No information provided regarding students, doctoral advisees, or research funding. He operates within the Partial Differential Equations team at IECL, which collaborates with INRIA teams (SIMBA, SPHINX, PASTA) on projects spanning analysis, PDEs, geometry, probability, and statistics across the IECL research spectrum.

Mohammad Rastegariمشاهده پروفایل

استادیار مدعو

• Computer Vision
• Efficient Machine Learning
• Model Compression
• +۳ مورد دیگر

Mohammad Rastegari serves as a senior technical manager in Apple's AI/ML organization and holds an affiliate assistant professor position in the Computer Science and Engineering Department at the University of Washington. Previously, he was a research scientist at the Allen Institute for AI (AI2) where he contributed to the PRIOR team, and co-founded XNOR.ai as Chief Technology Officer. He completed his Ph.D. at the University of Maryland under the supervision of Professor Larry S. Davis, focusing on computer vision and machine learning. His research centers on efficient deep learning with major contributions in binary neural networks (notably XNOR-Networks for resource-constrained devices) and model compression . Current work extends to large language model efficiency through weight clustering, token pruning, and memory optimization while maintaining strong foundations in computer vision. Analysis of his recent publications reveals a strategic shift toward optimizing transformer-based architectures across vision and language domains, with consistent emphasis on deployable solutions for edge computing and real-world applications. He co-founded XNOR.ai (acquired by Apple) and contributed to AI2's PRIOR initiative, demonstrating a career trajectory bridging academic innovation and industrial implementation in efficient AI systems.

• Quantum Materials
• Superconductivity
• Nanoscale Engineering
• +۳ مورد دیگر

Elina Zhakina is a Post Doctoral Research Scientist at the Max Planck Institute for Chemical Physics of Solids in Dresden, Germany, where she conducts research in the Spin3D group focused on three-dimensional magnetic systems. Her work centers on microstructuring and scanned probe spectroscopy of quantum materials, with emphasis on superconducting nanoarchitectures and electron irradiation effects. Her academic background includes: MSc in Physics from Moscow State University (2016) PhD from the Max Planck Institute for Chemical Physics of Solids (2016-2021) under the supervision of Prof. Andy Mackenzie Dr. Zhakina specializes in quantum materials, particularly superconductivity and transport phenomena in delafossite metals such as PdCrO2 and PdCoO2. Her experimental approach combines nanofabrication, high-energy electron irradiation, and scanned probe techniques to investigate ballistic-ohmic transitions, vortex dynamics, and Planckian scattering behavior. Her research bridges fundamental condensed matter physics with applied nanoscale device engineering, revealing how defect engineering manipulates quantum states in low-dimensional systems. Analysis of her 15 most recent publications (2019-2025) shows consistent focus on three-dimensional superconducting architectures and electron irradiation effects in quantum materials. Key trends include the development of reconfigurable superconducting nanostructures, investigation of nonlocal transport in microstructured geometries, and systematic studies of Planckian behavior in high-conductivity oxides. Her work demonstrates how controlled defect introduction via electron irradiation serves as a powerful tool for probing fundamental transport mechanisms. No scientific awards are documented in the available information. Dr. Zhakina has no listed advisees or formal mentoring roles, and grant details are not specified. Her research is conducted within the Spin3D group, which explores novel magnetic phenomena through advanced microstructuring techniques and spectroscopic methods.

• Statistical Signal Processing
• Communication Systems
• RF Hardware Analysis
• +۷ مورد دیگر

Thomas Eriksson is a Professor at the Communication Systems department of Chalmers University, Sweden. He serves as Vice Head of the Department of Electrical Engineering, overseeing undergraduate and Master's education. His research focuses on statistical signal processing and communication systems, with particular emphasis on 'Dirty RF' processing—analyzing and compensating for hardware imperfections such as nonlinear amplifiers, phase noise, and I/Q imbalance. He also explores feedback mechanisms for channel state information (CSI) using quantization or hybrid ARQ techniques. Specialized in wireless communication and MIMO systems Conducts research on hardware impairments and signal processing Active in mm-Wave and 6G technology development His recent publications address challenges in millimeter-wave communication, digital predistortion, and phase noise compensation. Thomas leads projects involving testbeds for advanced communication systems and collaborates on technologies for energy-efficient and high-performance transmitters.

• Atomic theory
• Spin-angular integration
• Atomic structure calculations
• +۷ مورد دیگر

Habil. dr. Gediminas Gaigalas serves as Chief Researcher at the Institute of Theoretical Physics and Astronomy (ITPA) within Vilnius University's Faculty of Physics. His work bridges theoretical atomic physics with astrophysical applications, particularly in understanding heavy element spectroscopy for neutron star merger events. Gaigalas maintains active international collaborations, including the Lithuanian-Japanese project on gravitational wave sources, and contributes to major computational atomic physics initiatives. His research focuses on multi-electron atomic theory , relativistic atomic structure calculations , and computational methods for atomic spectroscopy. Specializing in spin-angular integration techniques and configuration interaction methods, Gaigalas has developed advanced computational approaches for modeling heavy elements. His work with the GRASP (General Relativistic Atomic Structure Package) computational framework has been particularly influential in the field, enabling precise calculations of atomic properties for elements critical to understanding kilonova phenomena. Analysis of Gaigalas's recent publications reveals a strong emphasis on astrophysical applications of atomic data , particularly for neutron star merger events (kilonovae). His work spans both theoretical development of computational methods and practical applications to spectroscopic problems, with particular focus on lanthanides and other heavy elements. The research demonstrates increasing sophistication in handling relativistic effects and electron correlations in complex atomic systems. Adolfas Jucys Award of the Lithuanian Academy of Sciences (2022) for series of works 'Second quantization and additional symmetries in atomic physics: theory and applications' Vilnius University Rector's science award (2018) Research Award of the Lithuanian Academy of Sciences (2014) Guest Professor of Lanzhou Northwest Normal University (2007) Letter of Appreciation from the President of Lithuania (2005) Gaigalas has supervised numerous doctoral students including L. Radžiūtė, J. Grumer, P. Rynkun, O. Sharf, and T. Žalandauskas, with postdoctoral supervision of L. Radžiūtė and P. Rynkun. He leads the Lithuanian-Japanese project 'Theoretical Studies of Structure and Properties of Heavy Elements Toward Identification of Gravitational Wave Sources' funded by the Research Council of Lithuania. His teaching spans all academic levels, from BSc courses in atomic and nuclear physics to PhD-level courses on symbolic programming and atomic theory. As an active member of the international atomic physics community, Gaigalas participates in the 'International Collaboration on Computational Atomic Structure' and serves on the editorial board of the journal Atoms. His research group focuses on developing computational methods for atomic structure calculations with applications to astrophysics, particularly in providing accurate atomic data for modeling kilonova emissions from neutron star mergers.

David Maloneمشاهده پروفایل

استاد

• Mathematical modeling of wireless networking
• Network measurement
• IPv6
• +۸ مورد دیگر

David Malone is a Professor and Director of the Hamilton Institute at Maynooth University's Faculty of Science & Engineering, Department of Mathematics & Statistics. He received his BA(mod), MSc and PhD degrees in mathematics from Trinity College Dublin in 1996, 1997 and 2000 respectively. During his postgraduate studies, he became a member of the FreeBSD development team. After working at Corvil Networks, he joined the Dublin Institute of Technology as a senior researcher in 2001 before moving to Maynooth University's Hamilton Institute in 2004. His research interests span mathematical models of wireless networking, network measurement, IPv6, WiFi, PLC, password use, security, 802.11 protocols, Internet measurement, time keeping, and the evolution of the Irish secondary education system. Malone has made significant contributions to networking literature and is the co-author of O'Reilly's 'IPv6 Network Administration'. His recent publications demonstrate strong activity across multiple domains including password security, network measurement, IPv6 deployment, and authentication systems. Malone's work shows a consistent focus on practical networking problems with theoretical foundations in mathematics. His research has evolved from fundamental mathematical work to applied networking research with significant real-world impact. As an academic supervisor, Malone has guided numerous PhD and Master's students through their research, with recent graduates including Hazel Murray (PhD, 2021), Peter Keane (MSc, 2020), and Ashley Sheil (PhD, 2024). His supervision record spans topics in networking, security, and mathematical applications. Malone maintains active involvement in the Hamilton Institute, where he directs research activities focusing on mathematical and statistical approaches to complex systems. His work bridges theoretical mathematics with practical networking applications, creating a unique interdisciplinary research profile.

Mohammadreza MOUSAVI-KALANمشاهده پروفایل

استادیار

• Statistical Machine Learning
• Transfer Learning
• Optimization Theory
• +۲ مورد دیگر

Mohammadreza MOUSAVI-KALAN is an Assistant Professor of Statistics at CREST-ENSAI. Previously, he was a postdoctoral fellow in the Department of Statistics at Columbia University. He received his Ph.D. in Electrical Engineering from the University of Southern California (USC) and his B.Sc. from Sharif University of Technology. Dr. MOUSAVI-KALAN's research focuses on theoretical foundations at the intersection of statistics and distributed computing. His primary interests include statistical machine learning, transfer learning, optimization theory, and distributed computing systems. He investigates how to design efficient algorithms that can leverage knowledge across related tasks while providing rigorous theoretical guarantees for learning procedures. His work addresses fundamental questions about sample complexity, computational efficiency, and statistical performance in modern machine learning settings. His publication record reveals a clear research trajectory from foundational work on distributed optimization (2018-2019) toward specialized topics in transfer learning and statistical hypothesis testing (2020-2025). A consistent theme across his work is establishing theoretical limits (minimax bounds, rate analyses) for practical machine learning problems. His recent publications focus on outlier detection, Neyman-Pearson classification frameworks, and transfer learning theory, demonstrating evolution toward more specialized statistical learning problems with practical applications. Dr. MOUSAVI-KALAN has established strong collaborative ties with researchers at USC, including Mahdi Soltanolkotabi, Salman Avestimehr, and Songze Li. His most influential work includes the Lagrange coded computing framework for distributed systems, which addresses critical challenges in resiliency, security, and privacy. His research bridges theoretical computer science, statistical learning theory, and practical distributed systems challenges, with implications for secure and efficient large-scale machine learning applications.

Matthieu Cordمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Natural Language Processing
• +۶ مورد دیگر

Matthieu Cord is a Professor at Sorbonne University, leading research in the MLIA (Machine Learning and Artificial Intelligence) team within the Institute for Intelligent Systems and Robotics (ISIR). His work focuses on advancing transformers, multimodal learning, and neural network optimization. Research Interests: Vision-language models, multimodal transformers, efficient neural network training, and compression techniques. Article Trends: Recent publications explore in-context learning for multimodal models, diffusion-based pose estimation, and quantization methods for edge AI deployment. Laboratory Affiliation: Active in MLIA, contributing to foundational AI research with applications in computer vision and NLP.

Fabrice Rastelloمشاهده پروفایل

پژوهشگر

• Automatic Parallelization
• Compiler Back-End Optimization
• Static Single Assignment (SSA)
• +۴ مورد دیگر

Fabrice Rastello is a leading researcher at Inria, France, where he serves as the leader of the CORSE (Compiler Optimization and Runtime SystEms) research team. His expertise spans automatic parallelization and compiler back-end optimization, with significant contributions to Static Single Assignment (SSA) form theory and register allocation techniques. Affiliated with University Grenoble Alpes - Inria - CNRS - Grenoble INP - LIG, he maintains an active research profile with continuous publications in top-tier conferences including PLDI, CGO, and PPoPP. His research interests focus on combining theoretical foundations with practical applications in compiler design. Key areas include automatic parallelization techniques (building on his PhD work on tiling as a loop transformation), compiler back-end optimization, and the emerging field of hybrid compilation that bridges static compilation with runtime techniques. He has made significant contributions to register allocation for JIT compilation in the context of SSA properties, advising three PhD students on related topics. Analysis of his recent publications reveals a strong trend toward data movement optimization, with numerous papers on I/O complexity bounds, cache modeling, and microarchitectural performance analysis. His work spans both theoretical computer science and practical applications, including contributions to healthcare technology during the pandemic with the eSpiro ventilator project. He has also explored intersections between compiler techniques and machine learning, particularly in using ML for performance modeling. Fabrice Rastello serves on program committees for major conferences in the field, including PLDI, CGO, and CC, demonstrating his standing in the research community. His 2022 book 'SSA-based Compiler Design' as editor further establishes his authority in compiler theory and practice. His recent work shows continued innovation in compiler techniques, with publications in 2024 demonstrating ongoing research activity across multiple domains including cache modeling, program analysis, and machine learning applications.

• Topological Dynamical Systems
• Ergodic Theory
• Classical and Quantum Stochastic Processes
• +۳ مورد دیگر

Artur Siemaszko is a Professor in the Faculty of Mathematics and Computer Science at the University of Warmia and Mazury in Olsztyn. He serves as a promoter for doctoral candidates and specializes in topological dynamical systems, ergodic theory, and classical/quantum stochastic processes. Department: Department of Analysis and Differential Equations Email: artur@uwm.edu.pl Research Interests: Topological Dynamical Systems Ergodic Theory Classical and Quantum Stochastic Processes Group Theory (Modular Groups) Scientific Contributions: His research spans integrability in geometric structures, spectral quantization of random walks, and the interplay between algebraic and topological properties in dynamical systems. Articles highlight applications in mathematical physics, computer science, and epidemiology. Grants & Resources: He confirms funding availability and infrastructure support for doctoral projects through the Department of Analysis and Differential Equations.