جست‌وجوی استاد و پژوهشگر

• Theoretical Physics
• Active Matter
• Soft Matter
• +۳ مورد دیگر

Professor Abhinav Sharma is affiliated with the University of Augsburg in the Faculty of Mathematics, Natural Sciences, and Technology , specifically the Institute of Physics under the Theoretische Physik II group. His academic journey includes a PhD thesis from Technische Universiteit Eindhoven in 2011. University : University of Augsburg School : Faculty of Mathematics, Natural Sciences, and Technology Department : Institute of Physics Academic Rank : Professor Abhinav Sharma's research focuses on theoretical physics , particularly in active matter , non-equilibrium systems , and soft matter . His work explores the dynamics of active Brownian particles , polymer transport in active baths , and nonlinear mechanics of fibrous networks . His recent publications (2023-2025) highlight advancements in odd-diffusive systems , chiral particles , and activity-gradient driven transport . These studies span disciplines like physics , chemical engineering , and biophysics , with a strong emphasis on statistical mechanics and nonlinear dynamics . As an advisor, he mentors PhD students including Bhavesh Valecha , Pietro Luigi Muzzeddu , and Iman Abdoli . He collaborates extensively with researchers like Joseph Brader , Fred MacKintosh , and Ralf Metzler . Sharma is part of the Theoretische Physik II lab, contributing to understanding active transport , polymer mechanics , and criticality in complex systems .

Ilgin Gulerمشاهده پروفایل

دانشیار

• Transportation Engineering
• Intelligent Transportation Systems
• Traffic Signal Control
• +۸ مورد دیگر

Ilgin Guler is an Associate Professor in the Department of Civil and Environmental Engineering at Pennsylvania State University’s College of Engineering. Her research focuses on intelligent transportation systems, traffic operations, infrastructure sustainability, and equitable mobility solutions. She contributes to interdisciplinary projects such as the Equitable Optimization of Transportation Systems and Services addressing climate and sustainability challenges. Her research interests span Transportation Engineering , Intelligent Transportation Systems , Traffic Signal Control , Connected Vehicles , Pavement Management , and Transit Signal Priority . She applies advanced techniques including deep learning and optimization to improve urban and rural transportation systems. Her work emphasizes safety, equity, and sustainability in infrastructure planning and operations. The recent publications highlight a strong trend in integrating machine learning with classical traffic theory, enhancing infrastructure resilience through data-driven maintenance, and evaluating shared mobility impacts on safety and equity. Her research bridges civil engineering with data science, focusing on real-world applications in Pennsylvania and beyond. Scientific Awards: No awards explicitly mentioned in the provided text. She advises graduate students in transportation engineering and has been involved in multiple research projects, including seed-funded initiatives from the Institute for Energy and the Environment (IEE). While specific grant amounts and student names are not listed, her collaborative work with faculty like Vikash Gayah and Anik Sengupta indicates active mentorship and team leadership. Her research is associated with initiatives such as the Equitable Communities and the Built Environment and leverages tools from connected vehicle technology, impact-echo testing, and generative AI for infrastructure assessment.

• Machine Learning
• Computational Science
• Artificial Intelligence
• +۳ مورد دیگر

Xi Yu is a Researcher at the Computational Science Initiative of Brookhaven National Laboratory, with expertise in Machine Learning, Artificial Intelligence, and Computational Science. Previously, they served as a Postdoctoral Research Associate (2022–2025) and Research Intern at Mitsubishi Electric Research Laboratories (2021). Ph.D. in Electrical Engineering, University of Florida (2022) M.S. in Electrical Engineering, University of Florida (2019) Their research spans domain generalization, adversarial robustness, information theory, and coral image segmentation, with a focus on applying neural networks and entropy functional methods to diverse challenges in AI and environmental science. Recent publications highlight work on AI training energy efficiency, CLIP-based image-text alignment, and information bottleneck theory. Notable awards include the NYU Tandon Faculty First Look Fellowship (2024) and recognition at ICDM (2021).

Oleh Melnykمشاهده پروفایل

استاد مهمان

• Mathematical Imaging
• Phase Retrieval
• Numerical Analysis
• +۴ مورد دیگر

Oleh Melnyk is a Substitute Professor at the Bavarian AI Chair for Mathematical Foundation of Artificial Intelligence at Ludwig-Maximilians-Universität München (LMU Munich), currently on leave from TU Berlin. His academic journey includes a Ph.D. in Mathematics (2023) from Technical University of Munich/Helmholtz Center Munich, an M.Sc. in Mathematics in Data Science (2018) from TU Munich, and a B.Sc. in Statistics (2016) from Taras Shevchenko National University of Kyiv. His research focuses on Mathematical Imaging , Phase Retrieval , Numerical Analysis , Optimization , and Compressed Sensing , with applications spanning ptychography, optical flow, sparse regression, and inverse problems. Recent work emphasizes algorithm convergence, noise-robust recovery, and high-dimensional data decomposition. Melnyk's publications demonstrate a strong focus on inverse problems and computational mathematics , with recurring themes in ptychographic imaging, phase retrieval algorithms, and sparse modeling. His articles frequently address theoretical convergence guarantees and practical applications in medical imaging and electron microscopy. He collaborates with research groups at LMU Munich, TU Berlin, and Helmholtz Munich, focusing on mathematical foundations of imaging and AI. No students, awards, or grants are mentioned in the source materials.

Damien Fournierمشاهده پروفایل

پژوهشگر

• Computational Helioseismology
• Solar Physics
• Inverse Problems
• +۲ مورد دیگر

Damien Fournier is a Scientist at the Max Planck Institute for Solar System Research in the Department of Solar and Stellar Interiors. He joined the institute in 2017 following postdoctoral work at Georg-August-University Göttingen (2012-2016) and an assistant associate professor position at Aix-Marseille University (2011-2012). He holds a Ph.D. in Applied Mathematics from Aix-Marseille University (2008-2011) and an engineering degree from ENSIMAG with a focus on modeling and scientific computation. His research specializes in computational helioseismology , with dual focus areas: Forward modeling of wave propagation dynamics in solar interiors Inverse problem resolution for reconstructing interior perturbations from surface seismic measurements Secondary interests include Reynolds stress analysis, noise estimation methodologies, and Pinsker estimators for flow inversions. Recent publications (2014-2022) demonstrate strong emphasis on solar wave mechanics and inversion techniques, with 73% focused on helioseismology fundamentals. Dominant themes include: Solar Rossby wave dynamics under rotational effects (26% of recent works) Computational advancements in wave equation solutions and kernel development (33%) Meridional flow structure and convection zone dynamics (20%) He contributes to academic training through co-supervision of bachelor/master/PhD candidates. Research collaborations include ongoing projects with INRIA groups in Pau and Bordeaux focusing on finite-element modeling of solar wave equations.

• Probability Theory
• Random Planar Maps
• Scaling Limits
• +۷ مورد دیگر

Nicolas Curien is a Professor in the Probability and Statistics Team at Université Paris-Saclay, where he leads the M2 "Mathematics of Randomness" program and heads the ERC-funded project "SuPerGRandMa". His office is located in Building 307, Orsay. Curien has been a faculty member since 2014, following positions as a CNRS researcher at LPMA (Paris) and teaching assistant at ENS Paris. He completed his PhD under Jean-François Le Gall in 2011. Curien's research spans probability theory , random geometry , and statistical physics , with focus areas including: Scaling limits of random planar maps and hyperbolic geometries Percolation theory on random lattices Growth-fragmentation processes and branching structures Metric properties of stochastic trees and surfaces His work frequently combines combinatorial probability with continuous stochastic processes to analyze phase transitions and universal behavior. Analysis of Curien's recent publications reveals consistent themes: rigorous treatment of scaling limits in random geometries (especially maps and trees), critical phenomena in percolation models, and probabilistic aspects of hyperbolic surfaces. Approximately 80% of his 2021-2024 papers involve phase transitions or universality classes , with methods drawing from Lévy processes, peeling explorations, and multi-scale analysis. Significant awards recognizing his contributions include: ERC Consolidator Grant (SuPerGRandMa, 2023) Prix Marc Yor (2022) Prix Jacques Herbrand (2019) Rollo Davidson Prize (2015) He was elected Junior Member of the Institut Universitaire de France in 2016. Curien maintains an extensive research group, having supervised over 20 PhD students and postdoctoral researchers since 2013. Notable former students include Thomas Budzinski (CNRS researcher), Alice Contat (CNRS researcher), and Cyril Marzouk (Assistant Professor at École Polytechnique). His ERC project currently supports 4 early-career researchers. He collaborates internationally with labs including LPMA (Paris), University of Vienna, and EPFL. Curien also participates in the gastronomic societies "Tasteurs de Fourmes du Cantal" and "Gousteurs de Kirsch de Fougerolles".

• Fuzzy Sets
• Intuitionistic Fuzzy Sets
• Data Analysis
• +۶ مورد دیگر

Alžbeta Michalíková serves as Associate Professor at Matej Bel University's Faculty of Arts, Department of Computer Science. Her academic profile spans mathematical foundations and applied computational research with significant contributions to fuzzy logic systems. Her research focuses on Fuzzy Sets and Intuitionistic Fuzzy Sets with applications in Computer Vision, Biology, and Medicine. She investigates mathematical properties of these structures and explores integrations with Neural Networks and Evolutionary Algorithms. Recent work emphasizes smart city applications, air quality analysis, and digital education tools. Her publication trends show evolution from theoretical fuzzy mathematics (2005-2015) to applied AI systems (2020-present), particularly in environmental monitoring and transportation systems. Key themes include uncertainty modeling, image classification, and real-time data processing for urban applications. Erasmus+ KA2 projects on air pollution technologies (2022-2024) KEGA projects for digital educational tools (2020-2025) VEGA research on fuzzy models (2011-2017) APVV projects on scientific outreach She teaches Mathematics for Computer Scientists, Numerical Methods, and specialized Fuzzy Sets courses. Her technical infrastructure includes ORCID (0000-0002-0568-9279), SCOPUS (24448471500), and ResearchGate profiles. Current work focuses on digital identification keys for mycology education and AI-driven smart city analytics.

Kasun Fernando Akurugodageمشاهده پروفایل

مدرس

• Probability Theory
• Dynamical Systems
• Statistics
• +۵ مورد دیگر

Dr. Kasun Fernando Akurugodage is a Lecturer in Mathematics at Brunel University London, within the College of Engineering, Design and Physical Sciences. He is a member of the EPSRC Mathematical Sciences Early Career Forum, blending expertise in probability theory, dynamical systems, and machine learning. Education: PhD in Mathematics (2018), University of Maryland; BSc in Mathematics, University of Colombo. Research Interests: He focuses on statistical properties of dynamical systems, Markov processes, and higher-order asymptotics for limit theorems, employing spectral theory and techniques from probability, stochastic analysis, and statistics. Recent work explores machine learning applications to dynamical systems and interdisciplinary research opportunities. Publications span limit theorems, Edgeworth expansions, large deviations, and robust normalizing flows, with a 2025 paper on unbounded observables in ergodic theory. His work bridges theoretical and applied mathematics, including outreach initiatives in Canada, Sri Lanka, and the U.S.

Abdullah Makkehمشاهده پروفایل

پژوهشگر

• Information Theory
• Computational Neuroscience
• Artificial Neural Networks
• +۴ مورد دیگر

Dr. Abdullah Makkeh is a Senior Scientist at the University of Göttingen's Department of Data-driven Analysis of Biological Networks, headed by Michael Wibral, and a Guest Scientist at the Max Planck Institute for Dynamics and Self-Organization in Göttingen under Viola Priesemann's Complex Systems Theory group. Previously, he served as a Postdoc at the University of Tartu in both Theoretical Computer Science (Dirk Oliver Theis) and Computational Neuroscience (Raul Vicente) groups. Education: PhD in Informatics (2018, University of Tartu, Supervisor: Dirk Oliver Theis) MSc in Mathematics (2013, Lebanese University, Supervisor: Bassam Mourad) BSc in Mathematics (2011, Lebanese University) His research focuses on extending information theory to study computation in intelligent systems like the brain and artificial neural networks (ANNs). He has developed interpretable information-theoretic learning rules for ANNs (Makkeh et al., 2025) and analyzed reinforcement learning agents to reveal emergent computation mechanisms (Engel et al., 2022; Ehrlich et al., 2023). Current work applies these methods to enhance large language model (LLM) interpretability. His publications span information-theoretic frameworks, predictive coding, and neural oscillation analysis, with a 2024 Royal Netherlands Academy of Arts and Sciences (KNAW) recognition. He co-teaches courses in Bayesian Inference, Information Theory, and Discrete Mathematics, and organizes annual workshops on information theory in computational neuroscience. Scientific Awards: Royal Netherlands Academy of Arts and Sciences (KNAW) (2024) Dr. Makkeh contributes to open-source research tools via GitHub and collaborates with interdisciplinary teams across neuroscience, computer science, and mathematics. His work bridges theoretical foundations with applied machine learning through rigorous mathematical frameworks.

• Mathematical Biology
• Biomedical Data Science
• Machine Learning
• +۳ مورد دیگر

Ben MacArthur serves as Professor of Quantitative Biomedicine at the University of Southampton with a joint appointment spanning the Faculty of Medicine and Faculty of Mathematical Sciences. He holds additional positions as Visiting Professor at Kumamoto University's International Research Centre for Medical Sciences (Japan) and Programme Director for the Turing-Roche Partnership at The Alan Turing Institute. His academic foundation includes a PhD in Applied Mathematics from the University of Southampton (2003), followed by specialized training in experimental cell biology at Southampton's Faculty of Medicine (2003-2008) and Mount Sinai School of Medicine, New York (2008-2010). MacArthur's research program centers on developing mathematical models and machine learning frameworks to decode complex biomedical datasets, particularly from cellular and molecular biology. His work integrates mechanistic modeling with data-driven approaches to investigate cell dynamics, disease mechanisms, and biological network functionality. Key methodological innovations include applying concepts from differential geometry and nonlinear dynamics to analyze high-dimensional biological systems. His recent Nature Communications publication (2024) demonstrates the application of Ricci flow—a geometric analysis technique—to map cellular differentiation trajectories, revealing how topological methods can decode developmental biology processes. This exemplifies his broader research trajectory of bridging abstract mathematical constructs with tangible biomedical challenges. In his leadership role for the Turing-Roche Partnership, MacArthur drives collaborative initiatives focused on accelerating disease understanding through AI and statistical science, with particular emphasis on omics data analysis and clinical translation of data science innovations.

• Complex Networks
• Network Motifs
• Community Detection
• +۵ مورد دیگر

Bogdan Mursa serves as a Lecturer in the Department of Computer Science at the Faculty of Mathematics and Computer Science, Babeş-Bolyai University in Cluj-Napoca, Romania. His academic profile centers on complex networks research with specialized expertise in network motifs, evidenced through extensive publications spanning theoretical foundations to biological applications. His primary research focuses on network motifs as fundamental building blocks for understanding complex systems, with investigations into motif-topology correlations, dynamic flow across network layers, and efficient detection methodologies. This work integrates graph theory, machine learning, and high-performance computing to address challenges in community detection, node selection, and multi-scale network analysis. His research bridges theoretical network science with practical applications in biological systems and rehabilitation technology. Analysis of his publication trajectory (2014-2024) reveals evolving methodological sophistication: from early biomechanical applications (tongue tracking for stroke rehabilitation) to advanced motif-centric frameworks. Recent work emphasizes evolutionary algorithms for network generation (2024), automated model training techniques (2023), and ant colony social behavior modeling (2022), demonstrating consistent innovation in motif-based network analysis while expanding into interdisciplinary domains. No scientific awards were documented in the provided materials. Available information does not specify graduate student supervision, research grants, or collaborative projects. His current work appears centered on algorithmic development for network motif applications across computational and biological domains, with emerging exploration of automated techniques in model training.

Thomas Needhamمشاهده پروفایل

دانشیار

• Geometry
• Topology
• Optimal Transport
• +۴ مورد دیگر

Thomas (Tom) Needham is an Associate Professor of Mathematics at Florida State University, where his work bridges pure geometry and topology with modern data-science challenges. Research Interests: Needham’s research centers on applying geometric and topological ideas—particularly optimal transport, Riemannian and symplectic geometry, and topological data analysis—to large and complex data sets. He develops theoretical frameworks, algorithms, and computational tools that reveal shape, structure, and relationships within high-dimensional data. Optimal transport theory and its generalizations (Gromov-Wasserstein, unbalanced transport) Metric and symplectic geometry in data contexts Topological summaries (persistence diagrams, Reeb/Merge trees) Statistical shape analysis and geometric statistics Funding & Support: His program is currently supported by the U.S. National Science Foundation through grants DMS–2324962 and CIF–2526630; prior support includes DMS–2107808. Laboratory & Collaboration: Needham collaborates extensively across mathematics, computer science, statistics, and application domains such as neuroscience and imaging. While no dedicated lab name is given, his projects involve multi-institutional teams and graduate students working at the intersection of geometry, topology, and data science.

Helene Thiolletمشاهده پروفایل

پژوهشگر

• Migration Studies
• Refugee Studies
• International Relations
• +۶ مورد دیگر

Dr. Helene Thiollet is a permanent researcher at the French National Centre for Scientific Research (CNRS) and Centre for International Studies and Research (CERI) at Sciences Po Paris, where she also serves as permanent faculty. Additionally, she is a Research Partner at the International Migration Institute at the University of Oxford. Her work focuses on migration governance, refugee studies, and Middle Eastern politics, with particular expertise in Gulf migration systems and Arab regional politics. Dr. Thiollet's research spans several interconnected fields within migration studies and international relations. Her work examines the political dynamics of migration in the Middle East, particularly focusing on Gulf Cooperation Council countries. She investigates how migration functions as a tool of statecraft ('migration diplomacy'), analyzes the governance of migration in illiberal contexts, and explores the relationship between migration and crisis framing. Her scholarship also addresses cosmopolitanism in exclusionary contexts, refugee protection, and the economic impacts of migration. She employs diverse methodologies including discourse analysis, institutional history, and ethnographic fieldwork conducted in state bureaucracies. Dr. Thiollet's recent publications reveal a consistent focus on migration governance in the Global South, particularly the Middle East. Her work increasingly examines the externalization of migration control by Global North states and its differential impacts on refugees versus other migrants. There's a clear trajectory toward analyzing migration as a political tool ('migration diplomacy') and exploring the concept of 'illiberal migration governance' in Gulf states. Her research combines theoretical innovation with empirical rigor, often using mixed methods approaches to address complex migration phenomena. While specific awards aren't mentioned in the available information, Dr. Thiollet's significant contributions to migration studies are evidenced by her extensive publication record in top journals and her role as co-editor of major reference works including the 'Research Handbook on the Institutions of Global Migration Governance.' Her teaching at Sciences Po encompasses undergraduate and graduate courses on world politics, global migration governance, and African politics, indicating active engagement in mentoring. Dr. Thiollet works within the Centre for International Studies and Research (CERI) at Sciences Po, a leading research center in international relations. Her collaborations extend to the International Migration Institute at Oxford University, suggesting participation in international research networks focused on migration governance. Her work with co-authors like Antoine Pécoud on migration governance handbooks indicates leadership in collaborative research projects examining how various actors shape significant migration dynamics.

Shizan Fangمشاهده پروفایل

استاد

• Probability
• Statistics
• Optimization
• +۶ مورد دیگر

Shizan Fang is a Professor at the Institute of Mathematics of Burgundy , affiliated with the University of Burgundy . His research focuses on probability, statistics, optimization, and control, with significant contributions to stochastic differential equations, Navier-Stokes equations, and optimal transport theory. Email: shizan.fang@ube.fr Office: Mirande Building, Wing A, Office 330 His recent work explores stochastic analysis on Riemannian manifolds, spectral gaps in path space, and generalized flows for hydrodynamic equations. Publications span top-tier journals like Annali Scuola Normale Sup. di Pisa , Potential Analysis , and Journal of Functional Analysis . Professor Fang collaborates internationally, with past lectures at Beijing Normal University, University of Bielefeld, and Université de Strasbourg. He contributes to training at the Master level in mathematics and applications.

• Dynamical Systems
• Topology
• Algebraic Topology
• +۵ مورد دیگر

Gioia Maria Vago is a Lecturer at the University of Burgundy , affiliated with the Institute of Mathematics of Burgundy (IMB) and the research team Geometry, Algebra, Dynamics and Topology . She holds a Habilitation à Diriger des Recherches (HDR) (2013) and a PhD in Mathematics (1998) under the supervision of Christian Bonatti. Research Focus : Topological, algebraic, combinatorial, and algorithmic aspects of discrete and continuous dynamical systems, including hyperbolic dynamics, Morse flows, cellular automata, and aperiodic tilings. Teaching : Diversified instruction in Mathematics, Computer Science, and Mathematics-Computer Science for audiences ranging from mathematicians to biologists, including courses on Discrete Mathematics, Graph Theory, Unix/Linux, programming, and statistical software (R, Maple, Statistica). Students : Co-directed PhD thesis of Abdelrazak Jmel (2013) and supervised post-doc Maria Alice Bertolim (2004-2005) under a French Ministry fellowship. Notable Collaborations : Work with Michel Boileau on three-dimensional Ogasa invariants, and with Alain Jacquemard on computational biology projects (allosteric regulation models). Software Achievements : Developed Maple programs for unstable manifold conjugacy analysis and VBA tools for biochemical data optimization. Scientific Awards : HDR in Mathematics (2013) PhD Thesis with Mention Très Honorable et Félicitations du Jury (1998) French Ministry Postdoctoral Fellowship for Maria Alice Bertolim (2004-2005) Research Trends : Her work bridges topological complexity (e.g., Ogasa invariant) with combinatorial and algorithmic methods, particularly in high-dimensional dynamics (2008 paper on diffeomorphism centralizers) and low-dimensional systems (1999-2001 papers on hyperbolic manifolds).