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Abdullah MakkehView profile

Researcher

• Information Theory
• Computational Neuroscience
• Artificial Neural Networks
• +4 more

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
• +3 more

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
• +5 more

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 NeedhamView profile

Associate Professor

• Geometry
• Topology
• Optimal Transport
• +4 more

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.

• Traumatic Brain Injury
• Disorders of Consciousness
• Consciousness Neuroscience
• +4 more

Professor David Menon CBE FMedSci is Group Leader (Professor) in the Department of Medicine , University of Cambridge, and a Fellow of Queens’ College . He co-directs three flagship international programmes in traumatic brain injury (TBI): the €30 million EU CENTER-TBI consortium, the InTBIR initiative, and the UK national platform TBI-REPORTER . His work integrates advanced neuroimaging (PET, MRI, fMRI, DTI) with large-scale clinical consortia to understand consciousness, TBI pathophysiology and outcome prediction. Education & Qualifications Primary medical qualification and specialist training in anaesthesia & intensive care medicine (details not specified in text). Elected Fellow of the Academy of Medical Sciences (FMedSci). Appointed Commander of the Order of the British Empire (CBE) for services to medicine. Research Interests Professor Menon’s research spans three interconnected domains: Neurotrauma: pathophysiology of cerebral ischaemia, metabolic derangements, biomarker discovery and precision medicine in TBI. Consciousness Science: neural signatures of anaesthetic and pathological unconsciousness, default-mode network dynamics, and cognitive motor dissociation. Neuroimaging Methodology: harmonisation of multicentre MRI data, functional connectomics, and integrated information decomposition. Recent Publication Trends (2020-2024) His latest 15 high-impact articles reveal a sustained focus on traumatic brain injury and disorders of consciousness , leveraging PET, fMRI and DTI to map acute metabolic changes, thalamic connectivity and covert cognition. Multi-centre consortia ( CENTER-TBI , TRACK-TBI ) underpin these studies, yielding predictive models for post-concussive symptoms and long-term functional outcomes. Scientific Awards & Honours Commander of the Order of the British Empire (CBE) Fellow of the Academy of Medical Sciences (FMedSci) Funding & Leadership Roles Co-Lead, EU €30 million CENTER-TBI consortium Co-Lead, International Initiative on TBI Research (InTBIR) Co-Lead, UK TBI-REPORTER national platform Executive Editor, UK All-Party Parliamentary Group Report on Acquired Brain Injury (2019) Laboratory & Teams Professor Menon heads a multi-disciplinary group within the Cambridge Neuroscience network, embedded in the Wolfson Brain Imaging Centre and the Division of Anaesthesia. The team integrates clinicians, physicists, data scientists and neuropsychologists to translate advanced neuroimaging into precision diagnostics for TBI and disorders of consciousness.

Helene ThiolletView profile

Researcher

• Migration Studies
• Refugee Studies
• International Relations
• +6 more

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 FangView profile

Professor

• Probability
• Statistics
• Optimization
• +6 more

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
• +5 more

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).

Florian PfaffView profile

Assistant Professor

• Sensor Fusion
• Estimation Theory
• Multi-target Tracking
• +4 more

Florian Pfaff is a Junior Professor (Assistant Professor) at the Institute of Automation and Software Systems, University of Stuttgart, leading the professorship for "Cognitive Sensor Technology for Future Mobility". He earned his doctorate in computer science from Karlsruhe Institute of Technology (KIT) with summa cum laude distinction. His research focuses on: Sensor fusion and multi-target tracking algorithms Probabilistic modeling for robotic systems Industrial automation and optical material sorting Estimation theory for high-dimensional spaces His publication trends show strong emphasis on sensor-based sorting systems, stochastic filtering techniques, and applications of machine learning in industrial automation. Recent work demonstrates increasing focus on 3D pose estimation and real-time control systems. Awards and honors include: ISIF Young Investigator Award SICK Science Award for best doctoral thesis Inclusion in "Excellent Computer Science Dissertation" compendium Multiple best paper awards He has led research projects at KIT on optical bulk material sorting, served as visiting scholar at University College London, and visiting professor at University of Oxford. He teaches courses on software systems and sensor networks. Leads the Cognitive Sensor Technology research group at University of Stuttgart, focusing on sensor fusion applications for mobility systems.

• Network Science
• Statistical Physics
• Mathematical Modeling
• +5 more

Antoine Allard is an Associate Professor at the Faculty of Science and Engineering, Université Laval. He works at the intersection of network science, statistical physics, and mathematical modeling, with applications in epidemiology, neuroscience, and ecology. Current funding: Mathematical description of complex networked systems (NSERC, 2024-2029); Joint modeling of longitudinal and survival data (Desjardins du Québec, 2021-2026) Past funding: Arctic sensor networks (Sentinelle Nord, 2020-2024); Hyperbolic network modeling (NSERC, 2019-2024) His research focuses on: Network structure and dynamics Hyperbolic space embeddings Epidemiological modeling on networks Network-based disease surveillance Ecological and environmental modeling Computational complexity analysis Recent publication trends show expertise in network theory, geometric modeling, and epidemic spread analysis. Key subfields include hyperbolic geometry, nonlinear contagion, brain connectivity, and climate change impacts. He supervises students in: Physics (Simon Lizotte, Vincent Thibeault, François Thibault, Charles Murphy) Biology (Gabriel Bergeron)

Julien ReygnerView profile

Professor

• Stochastic Processes
• Particle Systems
• Numerical Methods
• +4 more

Julien Reygner is a Professor at École des Ponts ParisTech (ENPC) and Deputy Director of CERMICS research laboratory. He holds a concurrent part-time position as Associate Professor at École Polytechnique. His academic background includes a PhD from Sorbonne Université (2011-2014) and a Habilitation thesis (2021). He has held positions as Assistant Professor at ENPC (2018-2023) and CNRS postdoctoral fellow at ENS Lyon (2014-2015). Reygner's research explores stochastic processes, particle systems, and numerical methods with applications in uncertainty quantification. His work bridges probability theory with mathematical analysis and statistical mechanics. Primary domains include Langevin dynamics, mean-field systems, Fokker-Planck equations, and computational statistics. His publications demonstrate consistent focus on stochastic modeling, particle methods, and probabilistic approaches to partial differential equations. Recent work emphasizes convergence analysis, structural reliability, and applications in statistical learning. He actively advises doctoral candidates, including projects on particle systems, Langevin processes, and structural fatigue. Research grants include ANR projects: Conviviality (2023-2028), QuAMProcs (2019-2024), and EFI (2018-2022). He leads CERMICS' Applied Probability team and co-organizes seminars on data transitions and uncertainty quantification.

• Hydrology
• Climatology
• Climate Change Impacts
• +6 more

Sergiy Snizhko is a Professor and Head of the Department of Meteorology and Climatology at Taras Shevchenko National University of Kyiv, Ukraine. He has led this department since 2002 and has over 40 years of experience in hydrology, climatology, and water resource management. His work focuses on assessing climate change impacts on Ukraine's water resources and developing adaptation strategies for the water sector, with particular emphasis on Ukraine's vulnerability due to its relatively low availability of internal water resources compared to other European countries. Dr. Snizhko's educational background includes: MSc in Hydrology and Hydrochemistry (1980) from Taras Shevchenko National University of Kyiv PhD in Hydrology, Water Resources and Hydrochemistry (1987) from Taras Shevchenko National University of Kyiv Doctor of Sciences in Hydrology, Water Resources and Hydrochemistry (2001) from Taras Shevchenko National University of Kyiv Professor title awarded in 2003 His primary research interests revolve around climate change impacts on water resources, particularly focusing on flood risk assessment, hydrological drought probability, and adaptation strategies for water-dependent sectors. Dr. Snizhko's research addresses how temperature rises and shifts in precipitation patterns could lead to changes in flood or drought frequency, water availability, and seasonality of water discharge. His team is one of Ukraine's leading research groups conducting comprehensive assessments of climate change impacts on the country's water resources, having studied 19 representative river basins across Ukraine. Dr. Snizhko's recent publications demonstrate a strong focus on the intersection of climate change, water security, and geopolitical challenges in Ukraine. His work increasingly addresses the compounded impacts of climate change and wartime conditions on water resources, particularly following the destruction of critical infrastructure like the Kakhovka reservoir. His research spans hydrological modeling, flood risk assessment in the Carpathian region, thermal comfort conditions in urban areas, and the development of adaptation technologies for the water sector. Dr. Snizhko leads a research group that includes Associate Professor O. Shevchenko, Dr. I. Didovets (a former PhD student who is now a PostDoc researcher at PIK), and PhD students M. Matviienko and I. Kostyrko. His team has secured numerous research collaborations with international institutions including Potsdam University, the Potsdam Institute for Climate Impact Research (PIK), Technical University of Denmark, and University of Cape Town. His research group is actively working on several key projects including assessment of changes in seasonal distribution of water flow under climate change, hydrological drought probability assessment, and water sector adaptation measure development. These projects are crucial for Ukraine's adaptation to climate change impacts on its water resources, especially in the current geopolitical context. His findings have been included in UNEP reports including the 'TECHNOLOGY NEEDS ASSESSMENT REPORT ADAPTATION' (2019) and 'BARRIER ANALYSIS AND ENABLING FRAMEWORK REPORT'.

• Mechanics of Deformable Solids
• Mathematical Modeling
• Economics
• +3 more

Tetyana Ihorivna Levitska is an Associate Professor in the Department of Applied Mathematics at Zaporizhzhia Polytechnic National University. With over 25 years of academic experience since 1999, she specializes in mechanics of deformable solids and mathematical modeling in economics. Her expertise spans both theoretical and applied mathematics with significant contributions to engineering applications. Her educational background includes: Bachelor's and Master's in Applied Mathematics from Zaporizhia State University (1994) Candidate of Technical Sciences (PhD equivalent) in Mechanics of Deformable Solids (2000) Professor Levitska's research focuses on the mechanics of deformable solids, particularly stress-strain analysis of shells and composite structures, and mathematical modeling in economics. Her work bridges theoretical mathematics with practical engineering applications, especially in aerospace engineering and economic systems modeling. She has developed innovative approaches to fuzzy logic applications in economic modeling and has contributed significantly to understanding complex shell structures under various loading conditions. Her recent publications demonstrate a strong trend toward interdisciplinary research, combining mechanical engineering principles with computational mathematics. The majority of her work centers on stress-strain analysis of aircraft components, composite materials, and shells of rotation, while also exploring applications of fuzzy logic in economic modeling and migration phenomena. Professor Levitska teaches Higher Mathematics, Probability Theory and Mathematical Statistics, Discrete Mathematics, and Higher and Applied Mathematics to undergraduate and graduate students. Her teaching approach integrates theoretical foundations with practical applications, preparing students for both academic and industry careers.

Patrick WahlView profile

Professor

• Sports Science
• Exercise Physiology
• Strength Training
• +5 more

Univ.-Prof. Dr. Patrick Wahl is a full-time Professor at the Institute of Training Science and Sports Informatics , specifically in the Department of Performance Physiology at German Sport University Cologne. He serves as Head of Master LTC and maintains an active research profile with 255 publications and 38 projects. Institute: Institute of Training Science and Sports Informatics Department: Performance Physiology Email: p.wahl@dshs-koeln.de Phone: +49 221 4982-6160 His research focuses on Exercise Physiology , Strength Training , Blood Flow Restriction Training , Lactate Threshold Analysis , and Neuromuscular Adaptations . Collaborations include Sebastian Keller , Sanghyeon Ji , and Joachim Mester , with projects funded through third-party grants. Recent publications highlight his work on blood flow dynamics , metabolic stress response , performance diagnostics , and sports informatics . His research combines physiological decomposition with artificial intelligence for athlete profiling. Current projects include national swimming performance diagnostics , neuromuscular profiling , and interval training optimization . He actively participates as a reviewer, lecturer, and conference organizer in sports science events. The department maintains strong collaborations with the German Research Center for Competitive Sports and other sports medicine institutions, focusing on elite athlete development and training methodology .

Alexander KorotinView profile

Assistant Professor

• Generative Modeling
• Unpaired Learning
• Optimal Transport
• +2 more

Alexander Korotin is an Assistant Professor at the Skolkovo Institute of Science and Technology (Skoltech) where he heads the Generative AI research group. He is also a senior research scientist at the Artificial Intelligence Research Institute (AIRI), leading the "Foundations of Generative AI" group. His academic journey includes a PhD in Math & Physics from Skoltech (2023), an MSc in Computer Science from the Higher School of Economics (HSE), and a BSc in Mathematics also from HSE. Dr. Korotin's research focuses on generative modeling, with particular emphasis on developing novel algorithms based on Optimal Transport and Schrodinger Bridges. His work bridges theoretical mathematics with practical machine learning applications, contributing significantly to the field of generative artificial intelligence. He has pioneered approaches to make Schrodinger Bridge solvers more efficient and practical, most notably with his "Light Schrödinger Bridge" framework that simplifies complex computational procedures while maintaining theoretical rigor. His publication record shows a clear progression toward making advanced generative modeling techniques more accessible and computationally efficient. Recent work demonstrates increasing sophistication in handling complex distribution matching problems through physics-inspired approaches (like electrostatic field matching) and novel distillation techniques that accelerate inference. The research spans from theoretical foundations to practical applications in image processing, semi-supervised learning, and reinforcement learning. Dr. Korotin has received recognition for his contributions to neural optimal transport and Schrodinger Bridges, with his papers frequently appearing in premier machine learning venues. His work on efficient computational methods for optimal transport has established him as a rising expert in these specialized areas of machine learning. As an academic leader, Dr. Korotin advises research students and collaborates extensively with colleagues across institutions, contributing to the advancement of generative AI through both theoretical developments and practical implementations. His work continues to push the boundaries of what's possible in generative modeling, with recent publications focusing on making advanced mathematical approaches more computationally efficient for real-world AI applications.