Han Yu is a Zeeman Lecturer and Leverhulme Early Career Fellow at the University of Warwick's Mathematics Institute. His research focuses on Number Theory, Fractal Geometry, Ergodic Theory, and Dynamical Systems, with particular interests in Diophantine approximation, self-similar measures, and additive combinatorics. He holds a PhD from the University of St Andrews, supervised by Dr. Jonathan Fraser and Dr. Mike Todd, and previously worked as a research associate at the University of Cambridge's DPMMS and a research fellow at Corpus Christi College. His work bridges pure mathematics disciplines, applying fractal geometry to number theory and exploring the geometric properties of dynamical systems. Notable contributions include studies on Bernoulli convolutions, fractal projections, and dimension growth in iterated sumsets. Scientific recognition includes the Leverhulme Early Career Fellowship. His teaching includes advanced modules like MA3D4 Fractal Geometry and MA426 Elliptic Curves at Warwick.
Furqan Aziz is a Lecturer in the School of Computing and Mathematical Sciences at the University of Leicester since 2022. Previously, he served as a Research Fellow at the Institute of Cancer and Genomic Sciences, University of Birmingham. He holds a Ph.D. in Computer Science from the University of York, UK, focusing on interdisciplinary research. His research interests include Spectral Graph Theory, Complex Networks, Machine Learning, and Bioinformatics. He applies these techniques in healthcare informatics, network analysis, and computational biology. Notably, his work explores disease phenotype modeling, multimorbidity prediction, and drug response analysis using machine learning. Recent publications highlight trends in network science applications, including link prediction, graph characterization, and predictive modeling in healthcare. His bioinformatics research bridges computational methods with medical data analysis, addressing challenges in personalized medicine and public health surveillance. No awards or grants are explicitly listed in his profile. He currently advises students in computational science and mathematical modeling, though specific advisee names are not provided. His work spans collaborations in academia and industry, emphasizing interdisciplinary problem-solving.
Piotr Zwiernik is a Professor Agregat in the Department of Economics and Business at Universitat Pompeu Fabra (UPF), Barcelona, and a member of the BSE Data Science Center. He is currently on leave from the Department of Statistical Sciences and the Department of Mathematics at the University of Toronto. His research bridges statistics, algebraic geometry, and machine learning, focusing on graphical models, covariance estimation, tensors, and algebraic methods in statistics. Research Interests: Graphical Models Covariance Matrix Estimation Convex Analysis Tensors Algebraic and Combinatorial Methods in Statistics Statistical Learning Theory His recent publications span high-impact journals such as the Annals of Statistics , Biometrika , and Journal of the Royal Statistical Society . The work demonstrates a consistent trend in developing mathematically rigorous frameworks for understanding dependence structures, latent variable models, and high-dimensional inference, often leveraging tools from algebraic geometry and convex optimization. Key themes include total positivity, tensor methods, and the geometry of statistical models. Scientific Recognition: Editorial Board Member, Journal of the Royal Statistical Society Series B (JRSS-B) Editorial Board Member, Biometrika Editorial Board Member, Scandinavian Journal of Statistics Editorial Board Member, Algebraic Statistics Zwiernik actively mentors students and is seeking PhD candidates with strong mathematical backgrounds at UPF and the Institute of Mathematics of UPC. He has led the development of several open-source R packages, including golazo , MTP2binary , and StructuralEM , facilitating research in graphical models and latent structures. His work is supported by a network of collaborations with leading statisticians and has significant theoretical and applied implications in data science. Laboratories and Research Groups: Statistics@UPF BSE Data Science Center
Prof. Dr. Amru Hussein is a faculty member in the Department of Mathematics at the University of Kassel, where he holds the position of Professor for Analysis of Partial Differential Equations since 2025. He is affiliated with the Faculty of Mathematics and Natural Sciences, within the Analysis and Applied Mathematics research group. 2025–present: Professor, University of Kassel 2019–2025: Junior Professor, Technical University of Kaiserslautern 2015–2019: Research Assistant, Technical University of Darmstadt 2009–2013: Research Assistant and Doctorate, Johannes Gutenberg University Mainz 2003–2008: Mathematics Studies, University of Duisburg-Essen and University of Bonn His research lies at the intersection of applied and pure mathematics, focusing on the analysis of partial differential equations, spectral theory of differential operators, and functional analytic methods. His work has applications in mathematical physics and the study of operators on bounded domains and metric graphs. He has contributed to theoretical developments in scattering theory and spectral analysis. No recent publications are listed in the provided text, so no trend analysis can be performed. No scientific awards are mentioned in the available information. There is no information available about students he may have advised or any research grants he has received. However, his academic trajectory suggests active involvement in research and teaching within the German academic system. His role as a full professor indicates leadership in research and curriculum development in analysis. He is part of the Analysis and Applied Mathematics research team at the University of Kassel, contributing to collaborative projects such as the DFG Scientific Network. His office is located in Heinr.-Plett-Str. 40, Room 2410, Kassel, with consultation hours on Tuesdays from 11:00 to 12:00.
Wei Qu, M.D., Ph.D., M.S., is a Senior Biologist at the National Toxicology Program (NTP) within the National Institute of Environmental Health Sciences (NIEHS). His work focuses on integrating machine learning, bioengineering, and neuroimaging to advance toxicology and medical research. He leads projects in graph neural networks, functional connectivity modeling, and biomedical image analysis. Qu’s research bridges computational methods with biological systems, addressing challenges in neurodegenerative diseases, biosensing interfaces, and automated medical diagnostics. Key research areas include graph-based representation learning, dynamic functional connectivity analysis, and 3D microscopy image processing. His contributions span neuroimaging data interpretation, biosensor design, and cervical cancer screening algorithms. Qu collaborates across interdisciplinary teams to develop replicable machine learning frameworks for environmental health studies.
Vincenzo Nicosia is Senior Lecturer in Networks and Data Analysis at the School of Mathematical Sciences, Queen Mary University of London, and a member of the Centre for Complex Systems. His research deciphers the structure and dynamics of complex networks, with particular emphasis on multilayer and multiplex systems, random-walk processes, synchronisation, and their applications to urban analytics, neuroscience and epidemic modelling. Education & early career: While explicit degrees are not listed in the supplied text, Dr Nicosia has built an extensive publication record (100+ papers) since 2006, indicating long-standing academic training and international recognition in network science. Research interests: Nicosia’s work revolves around three inter-related pillars: Fundamental theory: random walks, diffusion, opinion dynamics, synchronisation and percolation on single and multilayer networks Methodological development: visibility graphs, first-passage observables, metadata-dependent embeddings, spectral and entropy-based metrics Data-driven applications: quantifying urban segregation and epidemic disparities, modelling cancer-spatial evolution, mining musical harmony networks, and analysing brain multiplex motifs His recent publications (2020-2023) reveal a strong focus on spatial stochastic processes —using random walks to measure segregation, mutation clustering in tumours, and the impact of city layout on COVID-19 spread—and on algorithmic inference in multiplex structures, including optimal percolation and compressed network representation. Grants & awards: He currently holds / has led EPSRC grant "Assessing spatial heterogeneity through random walks on graphs" (£162,886, 2019-2021). No other awards or fellowships are mentioned in the supplied material. PhD supervision & team: He advises an active cohort of doctoral researchers: Liam Fahey (temporal knowledge graphs), Yuhan Li (adaptive epidemic modelling), and Tom Roberts (stochastic sampling on lattice animals), among others. Outreach & service: Nicosia serves on the Council and Executive Committee of the Complex Systems Society, contributing to the governance and strategic direction of the international community.
Geir Dahl is a Professor in the Department of Mathematics at the University of Oslo, specializing in Differential Equations and Computational Mathematics. His research focuses on combinatorial matrix theory, including majorization order, polytopes, and (0,1)-matrices. He teaches courses such as Linear Algebra, Linear Optimization, and Mathematical Optimization. Dahl's work spans topics like doubly stochastic matrices, spectral graph theory, and applications in tennis rankings. His extensive publications include studies on permutation polytopes, matrix classes, and combinatorial optimization. He is actively involved in the Computational Mathematics research group and has collaborated extensively with researchers like Richard Brualdi.
Xiaodong Li is an Associate Professor in the Department of Statistics at the University of California, Davis, within the College of Letters and Science. His research lies at the intersection of high-dimensional statistics, statistical learning, and nonconvex optimization, with applications in matrix completion, phase retrieval, and network analysis. His research interests focus on developing and analyzing statistical methodologies for modern data science challenges. He investigates high-dimensional inference , nonconvex optimization algorithms , and robust statistical methods for complex data structures. His work often bridges theoretical guarantees with practical algorithmic implementation, particularly in unsupervised learning and signal processing. The recent publications highlight a strong trend toward theoretical analysis of nonconvex methods in matrix estimation, community detection in networks , and high-dimensional signal recovery . His work frequently appears in top-tier journals such as the Annals of Statistics , IEEE Transactions on Information Theory , and Journal of Machine Learning Research , reflecting consistent contributions to both statistical theory and machine learning. Scientific Awards: No scientific awards mentioned in the provided text. Xiaodong Li has actively advised multiple PhD students, including Ji Chen (2020), Xingmei Lou (2022), Yucheng Liu (2022), and Xiaohan Hu (2024), with Zhentao Li currently under supervision. His research is supported by publications rather than explicitly mentioned grants, indicating a strong publication-driven academic profile. He teaches a wide range of courses from undergraduate probability and regression to graduate-level mathematical statistics and longitudinal data analysis. While no specific lab or research group name is mentioned, his collaborative work with researchers such as Emmanuel J. Candes, T. Tony Cai, and Yudong Chen suggests active participation in the broader statistical learning and high-dimensional data analysis community. His recent work on polytree models and community detection indicates ongoing exploration into structured high-dimensional models and network inference.
Prof. Dr. Matthias Erbar is a faculty member in the Faculty of Mathematics at Bielefeld University, where he leads the research group AG Erbar. His work is centered on mathematical analysis, with a focus on optimal transport, gradient flows, and discrete geometric analysis. He is affiliated with the Department of Mathematics and maintains an active research profile with numerous publications in top-tier journals. His research interests lie at the intersection of analysis, probability, and geometry. He investigates optimal transport on discrete and continuous spaces, gradient flow structures in PDEs and stochastic processes, Ricci curvature on metric measure spaces and graphs, and functional inequalities such as Poincaré and logarithmic Sobolev inequalities. His work often employs entropy-based methods and explores the geometric implications of curvature conditions in non-smooth settings. The recent publications show a consistent trend toward understanding geometric and analytic properties of discrete systems, including graphs, Markov chains, and configuration spaces. There is a strong emphasis on formulating continuous concepts like Ricci curvature and gradient flows in discrete settings, enabling applications in probability, statistical mechanics, and numerical analysis. The work frequently involves collaboration with leading researchers in the field. Scientific Awards: No awards explicitly mentioned in the provided text. Prof. Erbar advises doctoral students and leads an active research group (AG Erbar), suggesting engagement in graduate supervision and collaborative research. While specific grants are not listed, his publication output and collaborations imply sustained research funding. He has co-authored papers with researchers across Europe, indicating international collaboration and academic leadership. He is the principal investigator of AG Erbar , a research group within the Faculty of Mathematics at Bielefeld University. The group focuses on analysis and probability, particularly in the context of optimal transport and discrete geometry. The group likely includes PhD students and postdoctoral researchers, contributing to the broader research environment in mathematical analysis at Bielefeld.
Nicolas Rubido Obrer is a Lecturer in the Department of Physics at the School of Natural and Computing Sciences, University of Aberdeen, UK. He is affiliated with the Institute for Complex Systems and Mathematical Biology (ICSMB) and previously served as an Adjunct Professor at the Physics Institute, Universidad de la República (UdelaR), Uruguay, and as a Research Fellow at the Aberdeen Biomedical Imaging Centre (ABIC). He earned his PhD in Physics from the University of Aberdeen in 2014, with a thesis on energy transmission and synchronization in complex networks. PhD in Physics – University of Aberdeen (2014) MSc in Physics – Universidad de la República (2010) BSc in Physics – Universidad de la República (2008) His research focuses on complex systems, employing network theory, dynamical systems, numerical modeling, and data analysis to study emergent behaviors such as synchronization and chaos. He applies these methods across interdisciplinary domains including brain networks (Alzheimer’s disease, sleep-wake cycles), climate modeling, and power-grid stability. He is particularly interested in network inference and reverse engineering system connectivity from observational data. The recent trend in his publications reflects a strong emphasis on interdisciplinary neuroscience, with high-impact contributions in Nature Medicine , NeuroImage , and Scientific Reports , focusing on brain aging, neurodegeneration, and neuroimaging. His work also spans climate dynamics and foundational network theory, demonstrating both applied and theoretical depth. His scientific awards include the prestigious Springer Theses Award (2015) and the Young Researchers Award at Dynamics Days Europe (2016), as well as the SUPA Studentship Prize (2011). Nicolas supervises PhD and MSc students in Physics, Mathematics, and Applied Health Sciences, and is actively involved in international collaborations with institutions in Uruguay, Brazil, Spain, Argentina, and the UK. He has led and contributed to projects on EEG-based biomarkers for aging, functional ultrasound imaging, and climate forecasting. His research integrates theoretical modeling with experimental and clinical data, advancing methodologies in network science and complex systems analysis. He is a member of the Non-Linear Physics group at UdelaR and associated with the Laboratory of Instabilities in Fluids. His ongoing work includes developing data-driven models for complex systems and exploring the role of network structure in synchronization and information transmission.
Jose Julian Toledo Melero is a Professor in the Department of Mathematical Analysis at the Faculty of Mathematics, Universitat de València. He is a leading researcher in nonlinear partial differential equations, with a focus on nonlocal diffusion, evolution equations, and optimal transport in metric random walk spaces. His work bridges pure and applied analysis, with strong collaborations with mathematicians such as José M. Mazón and Julio D. Rossi. His research interests include: Nonlinear Partial Differential Equations Nonlocal Diffusion and Operators Calculus of Variations and Optimal Transport Evolution Problems in Metric Spaces Geometric Analysis and Minimal Surfaces Doubly Nonlinear and Degenerate Equations The recent articles show a consistent focus on analytical and geometric properties of nonlocal models, particularly in discrete and metric random walk settings. His work frequently appears in top journals such as Calculus of Variations , SIAM Journal on Mathematical Analysis , and Journal of Evolution Equations , often in collaboration with leading experts. The publications reveal a deep engagement with both theoretical foundations and structural analysis of solutions. He has co-authored influential books such as Nonlocal Diffusion Problems (AMS, 2010) and Variational and Diffusion Problems in Random Walk Spaces (Birkhäuser, 2023), which are key references in the field. His research is highly cited, indicating significant impact in the mathematical community. He advises no publicly listed students in the provided data. There is no mention of specific grants or teaching roles, but his extensive publication record and book authorship reflect a sustained and high-level research career. He maintains an active research profile with upcoming events such as the 2025 CIMPA-UCA School on optimal transport and PDEs, suggesting ongoing leadership and contribution to the international mathematical community.
Chao Yin is a researcher at Shanghai University , Department of Computer Engineering and Science. His work spans multiple domains including Machine Learning, Cloud Computing, Fault Diagnosis, and Supply Chain Optimization. Key research areas: Machine Learning , Cloud Computing , Quantum Computing , Supply Chain Systems , Network Security Scientific Contributions (2024-2025): Developed heterogeneous graph neural networks for automotive supply chain analysis Created MSDF-VAE cloud-edge fault diagnosis framework using transfer learning Proposed quantum metrology methods with Heisenberg-limited precision Designed LARP pseudonym protocol for V2X communication Optimized fog computing resource scheduling with hybrid metaheuristics Prior Work (2012-2023): Contributed to fluid animation feature preservation from single images Developed label distribution learning for facial age estimation Designed erasure coding storage systems for big data Created multi-agent manufacturing networks in cloud environments
Anthony Hastir is a researcher at the Namur Institute for Complex Systems , focusing on control theory, nonlinear systems, and partial differential equations (PDEs). His work bridges mathematical analysis with applications in chemical engineering and mechanical systems. Education: PhD in Sciences (2022) and Master in Mathematical Sciences (2018), both advised by Prof. Joseph Winkin. Research Interests include: Stability analysis of nonlinear infinite-dimensional systems Optimal control of distributed parameter systems Frequency-domain approaches for PDE control Stabilization of reaction-diffusion and wave networks His scientific awards include the 2022 Distributed Parameter Systems TC Outstanding Student Paper Prize and the 2018 Frank Callier Prize. Hastir's projects involve: FRACTIONS (2022–2025): Frequency analysis for nonlinear input-output systems Local stabilization of non-isothermal tubular reactors (2020–2022) LQ-optimal control of tubular reactors (2018–2020) His activities span conference presentations on spectral analysis, Kalman filtering, and LQ control of hyperbolic PDEs.
Boaz Barak is a Professor at the Weizmann Institute of Science in the Department of Computer Science, Faculty of Mathematics and Computer Science. With an h-index of 64 and over 17,301 citations, he is a leading researcher in theoretical computer science with significant contributions spanning computational complexity, cryptography, and machine learning theory. His research interests include: Computational Complexity Cryptography Zero-Knowledge Proofs Program Obfuscation Interactive Proofs Privacy-Preserving Computation Machine Learning Theory Barak's publication record reveals a trajectory from foundational work in theoretical computer science to contemporary research at the intersection of theory and practice. His early work established impossibility results for program obfuscation and advanced techniques for zero-knowledge proofs beyond black-box simulation. His influential textbook "Computational Complexity: A Modern Approach" has become a standard reference in the field. More recently, his research has expanded into machine learning phenomena like double descent and scaling laws for language models, demonstrating the evolving nature of his theoretical contributions. His work consistently bridges deep theoretical insights with practical implications for computing. His notable collaborations include extensive work with Sanjeev Arora (77 publications, 6,879 citations), David Steurer (95 publications, 4,945 citations), and Oded Goldreich, among others. Professor Barak leads a research group at the Weizmann Institute focused on theoretical aspects of computer security and complexity theory. His work has been consistently supported by major research funding, enabling significant contributions to the theoretical foundations of computer science. He maintains an active research program with publications spanning over two decades, demonstrating sustained impact in multiple subfields of theoretical computer science.
Dr. Krystal Guo is an Assistant Professor of Discrete Mathematics at the Korteweg-de Vries Institute for Mathematics , University of Amsterdam. Her research spans algebraic graph theory, quantum computing, and spectral graph theory, with a focus on eigenvalues of graphs and digraphs. She is also affiliated with QuSoft , the Dutch research center for quantum software. Education: PhD in Mathematics (2015) from Simon Fraser University under Bojan Mohar. Prior Positions: Postdoctoral stints at Université de Montréal (2019-2020), Université Libre de Bruxelles (2017-2019), and University of Waterloo (2015-2017). Her research bridges linear algebra and combinatorics, with applications to quantum information theory, directed graphs, and linear optimization. She actively explores connections between graph polynomials, association schemes, and quantum walks. Recent publications span graph isomorphism complexity, quantum error correction, and four-color theorem proofs using generating functions. She serves as Managing Editor of the Electronic Journal of Combinatorics since 2022 and organizes the General Mathematics Colloquium at UvA with Eni Musta and Jeroen Zuiddam. Key Scientific Awards: 2013 finalist in Simon Fraser University’s 3MT thesis competition. She maintains an active research blog, Graphs on Napkins , and contributes to open-source mathematics via GitHub repositories containing cubic graph census data and computational tools.
