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Florian LehnerView profile

Senior Lecturer

• Graph Theory
• Combinatorics
• Group Theory
• +3 more

Florian Lehner is a Senior Lecturer in the Department of Mathematics at the University of Auckland. His research focuses on graph theory and combinatorics, particularly exploring the interface of group theory and graph theory, with interests in probabilistic and geometric aspects. He holds a PhD from Graz University of Technology (Sub auspiciis Praesidentis) and has held academic positions internationally, including at the University of Warwick and Graz University of Technology. His work includes studies on asymmetric colorings, pursuit-evasion games (e.g., cop number analysis), and structural properties of infinite graphs. Recent contributions include extending Stallings' splitting theorem to infinite graphs and analyzing self-avoiding walks on graphs. Education: PhD in Mathematics and Scientific Computing (Graz University of Technology, 2014), Master's in Mathematical Computer Science (Graz University of Technology, 2011), and Bachelor's in Technical Mathematics (Graz University of Technology, 2008). Research Interests: Graph symmetries, combinatorial game theory (e.g., Cops-and-Robber), Hamiltonian decompositions, probabilistic graph analysis, and geometric properties of infinite graphs. His work often bridges algebraic structures with discrete mathematics, demonstrated through collaborations on tree structures, automorphism groups, and graph colorings. Publications Trends: Recent articles emphasize pursuit-evasion dynamics, asymmetric colorings in infinite graphs, and structural graph properties. He frequently collaborates with researchers like B. Miraftab and J. Erde, focusing on foundational graph theory problems with applications in combinatorial optimization and geometric group theory. Advising & Grants: While specific grants aren't detailed, his academic trajectory reflects sustained research output in prestigious journals. Advising history isn't explicitly listed, though his PhD thesis supervision and postdoctoral mentorship roles suggest active involvement in training early-career researchers.

• Operator algebras and functional analysis
• Algebra and number theory
• Category theory, K-theory, homological algebra

Dr. Becky Armstrong is a Postdoctoral Fellow in the School of Mathematics and Statistics at Victoria University of Wellington, New Zealand. She holds a PhD in Mathematics from the University of Sydney (2019) and degrees from the University of Wollongong, including a Bachelor of Mathematics Advanced (Honours) and Bachelor of Computer Science. Her research focuses on operator algebras, C*-algebras, and their connections to groupoids and topological structures. She has held academic positions at institutions including the University of Münster, Germany, and the University of Sydney, Australia. Education: PhD in Mathematics, University of Sydney (2016–2019) Bachelor of Mathematics Advanced (Honours, Class I, University Medal), University of Wollongong (2014) Bachelor of Mathematics and Computer Science, University of Wollongong (2013) Research Interests: Becky’s research explores the interplay between C*-algebras, groupoids, and topological higher-rank graphs. She investigates properties like simplicity under cohomological twists and their implications for algebraic structures. Her work bridges operator algebras with algebraic topology and category theory. Awards & Grants: Marsden Fund Fast-Start Grant (2025–2027): Principal Investigator for a project on twisted groupoid algebras. Lift-Off Fellowship (2019): Awarded by the Australian Mathematical Society. Cheryl E. Praeger Travel Award (2018): Supported international collaboration. Recipient of multiple travel and research grants from institutions like the Heidelberg Laureate Forum and the Simons Foundation. Advising & Grants: Becky supervises doctoral and honors students, including Malcolm Jones (MSc) and Phoebe McDougall (BSc Honours). She leads or co-leads research projects on twisted algebras and groupoid theories, supported by grants from the ARC, Royal Society of New Zealand, and others. Labs & Collaboration: Her work is part of collaborative networks in operator algebras, including international partnerships with researchers at the University of Münster and the University of Sydney.

Xue HansongView profile

Lecturer

• Solar Photovoltaics
• Transport Phenomena
• Applied Machine Learning
• +3 more

Xue Hansong is a Lecturer at the Singapore University of Technology and Design (SUTD), affiliated with the Science, Mathematics, and Technology Cluster. His research focuses on solar photovoltaics, transport phenomena, applied machine learning, and knowledge graphs. He holds a PhD in Chemical & Biomolecular Engineering from NUS and has held roles at the Solar Energy Research Institute of Singapore and Cambridge CARES. Education: PhD in Chemical and Biomolecular Engineering, National University of Singapore (2019) MSc in Chemistry, Nanyang Technological University (2015) MSc in Mathematics, National University of Singapore (2011) BSc in Mathematics (First Class Honors), National University of Singapore (2009) Research Interests: Hansong’s work integrates physics-based modeling with machine learning to advance solar energy technologies. He has pioneered studies on perovskite solar cells, exploring material interfaces, tandem configurations, and degradation mechanisms. His machine learning approaches optimize device performance and bridge R&D gaps. Publications: Over 15 publications since 2017 highlight his contributions to photovoltaics and energy systems, including high-impact work in Nature Energy and Solar Energy . Recent trends focus on neural networks for device characterization and data-driven optimization for manufacturing. Awards: President Graduate Scholarship (NUS, 2010–2011) Baden-Württemberg Scholarship (Karlsruhe Institute of Technology, 2007–2008) Singapore Government Undergraduate Scholarship (2005–2009) Labs & Projects: Led teams at the Solar Energy Research Institute of Singapore (2019–2021) and contributed to Cooling Singapore as an energy specialist. Currently part of Cambridge CARES, advancing Digital Twin technology to reduce carbon footprints.

Benedikt StuflerView profile

Professor

• Combinatorics
• Probability Theory
• Random Structures
• +5 more

Prof. Benedikt Stufler is a Professor of Mathematics at Technische Universität Wien's Institute of Discrete Mathematics and Geometry. His research focuses on the interplay between combinatorics and probability, particularly the analysis of random structures such as graphs, trees, and maps. He holds a Habilitation in Mathematics (2022) and a Doctorate from Ludwig-Maximilians-Universität München (2015). Education: Habilitation in Mathematics, TU Wien (2022) Doctorate in Mathematics, LMU Munich (2015) Master's (Diplom-Mathematiker), LMU Munich (2013) Study abroad at Universidad de Buenos Aires (2011) Research Interests: His work combines stochastic methods with combinatorial constructions to study models of random structures. Key areas include: Limits of random structures (Gromov-Hausdorff-Prokhorov convergence) Branching processes and random maps Enumerative combinatorics and Gibbs partition models Phase transitions in weighted graphs Algorithmic sampling of combinatorial structures Recent Grants: FWF Project 'Limits of random networks' (2024–2027, €435k) Principal Investigator in SFB 'Discrete Random Structures' (2024–2028, €3.9M total) Lab/Group: Current members include postdoc Dr. Colin Desmarais and PhD students Niccolò Bosio and Philipp Beltran. He supervises master's theses in probability theory.

Yamir MorenoView profile

Professor

• Complex Systems
• Network Science
• Disease Dynamics
• +7 more

Prof. Yamir Moreno is a Professor of Physics at the Department of Theoretical Physics, Faculty of Sciences, University of Zaragoza, where he also serves as Director of the Institute for Biocomputation and Physics of Complex Systems (BIFI) and leads the Complex Systems and Networks Lab (COSNET). He holds international affiliations as Research Director at CENTAI Institute (Turin, Italy) and External Professor at the Complexity Science Hub Vienna (Austria), and was previously a Principal Scientist and Area Coordinator at the ISI Foundation. PhD in Physics, University of Zaragoza (Summa Cum Laude, 2000) Ramón y Cajal Fellow (2005-2010) First-class Fellow, ISI Foundation Fellow, American Physical Society (2021) Fellow, Network Science Society (2022) His research focuses on complex systems , network science , epidemic modeling , and social dynamics . He investigates disease dynamics, diffusion processes, nonlinear systems, and higher-order network structures using tools from physics, mathematics, and data science. His work bridges theoretical foundations with real-world applications in public health, urban systems, and social behavior. Recent publications highlight trends in multilayer and higher-order networks , digital twins of cities , epidemic modeling with behavioral feedback , and social contagion in complex systems . His research integrates data-driven modeling, machine learning, and network theory to study urban resilience, information spread, and collective behavior. Complex Systems Society’s Senior Scientific Award (2019) Highly Cited Researcher (Web of Science, 2019) Service Award, Complex Systems Society (2022) Editor-in-Chief, Journal of Complex Networks Editor, PLoS Computational Biology, Chaos, Solitons & Fractals, Proceedings of the Royal Society A Divisional Associate Editor, Physical Review Letters (2014-2020) Prof. Moreno has supervised 23 PhD theses and over 30 master’s theses. He has led multiple EU-funded projects and has served on advisory boards for the EU Future and Emerging Technologies and the WHO Collaborative Center on Complexity Sciences for Health Systems. His collaborative network spans institutions in Europe, Latin America, and North America. He leads the Complex Systems and Networks Lab (COSNET) at BIFI, a multidisciplinary team focused on network theory, computational epidemiology, and social dynamics. The lab develops models for epidemic forecasting, urban resilience, and collective behavior using high-performance computing and data analytics.

Michael GroechenigView profile

Professor

• Algebraic Geometry
• Arithmetic Geometry
• Higgs Bundles
• +4 more

Michael Groechenig is a Professor at the University of Toronto in the Mathematical and Computational Sciences department. He also serves as Vice Chair for both Mathematics and Statistics. His research spans arithmetic geometry , Higgs bundles , p-adic integration , and algebraic K-theory , often bridging number theory with geometric problems. Research Trends: His recent work focuses on arithmetic approaches to geometric problems, including moduli spaces of Higgs bundles, Zakharevich K-theory , and p-adic integration techniques. Key themes include the interplay between algebraic geometry , homotopy theory , and category theory . Scientific Awards: Alfred P. Sloan Fellowship (2022) Marie Skłodowska-Curie Fellowship Chapman Fellowship at Imperial College Teaching and Workshops: He has taught courses on complex manifolds , algebraic stacks , and arithmetic techniques in algebraic geometry . He co-organized online workshops on Higgs bundles (2020) and K-theory (2021).

Arnaud DurandView profile

Professor

• Structural Complexity
• Descriptive Complexity
• Finite Model Theory
• +4 more

Arnaud Durand is a Professor at Paris-Diderot University's Faculty of Mathematics since 2005. He currently serves as Director of the Mathematics Department (UFR de mathématiques) and has held several leadership roles including Pole Manager (2006-2010), Deputy Director (2010-2014), and Co-Director (2014-2017) of the GDR IM - Mathematical Computer Science research group. He previously headed the Master's program in Mathematical Logic and Foundations of Computer Science (2007-2013) and the Logic research team (2014-2017), and was instrumental in establishing the university's Mathematics and Computer Science double degree program (2009-2016). Education includes: Ph.D. from University of Caen (1994-1996) Postdoctoral research at University of Pisa (1997-1998) His research bridges mathematics and computer science, focusing on: Structural complexity : Counting and enumeration problems Descriptive complexity : Logical characterization of complexity classes Database theory : Query evaluation and tractability Finite model theory : Logical aspects of finite structures Graph/hypergraph theory : Algorithmic and structural properties Recent publications demonstrate a strong emphasis on computational complexity, logical methods in computer science, and database theory, with frequent exploration of counting problems, team semantics, and conjunctive query evaluation. Research consistently intersects theoretical computer science, mathematical logic, and discrete mathematics. Student Advising : Has supervised doctoral candidates including: David Duris (2006-2009) Yann Strozecki (2007-2010) Nicolas de Rugy-Altherre (2011-2015, co-supervised) Florent Capelli (2013-2016) Alexandre Vigny (current, co-supervised)

Viktor ZamaraevView profile

Lecturer

• Theoretical Computer Science
• Graph Theory
• Combinatorics
• +3 more

Viktor Zamaraev is a Lecturer in the Department of Computer Science at the University of Liverpool since 2019. Previous roles include Research Associate at Durham University (2017-2019), Research Fellow at the University of Warwick's Mathematics Institute (2014-2017), and concurrent appointments as Associate Professor at Higher School of Economics, Assistant Professor at Lobachevsky State University of Nizhni Novgorod, and Researcher at LATNA (2011-2014). His educational background features: Ph.D. in Theoretical Computer Science from Lobachevsky State University of Nizhni Novgorod (2009-2012) M.Sc. in Applied Mathematics from Lobachevsky State University of Nizhni Novgorod (2003-2009) Zamaraev specializes in theoretical computer science with concentrated expertise in discrete mathematics and graph theory. His research examines structural properties of graph classes, combinatorial optimization, and algorithmic complexity in network analysis, often bridging abstract theory with computational applications. He frequently investigates width parameters and extremal graph problems, evidenced by recurring invitations to major combinatorics and algorithms conferences. His seminar engagement pattern since 2010 reveals consistent focus on graph algorithms across institutions like Oxford, Warwick, and Bristol, with notable invited presentations at the European Congress of Mathematics (2021) and International Conference on Network Analysis (2021). No scientific awards are documented in the source material. The text provides no details about student advising or research grants. His industry experience includes software design roles at Mera (2004-2007), Intel (2007-2008), and Auslogics Software (2009), suggesting early career industry-academia integration. He was affiliated with the Laboratory of Algorithms and Technologies for Networks Analysis (LATNA) at Higher School of Economics from 2011 to 2014, where he contributed to Russia-based network analysis research before moving to UK institutions.

Lionel ParreauxView profile

Assistant Professor

• Programming Languages
• Type Systems
• Functional Programming
• +3 more

Lionel Parreaux is an Assistant Professor at The Hong Kong University of Science and Technology (HKUST) in the Department of Computer Science and Engineering since February 2021. He is actively recruiting students for his research group focusing on programming languages, type systems, and compiler optimization. He earned his PhD in 2020 from École polytechnique fédérale de Lausanne (EPFL) under the Data Analysis Theory and Applications Laboratory (DATA), where he developed the Squid type-safe metaprogramming library for Scala. His research spans Programming Languages Type Systems Functional Programming Generative Programming Compiler Optimization Language Design Recent publications (2024) cover type inference, metaprogramming, and compiler optimization. Notable works include Scope-safe metaprogramming Deforestation techniques Conditional syntax design Subtyping constraints Lazy memory management These reflect his focus on theoretical and practical language implementation challenges. Current research projects include "A Lightweight Type System for Scope and Effect Safety" (2024, Huawei Technologies) "Optimizing Functional Programs by Building on Optimal Graph Reduction Techniques" (2022-2025, RGC Early Career Scheme) He supervises 8 PhD/MPhil students and serves on program committees for POPL, APLAS, GPCE, and SPLASH symposia.

Nick BartonView profile

Professor

• Evolutionary Genetics
• Population Genetics
• Speciation
• +9 more

Nick Barton is a Professor and Group Leader at the Institute of Science and Technology Austria (ISTA), where he leads research in evolutionary genetics. He is affiliated with the SFB Polygenic Adaptation at the University of Veterinary Medicine Vienna, reflecting collaborative research networks in Austria. His work bridges theoretical models and empirical data, particularly in hybrid zones and speciation. PhD in Genetics, University of East Anglia (1979) B.A. in Natural Sciences (Genetics), University of Cambridge (First Class, 1976) His research interests center on evolutionary genetics , with a focus on hybrid zones , speciation , polygenic adaptation , and quantitative genetics . He uses mathematical models to study selection on large numbers of genes and spatially continuous populations. His long-term field study of Antirrhinum majus in the Spanish Pyrenees integrates genomics, ecology, and pollinator behavior to understand how species diverge. He also investigates theoretical models of recombination, coalescence with selection, and evolutionary computation . The recent publications reflect a strong trend in genomic analysis of hybrid zones , fitness landscapes , self-incompatibility evolution , and theoretical models of information accumulation in evolution . Many studies combine field data with advanced computational methods like ancestral recombination graphs (ARGs) and population genomic inference. His scientific awards include: Royal Society Darwin Medal (2006) Linnean Society Darwin-Wallace Medal (2009) ERC Advanced Grants (2009, 2010) Erwin Schrödinger Prize (2013) Mendel Medal (2013) Royal Society/Wolfson Research Merit Award (2005–2010) Nick Barton has supervised numerous PhD students, including Parvathy Surendranadh, Oluwafunmilola Olusanya, Michal Hledik, and Eniko Edelsbrunner, whose theses cover topics like local adaptation, genetic load, and self-incompatibility. His research has been supported by major grants from the ERC (€1.9M), EPSRC, NERC, BBSRC, and FWF (€399K for Snapdragon speciation). He leads a vibrant research group involving postdocs and PhD students working on speciation, hybridization, and theoretical population genetics. The group conducts annual fieldwork in the Pyrenees, studying snapdragon hybrid zones, and fosters interdisciplinary collaboration with researchers in the UK, France, and Australia. His lab, the Research Group of Nick Barton , is based at ISTA and includes postdocs like Hilde Schneemann, Daria Shipilina, and Benjamin Wölfl, and PhD students such as Louise Fouqueau and Gemma Puixeu. The group is known for integrating theoretical models with empirical data and for its collaborative, team-oriented approach to evolutionary research.

Laura Fernández RoblesView profile

Professor

• Computer Vision
• Intelligent Systems
• Machine Learning
• +4 more

Laura Fernández Robles is a Professor at the Department of Mechanical, Informatics and Aerospace Engineering within the University of León, Spain. She leads research in computer vision, intelligent systems, and machine learning applications in engineering projects. Education: PhD in Engineering (2016), Universidad de León Thesis: "Técnicas de reconocimiento de objetos en aplicaciones reales" Supervised by Dr. Manuel Castejón Limas, Dr. Nicolai Petkov, and Dr. Enrique Alegre Gutiérrez Research Focus: Her work bridges computer vision, biometric identification, and engineering optimization, with recent emphasis on explainable AI, smishing detection, and sustainability integration in engineering projects. Publication Trends: Over the last decade, her research has spanned object recognition systems, audio embeddings for speech tasks, cybersecurity frameworks, and interdisciplinary applications in agriculture and education. Her methodologies often combine deep learning, feature engineering, and robust pattern analysis.

• Parameterized Complexity
• Graph Algorithms
• Approximation Algorithms
• +5 more

Erik Jan van Leeuwen is an Assistant Professor in the Department of Information and Computing Sciences at Utrecht University, Faculty of Science. His research focuses on theoretical computer science, particularly in algorithm design and analysis. Education: PhD in Computer Science, University of Amsterdam, 2009 Master's degree (cum laude), Utrecht University, 2004 Propaedeutic degree (cum laude), Utrecht University, 2000 Research Interests: Erik Jan van Leeuwen's research lies in the core areas of parameterized complexity , approximation algorithms , graph algorithms , and computational geometry . He investigates structural properties of graphs, especially H-free graphs , and develops efficient algorithms for hard problems in network design, connectivity, and optimization. His recent work explores streaming algorithms , survivable networks , and forbidden subgraph frameworks , contributing to both theoretical foundations and algorithmic techniques. Publication Trends: His recent publications show a strong focus on complexity frameworks for forbidden subgraphs, parameterized algorithms for network and graph problems, and structural graph theory. He frequently collaborates on problems involving diameter, connectivity, vertex covers, and disjoint paths in restricted graph classes. There is a clear trend toward fine-grained and parameterized complexity, often leveraging structural decompositions and kernelization. Scientific Awards: Best paper award at IWOCA 2024 Best paper award at IPEC 2023 Best student paper award at IPEC 2022 Third Place, Parameterized Algorithms Challenge Philips Mathematics Prize for PhD Students BRICKS 2007 Dissemination award Advising and Grants: He supervises PhD students Jelle Oostveen (defense scheduled 2025) and Sukanya Pandey (defended 2024, now postdoc at RWTH Aachen). As Principal Investigator, he secured a 2019 NWO grant on Parameterized Algorithms for Network Analysis . Other grants include Utrecht University funding for projects on Fake News in Social Networks and Prediction Methods for Sociological Theory . Labs and Teams: He is affiliated with the Theoretical Computer Science group at Utrecht University. He has been involved in organizing Lorentz workshops on Fine-grained and Parameterized Computational Geometry (2025) and Graph Decompositions (2022), indicating active collaboration in specialized research communities.

• Machine Learning
• Deep Learning
• Cybersecurity
• +7 more

Amandeep Kaur is a researcher with affiliations across multiple institutions including the University of Cambridge , IIT Delhi , and Central University of Punjab . Her work spans interdisciplinary fields such as machine learning , deep learning , cybersecurity , and biomedical signal processing . Her research focuses on solving complex problems in healthcare, network security, and agricultural technology. Key contributions include optimizing SDN environments for DDoS attack detection, advancing 6G IoMT applications, and developing deep learning models for rice disease detection and COVID-19 classification using X-rays. The 15 most recent articles highlight her expertise in multi-objective optimization , hybrid algorithms , and federated learning for privacy protection. Topics range from smart city management and blockchain security to medical image segmentation and sentiment analysis in multilingual contexts.

Graham WilliamsView profile

Professor

• Machine Learning
• Data Science
• AI for Social Good
• +4 more

Graham Williams is a Professor and Chief Scientist at the Software Innovation Institute within the School of Computing at the Australian National University (ANU). He holds a PhD in Computer Science (Machine Learning) from ANU and a BSc Hons in Mathematics (First Class) from the University of Adelaide. His work focuses on developing AI and Machine Learning solutions for real-world applications, emphasizing open source practices and ethical technology deployment. Research Interests: Graham's expertise includes Democratizing AI through platforms like MLHub and Rattle Privacy-preserving data analysis over distributed systems (e.g., Ecosysl.net) Knowledge graph development for entity resolution and link discovery Federated learning and differential privacy methodologies Key Projects: He leads projects such as Privacy-by-design Indigenous health infrastructure in collaboration with the Yarrabah Community Closed-loop brain stimulation treatment for depression Scalable data integration tools for industrial chemicals evaluation His work during the pandemic included developing the CRISPER system for real-time epidemic response. Professional Contributions: Graham has held leadership roles at Microsoft, the Australian Taxation Office, and CSIRO, where he pioneered Australia's first data mining research group. He is a proponent of open source software and innovative data science education.

• Geometric Group Theory
• Hyperbolic Groups
• CAT(0) Spaces
• +4 more

Abdul Zalloum is a faculty member at the Institute for Advanced Study in Mathematics, Harbin Institute of Technology (HIT). Previously, he was a post-doctoral fellow at the University of Toronto, working with Kasra Rafi and Giulio Tiozzo. He obtained his PhD in Mathematics in May 2019 under the supervision of Johanna Mangahas and Ruth Charney. His research focuses on geometric group theory, with a particular emphasis on generalizations of hyperbolicity, boundaries of groups, CAT(0) spaces, and cube complexes. He explores how geometric properties of spaces inform algebraic structures of groups, such as the relationship between tree geometries and free groups. Recent work has delved into hierarchically hyperbolic groups and their connections to CAT(0) cube complexes. His articles often address topics like Morse geodesics, injectivity properties, and geometric modeling of group actions. Zalloum has no listed scientific awards but maintains an active research profile, with a focus on theoretical contributions to geometric group theory. His advising record and grant activities are not detailed in the available information. He is affiliated with labs/teams through his research collaborations but no specific teams are explicitly mentioned.