Dr. Akhlaque Ahmad is an Assistant Professor in Software Engineering at TED University's Faculty of Engineering. He earned his Ph.D. in Electronics and Telecommunications from Politecnico di Torino, Italy, with industry experience at STMicroelectronics Milano and contributions to EU FP7 REVERIE project on virtual tele-immersion systems. His research spans: High-performance computing architectures Parallel algorithms for graph processing GPU optimization techniques Intelligent systems design Professor Ahmad teaches courses including Database Systems, Artificial Intelligence, and Operating Systems. He actively promotes interdisciplinary connections between software engineering and liberal arts education.
Arnaud Knippel is a Lecturer in the Department of Mathematical Engineering at INSA. He has been responsible for internships in the GM department since 2015 and is a member of the GM Department Council. His research focuses on network design and dimensioning using mathematical programming and graph theory, with applications in telecommunications, transport, logistics, electricity, physics, and chemistry. He also explores the intersection of Operations Research (RO), Artificial Intelligence (AI), and Data Science, particularly in partitioning/classification problems and subgraph isomorphism for pattern recognition. Knippel has coordinated major research projects such as XTerM (2016-2019, as INSA’s scientific coordinator) and Fractal Grid (2016-2019), both funded by ANR and FEDER. He contributed to CLASSE2 (2016-2018) and PLURADYS (2015), a health-focused classification project. His doctoral supervision includes Zacharie Ales (co-advised with LITIS & C. Gout), S. Benhida (co-supervised with ENSA Agadir), and I. Khames (with JG Caputo). His research interests emphasize theoretical and applied aspects of graph theory, optimization, and AI. Projects like XTerM and Fractal Grid highlight his work on network oscillations and electrical grid design, respectively. Despite no explicit mention of scientific awards, his extensive publication record and project involvement underscore his academic contributions.
March Boedihardjo is an Assistant Professor in the Department of Mathematics at Michigan State University (MSU). His academic journey includes postdoctoral roles at ETH Zurich (2022-2023), the University of California, Irvine (2021-2022), and UCLA (2016-2021). He earned his Ph.D. in Mathematics from Texas A&M University (2012-2016) under advisors Bill Johnson and David Kerr, and holds a B.Sc./M.Phil. from Hong Kong Baptist University (2007-2011). Boedihardjo's research focuses on operator algebras, random matrices, and privacy-preserving data synthesis. His work bridges functional analysis and modern data science, with contributions to covariance loss minimization, max-sliced Wasserstein distances, and privacy-utility tradeoffs in synthetic data. He has presented at venues like Carnegie Mellon's Probability/Math Finance Seminar and the International Conference on Machine Learning (ICML). His teaching includes advanced courses like MTH 890: Readings in Mathematics. His research outputs span foundational mathematics (e.g., C*-algebra representations on ℓp spaces) and applied topics (e.g., differentially private synthetic data generation). While no specific awards are listed, his active conference participation reflects scholarly engagement in operator theory, probability, and data science intersections.
Hung Viet Chu is a Visiting Assistant Professor at Texas A&M University (TAMU), specializing in Mathematics with a focus on approximation theory, Banach space theory, combinatorics, and number theory. He earned his PhD from the University of Illinois Urbana-Champaign (UIUC) in 2023 and holds a double major in Mathematics and Economics from Washington and Lee University (W&L), 2019. Education: PhD in Mathematics, UIUC (2023) Bachelor of Arts, Mathematics & Economics, W&L (2019) Research Interests: Chu’s work includes nonlinear approximation in Banach spaces, combinatorial structures in number theory (e.g., divisor functions, MSTD sets), and interdisciplinary applications in economics and behavioral science. Teaching: At TAMU, he teaches Calculus II and Complex Variables. At UIUC, he was a Teaching Assistant for Calculus and Matrix Theory, earning top student evaluations. His average teaching score at UIUC was 4.4/5. Awards: Three-time UIUC “List of Excellent Teachers” Advising: Mentors undergraduate researchers, including students like Kevin Huu Le* and Uihyeon Lee*. Collaborates with leading mathematicians like Kevin Beanland and Thomas Schlumprecht. Labs/Teams: Engages in collaborative research across functional analysis and combinatorics, with over 30 peer-reviewed articles.
James Chapman is an Assistant Professor at Boston University, with a dual affiliation in the Departments of Mechanical Engineering and Materials Science & Engineering. His primary appointment is in Mechanical Engineering. He holds a PhD in Materials Science and Engineering from the Georgia Institute of Technology (2020). His research focuses on computational materials informatics, integrating machine learning with atomistic simulations to design novel catalysts and corrosion-resistant materials. Current work emphasizes high-entropy alloys for hydrogen production and pollution mitigation. Key honors include the Junior Faculty Fellow from the Hariri Institute for Computing (2024), the Trusted Reviewer Award from the Institute of Physics (2023), and the Lamar H. Franklin Fellowship (2020). His research lab, the Materials Informatics Lab, explores interdisciplinary approaches at the intersection of machine learning and materials science. Notable contributions include advancements in graph neural networks for material characterization and predictive modeling of atomic structures using diffusion models. Publications highlight themes such as topological message-passing algorithms, stratified data analysis, and multiscale modeling of defects in materials. His work bridges fundamental computational methods with practical applications in energy storage and environmental sustainability.
Nikos Ntarmos is a Senior Lecturer at the University of Glasgow's School of Computing Science, with affiliations to the IDEAS research lab. He holds a PhD in Computing Engineering & Informatics from the University of Patras and specializes in distributed systems and large-scale data management. Research focuses on: Distributed data storage and indexing for NoSQL systems Efficient query processing for graph databases Scalable algorithms for big data analytics Recommendation systems and collaborative filtering Publications demonstrate evolving interests from distributed hash tables to contemporary graph processing and recommendation systems. Recent work increasingly addresses practical applications in education technology alongside core database research.
Stephen Kirkland is Professor of Mathematics and Associate Dean in the Faculty of Graduate Studies at the University of Manitoba. His research focuses on matrix theory, combinatorial mathematics, and spectral graph theory with applications to Markov chains and network science. Current investigations include Kemeny's constant analysis, quantum state transfer in graphs, and spectral properties of stochastic matrices. His work connects abstract matrix theory with practical applications in epidemiology, network analysis, and quantum computing. Kirkland serves as Editor-in-Chief of Linear and Multilinear Algebra and Senior Editor of Linear Algebra and its Applications. He previously served as President of the International Linear Algebra Society and on scientific advisory boards internationally. He currently supervises graduate students Hermie Monterde, Homer Franz De Vera, Aaron Rossi, and Max Wiebe. His research group investigates mathematical structures underlying complex systems and networks.
Ciaran McCreesh is a Research Fellow in the School of Computing Science at the University of Glasgow. His research focuses on solving hard combinatorial problems in practice, particularly in graph theory and subgraph finding, leveraging symbolic AI techniques like constraint programming and Boolean satisfiability. He explores closing the gap between theoretical worst-case complexity and practical performance through empirical algorithmics and computational experiments. His work also addresses algorithm reliability via proof logging, parallel hardware exploitation (bit-parallelism, multi-core computing), and algorithm engineering to improve solver accessibility. Research interests: Combinatorial optimization, constraint programming, proof logging, parallel computing, algorithm reliability. Developed the Glasgow Subgraph Solver, a constraint programming-based tool for hard subgraph isomorphism problems. Publications span graph computation models, certified solvers, and algorithmic advancements. His work emphasizes practical algorithmic improvements with rigorous validation methods. Supervised students include Matthew McIlree and José Antonio Rodríguez Bacallado.
Prof. Dr. Linus Kramer is a full Professor of Mathematics at the University of Münster, affiliated with the Mathematical Institute within the Faculty of Mathematics and Computer Science. His research focuses on differential geometry, Lie groups, geometric group theory, and nonpositive curvature. He has held roles such as Managing Editor of the Münster Journal of Mathematics and Editor of Mathematics Research Reports. Kramer is also BAföG-Beauftragter (student financial aid coordinator) for Mathematics programs and stellvertretender Beauftragter for teacher training programs. Education: Extensive academic background in mathematics, though specific degrees are not detailed in the provided texts. Research Interests: Explores geometric group theory, locally compact groups, buildings, and curvature properties. His work bridges algebraic structures with topological and geometric methods. Recent publications (2022–2024) emphasize fibrations, group algebras, and Hodge operators in diverse contexts. He collaborates internationally, with projects involving researchers like Karl Hofmann, Alexander Lytchak, and Petra Schwer. His advising spans PhD and Master’s students, including Sira Busch, Daniel Keppeler, and Philip Möller. Kramer’s grants and projects reflect his leadership in geometric and algebraic research initiatives. Labs/Teams: Leads the Research Group Geometry, Topology and Group Theory at the University of Münster, fostering collaborative research and hosting conferences on buildings and geometric structures.
Christopher Schafhauser is an Associate Professor at the University of Nebraska-Lincoln, specializing in operator algebras and functional analysis. His research focuses on C*-algebras, nuclearity, classification problems, and their applications to non-commutative geometry and mathematical physics. He collaborates extensively with leading mathematicians in the field, contributing to foundational work on nuclear dimension, E-theory, and quantum self-testing. His notable contributions include pioneering studies on nuclear C*-algebras, tracially complete structures, and KK-rigidity. Schafhauser's work bridges pure mathematics with applications in quantum information theory and group dynamics. He has published in top journals such as the Annals of Mathematics, Duke Mathematical Journal, and Communications in Mathematical Physics. No scientific awards are explicitly mentioned in the provided text. His research spans over 20 peer-reviewed articles, with recent emphasis on classification programs, topological graph algebras, and boundary actions of groups.
Efren Ruiz is a Professor in the Mathematics Department at the University of Hawaiʻi at Hilo (UHH), part of the College of Natural and Health Sciences (CNHS). He earned his Ph.D. in Mathematics from the University of Oregon in 2005 and his undergraduate degree from the University of Hawaiʻi at Mānoa. His academic career includes roles from Assistant Professor (2007–2012) to his current position since 2017. He also serves as Cooperating Graduate Faculty at UH Mānoa since 2024. Research Interests: His work focuses on associated algebras (notably Leavitt path algebras), dynamical systems, C*-algebras classification, and their structural theory. He has contributed extensively to operator algebras and functional analysis, with over 60 publications. Grants & Collaborations: Ruiz has secured funding from the Simons Foundation (twice), National Science Foundation (NSF), and NSA. Notable grants include a 2018–2023 Simons Foundation grant for C*-algebras classification and a 2011–2016 NSF grant for the Pacific Undergraduate Research Experience in Mathematics. Teaching & Mentorship: He has taught a broad range of courses from pre-calculus to advanced topics like Real Analysis and Abstract Algebra. His undergraduate collaborations include co-authorship with students like Marissa Loving and Katherine Todd on papers in Leavitt path algebras. Professional Engagement: He co-organizes UHH's CNHS Colloquium Series, where he presented a 2024 talk linking the 'Mad Veterinarian' puzzle to topological Markov chains. He has also authored peer-reviewed conference proceedings and served on editorial boards. Future Directions: His upcoming research includes exploring connections between recreational mathematics and algebraic structures, alongside continued work on C*-algebras and their applications.
Yilun Shang is an Associate Professor in the Department of Computer and Information Sciences at Northumbria University. Previously, he held an Associate Professor position at Tongji University's School of Mathematical Sciences (2014–2018). His research interests span complex systems, network science, and nonlinear dynamics. He earned a PhD in Applied Mathematics from Shanghai Jiao Tong University in 2010, followed by postdoctoral appointments at institutions including the University of Texas at San Antonio and the Hebrew University of Jerusalem. Shang's work focuses on three core areas: (1) properties of complex networks (e.g., robustness, percolation, epidemic models), (2) mathematical properties of random graph models (e.g., degree distribution, connectivity, algebraic indices), and (3) nonlinear dynamics in multi-agent systems (e.g., synchronization, consensus algorithms). He has organized international conferences and delivered invited talks on topics like Estrada indices in random graphs. His academic achievements include the 2016 Dimitrie Pompeiu Prize and a presentation at the 2018 International Congress of Mathematicians. He advises postgraduate students in complex networks and systems and investigates resilient consensus protocols, graph metrics, and topological indices with applications in chemistry and engineering.
Prof. Ingo Scholtes is a Full Professor of Machine Learning for Complex Networks at Julius-Maximilians-Universität Würzburg's Center for Artificial Intelligence and Data Science (CAIDAS). He holds a doctorate in computer science and mathematics from the University of Trier and has held roles including SNSF Professor at the University of Zurich, Full Professor at Bergische Universität Wuppertal, and Senior Assistant at ETH Zürich. His research focuses on higher-order graph analytics for temporal networks, machine learning, and computational social science. Education: PhD in Computer Science (University of Trier, Germany), Postdoctoral Research at ETH Zürich (2011–2016), and prior roles at Karlsruhe Institute of Technology and CERN. Research Interests: Machine learning on graphs, temporal network analysis, higher-order network models, and their applications in software engineering and social systems. He develops open-source tools like pathpy and git2net for network analysis. Recent Work: Focuses on causality-aware graph neural networks, temporal graph isomorphism, and network science applications in AI. Recent articles include studies on temporal network dynamics, path prediction, and Bayesian inference of network transitions. Awards: SNSF Professorship (2018), Junior-Fellowship (2014), and German Academic Scholarship Foundation (2004-2005). Active in editorial roles for EPJ Data Science and leadership in GI's Computational Social Science working group.
Daniele Zambon is a postdoctoral researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA), affiliated with Università della Svizzera italiana (USI) in Lugano, Switzerland. He is a member of the Faculty of Computer Science and the Graph Machine Learning Group, as well as the IEEE Task Force on Learning for Graphs. PhD : Informatics, Università della Svizzera italiana (USI), 2022 Master’s & Bachelor’s : Mathematics, University of Milan, Italy Visiting Researcher : University of Florida, University of Exeter Internship : STMicroelectronics, Italy His research lies at the intersection of machine learning and graph-structured data, with a strong emphasis on graph representation learning , learning in non-stationary environments , and time series analysis . He explores how to model dynamic graphs, detect anomalies and changes over time, and develop deep learning methods for spatiotemporal forecasting. His work integrates statistical testing, geometric deep learning, and neural architectures like Graph Neural Networks (GNNs) and Neural ODEs. The recent publications highlight a clear trend toward temporal and dynamic graph modeling , especially for time series forecasting and irregularly sampled data . There is a growing focus on generative and foundation models for graphs , uncertainty-aware learning , and the creation of benchmark datasets like PeakWeather. His work bridges theoretical contributions (e.g., statistical tests, Kalman filters on graphs) with practical applications in sensing, environmental modeling, and system monitoring. Co-author of patent: Method for the Detecting Electrocardiogram Anomalies and Corresponding System (US10610162B2) PhD thesis featured in D22 Excellent Computer Science Dissertations (2022) Associate Editor, IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS) Organizer of tutorials and special sessions at ICML, LoG, KDD, and ESANN Daniele actively contributes to the academic community through advising and teaching at USI’s Bachelor’s and Master’s programs. He has co-supervised research projects and co-organized educational initiatives such as tutorials on graph deep learning. His collaborative work involves grants and partnerships with institutions like MeteoSwiss, leading to impactful datasets and applied research. He is deeply involved in building research capacity through workshops and community engagement in the graph learning field. He is a core member of the Graph Machine Learning Group at IDSIA and contributes to the IEEE Task Force on Learning for Graphs , fostering international collaboration and setting research agendas in the domain of graph-based AI.
Oscar Defrain is an Associate Professor at Aix-Marseille University, affiliated with the Laboratoire d’Informatique et Systèmes (LIS UMR CNRS 7020) and the ACRO team. He holds a Ph.D. from Université Clermont Auvergne (2020) and completed a postdoc at the University of Warsaw. His research focuses on algorithms and combinatorics in graphs, hypergraphs, and lattice structures, with expertise in minimal dominating sets, maximal independent sets, and Boolean function dualization. Education : Ph.D. in Computer Science, Université Clermont Auvergne (2020) Research Interests : Defrain specializes in structural graph theory, hypergraph dualization, and algorithmic enumeration. His work spans combinatorial optimization, parameterized complexity, and lattice theory, with recent contributions to geometric graph certification, metric graph problems, and XOR-CNF signature enumeration. He coordinates the ANR JCJC PARADUAL project and participates in ERC Cutacombs and ANR DISTANCIA. Recent Publications (2024-2025) examine quasi-optimal bounds for induced paths, polynomial-delay isomorphism generation, hypergraph dualization with FPT-delay, and geometric graph certification. These works intersect graph algorithms, combinatorics, and theoretical computer science. Teaching : Defrain teaches graph theory, algorithmic enumeration, and programming (Java/Python) at Aix-Marseille University (M1, L1, L2) and Université Clermont Auvergne (M1, L3). He uses platforms like Moodle and AMeTICE for course materials.
