Andrea Simonetto is a Research Professor at the Applied Mathematics Unit (UMA) , ENSTA Paris, Institut Polytechnique de Paris. His work spans optimization, control theory, and learning algorithms for large-scale and streaming data , with applications in smart grids, intelligent transportation, personalized health, and quantum computing. Current research focuses on online algorithms for time-varying optimization , personalized optimization for cyber-physical systems , and variational quantum algorithms . Past contributions include theoretical and algorithmic advances in convex/non-convex optimization, distributed optimization (robotic networks, smart grids), and signal processing for sparse reconstructions and parallel computing in particle filtering. Key application domains include renewable energy integration , quantum state preparation , and human-in-the-loop control systems . His research is published in journals like ACM Transactions on Quantum Computing , IEEE Control Systems Letters , and Automatica .
Peter Buneman is a prominent academic affiliated with the University of Edinburgh, UK. His research primarily focuses on databases, data provenance, XML, and semistructured data. He has held significant roles in advancing computational models for data citation, provenance tracking, and distributed systems. Key areas of expertise include database systems, graph algorithms, and collaborative data curation platforms. Buneman's work integrates theoretical foundations with practical applications in pharmacology data, digital libraries, and scientific data management. His contributions to data provenance and citation systems have shaped standards in academic and industrial settings. Notable projects include the Database Wiki platform for collaborative data management and the MITra framework for graph traversal. Buneman collaborates extensively with co-authors like Susan Davidson, Wenfei Fan, and Val Tannen. Publications span 1974–2025, emphasizing methodologies for data integration, distributed computing, and computational biology. His work bridges theoretical computer science with real-world applications in biomedicine and information systems.
Martin Bridson serves as President of the Clay Mathematics Institute since 2018 and holds the Whitehead Professorship of Pure Mathematics at the University of Oxford's Mathematical Institute, where he has been a Fellow of Magdalen College since 2007. Previously, he was Head of Oxford's Mathematical Institute (2015-2018), Professor of Pure Mathematics at Imperial College London (2002-2007), and Professor of Topology at Oxford (1999-2002). His academic journey began with undergraduate studies at Hertford College, Oxford, followed by PhD work at Cornell University. Bridson's research centers on Geometric Group Theory, Topology, and Spaces of Non-Positive Curvature. His work explores the deep connections between algebraic structures and geometric properties, particularly focusing on metric geometry, profinite completions, and the topology of non-positively curved spaces. His influential monograph "Metric Spaces of Non-Positive Curvature" (co-authored with André Haefliger) has become a foundational text in the field. His recent publications (2024-2025) demonstrate continued leadership in geometric group theory, with significant contributions to profinite rigidity, CAT(0) geometry, and automorphism groups. These works reveal evolving research directions toward computational aspects of group theory and deeper connections with low-dimensional topology. Steele Prize for Mathematical Exposition (2020, with Haefliger) Fellow of the Royal Society (2016) Fellow of the American Mathematical Society (2015) Royal Society Wolfson Research Merit Award (2012) London Mathematical Society Whitehead Prize (1999) Bridson has secured major research funding including EPSRC Senior Fellowships (2007-2012, 1997-2002), EPSRC Platform Grants (2010-2015), and multiple NSF grants. His leadership extends to directing the Mathematical Institute at Oxford and presiding over the Clay Mathematics Institute, where he influences global mathematical research directions. His collaborative work spans institutions including Princeton, Geneva, and Lausanne, reflecting his international research network.
Manuel Penschuck is a Research Fellow at the Institute of Computer Science , Goethe University Frankfurt, Germany. His research focuses on algorithm engineering, graph theory, and scalable network generation, with emphasis on parallel computing, I/O-efficient algorithms, and random graph models. He actively contributes to conferences like ESA, SEA, and IPDPS, and has co-authored publications in top venues including LIPIcs , IEEE Transactions , and SIAM . His work includes engineering algorithms for non-linear preferential attachment , parallel shuffling , and hyperbolic graph generation . He has co-organized program committees for ESA, EuroPar, and SEA, and his collaborations span institutions such as MPI-INF, TU Darmstadt, and Australian National University. Recent publications highlight advances in uniform graph sampling, geometric network models, and distributed systems. His research integrates theoretical rigor with practical implementation, addressing challenges in big data and high-performance computing. He is a key contributor to the Networkit toolkit for large-scale network analysis.
Violetta Lonati is an Assistant Professor at the University of Milan 's Department of Computer Science since 2005. Her research spans Formal Languages and Automata (operator precedence languages, Wang automata, tiling systems) and Computer Science Education . She co-authored over 15 publications in theoretical computer science and education, focusing on 2D language recognition, logic characterization of automata, and pattern statistics in stochastic models. Education : PhD in Computer Science (2005) and Laurea in Mathematics (2001) from University of Milan Research Groups : ALaDDIn Lab for Didactics and Dissemination of Informatics, Bebras International Initiative Her work on Wang automata established their equivalence to tiling systems while introducing deterministic variants. In education, she designed workshops for schools and contributed to Italy's national computing curriculum proposal (2019). She held leadership roles at ACM ITiCSE (WG5 leader 2022), served as Associate Program Chair (2019-2022), and reviewed for top venues like ICER and SIGCSE TS. She received Google CS[4]HS and Informatics Europe awards for her educational contributions. Key Publications (2017-2001): Input-driven locally parsable languages (TCS 2017) Operator precedence logic characterization (SICOMP 2015) Snake-deterministic tiling systems (MFCS 2009) Graph fibrations and PageRank (RAIRO 2006) Pattern statistics in rational models (STACS 2005) Scientific awards include Google CS[4]HS (2011, 2017, 2019) and the Informatics Europe Best Practices in Education (2016). As part of ALaDDIn, she developed teacher training programs and graduate courses on computing education. Her teaching experience covers Algorithms & Data Structures (2013-2023), Computer Science Teaching (2014-2023), and courses for Biotechnology and Geological Sciences programs (2005-2007).
Arijit Bishnu is an Associate Professor at the Indian Statistical Institute in the Advanced Computing and Microelectronics Unit (ACMU). He has taught courses such as Design and Analysis of Algorithms , Randomized Algorithms , Computational Geometry , and Algorithms for Big Data over multiple years (2008–2025), focusing on theoretical and applied aspects of computer science. Research Interests: His work spans Theoretical Computer Science , Randomized and Approximation Algorithms , Computational Geometry , and Combinatorics . He explores problems in sublinear algorithms, streaming computation, geometric data analysis, and complexity theory. Publications: Recent papers include contributions to STOC 2025 , RANDOM 2025 , and APPROX 2024 , covering topics like property testing, triangle counting complexity, and streaming algorithms. Collaborations with researchers like Sourav Chakraborty, Gopinath Mishra, and Sayantan Sen highlight his interdisciplinary approach. Academic Leadership: He has co-organized research courses such as Approximation Algorithms and Topics in Algorithms and Complexity , emphasizing mentorship and knowledge dissemination in theoretical computer science. His comprehensive work integrates algorithmic innovation with rigorous mathematical analysis, advancing computational techniques for large-scale and geometric data.
Gaurav Rattan is an Assistant Professor in the Department of Applied Mathematics at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), where he joined in May 2024. His research focuses on the mathematical foundations of machine learning on graphs and discrete structures, with particular emphasis on theoretical aspects of graph neural networks. University of Twente, Department of Applied Mathematics (May 2024-present) TU Darmstadt, Postdoctoral Researcher in Pascal Schweitzer's group RWTH Aachen, DFG Eigene Stelle Researcher in Martin Grohe's group Dr. Rattan completed his PhD at IMSc Chennai under V. Arvind and earned his B. Tech. from IIT Bombay, establishing a strong foundation in theoretical computer science and mathematics. His research spans graph theory, algorithms, and machine learning on graphs, with specific expertise in graph isomorphism, graph homomorphisms, and the theoretical underpinnings of graph neural networks. He applies mathematical techniques from logic and algebra to develop theory-driven approaches for graph learning systems, with practical applications in optimization, bioinformatics, and databases. Dr. Rattan's publication record reveals a consistent focus on the intersection of theoretical computer science and machine learning. His recent work explores Weisfeiler-Leman algorithms, symmetry breaking techniques, and parameterized complexity of graph problems, demonstrating how classical graph algorithms connect with modern graph learning methodologies. His research provides crucial theoretical foundations for understanding the capabilities and limitations of graph neural networks. Active in the academic community, Dr. Rattan regularly presents at conferences including the Netherlands Mathematical Congress, SIGAlgo, LOGAMS, and specialized workshops on graph learning. Recent presentations include "From Graph Homomorphisms Densities to Graph Learning" at the Graph Learning Workshop at NITMB Chicago and "Color Refinement: One Algorithm, Many Facets" at SIGAlgo 2024.
Prof Ian M. Wanless is a Professor in the School of Mathematics at Monash University, Melbourne, Australia. He has held academic positions at institutions including the Australian National University (ANU), University of Melbourne, Christ Church Oxford, and Charles Darwin University. His research primarily focuses on combinatorics, with specializations in Latin squares, matrix permanents, graph theory, and algebraic structures. He has made significant contributions to the enumeration and properties of Latin squares, including groundbreaking work on transversals, orthogonality, and symmetry. Wanless has also explored connections between Latin squares and algebraic structures like quasigroups and loops. His education includes a PhD from ANU (supervised by Brendan McKay) and postdoctoral fellowships at Oxford and ANU. He has been awarded an Australian Research Council Future Fellowship (2011) and has led major research initiatives, including organizing international conferences (e.g., 5ICC in 2017). His work spans theoretical results and computational methods, with over 100 publications in top journals like Journal of Combinatorial Theory and SIAM Journal on Discrete Mathematics . Wanless’s research interests extend to design theory, hypergraphs, and permutation polynomials. He co-edits the Electronic Journal of Combinatorics and has held leadership roles in professional societies, including president of the Combinatorial Mathematics Society of Australasia. His current projects include studies on perfect 1-factorizations, covering radii of permutation sets, and algebraic properties of Latin squares.
George Dasoulas is a Postdoctoral Researcher at Harvard University's Department of Biomedical Informatics, affiliated with the Zitnik Lab. He holds a PhD in Computer Science from École polytechnique in Paris, France, and previously worked at Huawei Technologies France. His research focuses on graph machine learning, particularly in biomedical applications and telecommunications, with contributions to graph neural networks (GNNs), attention mechanisms, and topological deep learning. Education: Ph.D., Computer Science (DaSciM group, LIX, École polytechnique); Diploma in Electrical & Computer Engineering (National Technical University of Athens). His work includes developing Lipschitz-normalized attention layers, parametrized graph shift operators, and modularity-aware graph autoencoders. He has been recognized with the 2022 Wojcicki and Troper Fellowship from Harvard's Data Science Initiative. Key Research Themes: Graph Representation Learning, Topological Neural Networks, Equivariant Learning, Multimodal Learning Applications: Biomedical Informatics, Telecommunications, Sustainable AI His articles emphasize scalable GNN architectures, graph-based unlearning strategies, and multimodal protein phenotyping. He has contributed to open-source projects like LipschitzNorm and PGSO, and actively publishes in top conferences (ICML, ICLR, NeurIPS).
Denka Kutzarova is a Professor in the Department of Mathematics at the University of Illinois at Urbana-Champaign. She holds an office in Altgeld Hall and specializes in functional analysis, particularly focusing on the geometry of Banach spaces and approximation theory. Her work includes studies on subsymmetric sequences, norms in Banach spaces, and greedy algorithms. Education: 1983 Ph.D. in Mathematics, University of Sofia, Bulgaria Research Interests: Dr. Kutzarova’s research explores advanced topics in functional analysis, including the geometric properties of Banach spaces, renorming theory, and the structure of infinite-dimensional spaces. She investigates properties like uniform convexity, uniform smoothness, and the interplay between these properties and operator theory. Her work also delves into approximation theory, greedy algorithms, and the construction of explicit matrices with restricted isometry properties for compressed sensing applications. Publications Overview: Her recent work focuses on subsymmetric sequences, 2-rotund norms, and metric embeddings. Key themes include the structural analysis of Banach spaces, properties of sequence spaces, and applications to transportation cost spaces. Her research bridges theoretical functional analysis with practical applications in approximation and matrix theory. Additional Contributions: Beyond academic work, she authored a series of novels titled Journey of the Guinea Pig , exploring themes of academia and cultural identity. She also contributes poetry, including works published in both Bulgarian and Russian.
Laks V.S. Lakshmanan is a Professor in the Department of Computer Science at the University of British Columbia (UBC), within the Faculty of Science. His research focuses on data management, graph computing, machine learning, and algorithms, with notable contributions to dense subgraph discovery, influence maximization, and healthcare informatics. He teaches advanced courses on databases and data management, including CPSC 404 (Advanced Relational Databases) and CPSC 534L (Topics in Data Management). His awards include the ACM SIGMOD Research Highlight Award, the IEEE Data Science Best Paper Award, and recognition as an ACM Distinguished Scientist (2016). His work bridges theoretical algorithm design with practical applications in social networks, bioinformatics, and healthcare. Key research themes include optimizing graph algorithms for large-scale data, combating misinformation through network analysis, and developing efficient methods for subgraph enumeration and influence propagation. His recent publications explore topics like clinical event prediction (TRACE), cost-effective LLM selection (ThriftLLM), and cross-modal consistency in AI systems. Education: Details not explicitly provided in sources. Grants & Funding: Recipient of NSERC Discovery Accelerator Supplements. Labs/Teams: Engaged in UBC's data management research groups and collaborative initiatives with industry partners.
Roland Bauerschmidt is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He previously held a professorship at the University of Cambridge and conducted postdoctoral research at Harvard and the Institute for Advanced Study. His research focuses on probability theory, mathematical physics, and statistical mechanics, particularly in spin systems, renormalization group methods, stochastic dynamics, and random matrices. He has contributed extensively to understanding phase transitions, critical phenomena, and universality classes. Education: PhD from the University of British Columbia (advised by David Brydges and others), undergraduate degrees from ETH Zurich. Research interests include applications of supersymmetry in probability, Coulomb systems, and log-Sobolev inequalities. He has organized programs such as the HIM Trimester Program on Probabilistic Methods in Quantum Field Theory. His teaching spans graduate and undergraduate courses in probability theory, stochastic dynamics, and mathematical analysis at NYU, Cambridge, and international institutions. His work frequently intersects with renormalization group techniques, spectral gaps, and non-equilibrium dynamics. Recent publications explore log-Sobolev inequalities in spin systems, high-temperature discrete Gaussian models, and percolation transitions in random forests.
Kevin Aguyar Brix is a mathematician at the University of Southern Denmark (Odense, Denmark), where he holds a Reintegration Fellowship from the Carlsberg Foundation (CF23-1328). Previously, he worked as a postdoc at Lund University (Sweden), the University of Glasgow (Scotland), and the University of Wollongong (Australia), funded by grants from the Swedish Research Council, the Independent Research Fund Denmark, and the Carlsberg Foundation. PhD in Mathematics (2019) from University of Copenhagen, Denmark Member of the Centre for Symmetry and Deformation Supervised by Søren Eilers Kevin's research focuses on the intersection of pure mathematics, particularly exploring the connections between topological dynamical systems, operator algebras, and group theory. He specializes in symbolic dynamical systems and their encoding into C*-algebras, investigating how much dynamical structure is preserved or lost in this process. His work often employs operator algebraic tools to derive properties of the original dynamical systems, with specific emphasis on shift equivalence, covers of dynamical systems, and ideal structures of C*-algebras. Kevin's publications reveal a consistent focus on the interplay between dynamical systems and operator algebras. His work spans symbolic dynamics, groupoids, C*-algebras, and their ideal structures. Recent papers explore unital shift equivalence, Hausdorff covers for non-Hausdorff groupoids, and maximal ideals of reduced group C*-algebras. His research often involves collaborations with leading mathematicians worldwide, addressing fundamental questions about the relationship between algebraic structures and dynamical properties. Carlsberg Foundation Reintegration Fellowship (CF23-1328) Swedish Research Council Starting Grant (2024-2028) for "Dynamics, Groups, and C*-algebras: from one to many dimensions" Swedish Research Council Starting Grant (previous) Independent Research Fund Denmark International postdoctoral grant Carlsberg Foundation Internationalisation Fellowship Though specific details about Kevin's advisees are not provided in the available information, his extensive publication record suggests active mentorship of junior researchers. His research is supported by prestigious grants including the Carlsberg Foundation Reintegration Fellowship and a Swedish Research Council Starting Grant. These grants fund his work on "Dynamics, Groups, and C*-algebras: from one to many dimensions," indicating significant recognition of his research potential and contributions to the field. Kevin's collaborative approach is evident in his numerous co-authored papers with established and emerging researchers in operator algebras and dynamical systems. Kevin is actively involved in the international mathematical community, participating in workshops and collaborations across Europe and Australia. He co-organized the Glasgow Late August Symbolic dynamics, Groups, and Operators Workshop in 2022, which aimed to include young mathematicians in the interactions between group theory, dynamical systems, and operator algebras. His research network includes prominent mathematicians from institutions worldwide, reflecting his position within a vibrant interdisciplinary research community. He has presented his work at numerous conferences including IWOTA in Kent, seminars in Florianopolis, and workshops in Oberwolfach, demonstrating his active engagement with the global mathematics community.
Andrej Bogdanov is a Professor at the University of Ottawa in the School of Electrical Engineering and Computer Science . He earned his B.S. and M.Eng. from MIT and Ph.D. from UC Berkeley . Before joining Ottawa, he held positions at the Chinese University of Hong Kong , ITCS (Tsinghua) , DIMACS (Rutgers) , and the Institute for Advanced Study . He has served as a Visiting Professor at the Tokyo Institute of Technology (2013) and the Simons Institute (2017, 2021). Research Interests : Computational complexity, cryptography foundations, pseudorandomness, one-way functions, property testing, quantum algorithms, and sublinear-time algorithms. Teaching : Courses on Discrete Mathematics, Great Algorithms, Computational Complexity, and Cryptography at University of Ottawa, Chinese University of Hong Kong, and Rutgers University. Publications : 15+ recent works in TCC , CRYPTO , ICALP , RANDOM , and journals like Journal of Cryptology and Theory of Computing . Service : Program co-chair for SAC 2026 , and committee member for major conferences including CRYPTO , TCC , Eurocrypt , and FOCS . Advising : 12 current and former Ph.D./M.Phil. students, with postdoctoral advisees at institutions like IIT Palakkad and Academia Sinica . His work bridges theoretical computer science with applications in cryptography, quantum computing, and network security.
Joy Morris is a Professor at the Department of Mathematics & Computer Science at the University of Lethbridge. She earned her BSc from Trent University in 1992 and her PhD from Simon Fraser University in 2000 under Brian Alspach. Her academic journey includes tenure in 2005, promotion to Associate Professor, and full Professor status in 2015. Research Focus: Interactions between group theory and graph theory, with emphasis on Cayley graphs and automorphisms Key Contributions: Solving the distinguishing number problem for various graph families, advancing DCI/CI group theory Research Overview Her work bridges abstract algebra with graph theory through Cayley graphs. She investigates automorphism groups, Hamilton cycles, and graph symmetry properties. Notable contributions include resolving the DCI property for specific groups, analyzing Praeger-Xu graphs, and studying color-preserving automorphisms. Academic Leadership As a co-author of two influential open-access textbooks ( Proofs and Concepts with Dave Morris and her own Combinatorics text), she has shaped curriculum development and mathematical education outreach programs for parents in Alberta. Her teaching portfolio includes foundational courses like Math 2000 and advanced combinatorial theory instruction. Collaborative Impact With over 30 publications since 1996, her collaborations span international researchers including C. Praeger, P. Spiga, and E. Dobson. Current projects involve hypercube distinguishing costs and automorphism group analysis of vertex-transitive digraphs. She maintains active research into graphical regular representations and graph isomorphism problems.
