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.
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.
Celina Miraglia Herrera de Figueiredo is a full Professor at the Systems Engineering and Computer Science Program (PESC) of COPPE, the Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering at the Federal University of Rio de Janeiro (UFRJ). She holds a PhD in systems and computer engineering from UFRJ and a postdoctoral degree from the University of Waterloo, Canada. She is a CNPq Level 1A Research Fellow and a FAPERJ Cientista do Nosso Estado awardee, and leads the algorithms and combinatorics research group at COPPE/UFRJ. University: Federal University of Rio de Janeiro School: Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering Department: Systems Engineering and Computer Science Program Academic Rank: Professor Email: celina@cos.ufrj.br Her research centers on theoretical computer science, with a focus on graph theory, algorithms, computational complexity, and combinatorial optimization. She has made significant contributions to the understanding of graph classes such as perfect graphs and snarks, algorithm design, and computational complexity. Her work is grounded in the Mathematics Subject Classification codes 05-XX (Combinatorics), 68-XX (Computer Science), and 90-XX (Operations Research). The most recent publications indicate a strong trend in analyzing the computational complexity of graph problems (e.g., MaxCut, Steiner Tree, total coloring) on structured graph classes such as interval, permutation, and path graphs. Her work frequently involves proving NP-completeness results, developing parameterized algorithms, and studying graph invariants like pebbling numbers and chromatic numbers. She consistently publishes in high-quality journals such as Discrete Mathematics , Discrete Applied Mathematics , and RAIRO Operations Research . Giulio Massarani Award for Academic Merit (2006) COPPE Fifty Years Award (2013) CNPq Research Fellowship (Level 1A) FAPERJ Cientista do Nosso Estado Member of the Brazilian Academy of Sciences (2023) Celina has advised numerous students, including Raphael Machado, Vinícius de Sá, Alexsander Melo, and Ana Silva, and has secured significant research funding from CNPq and FAPERJ. She is deeply involved in the academic community, serving on the editorial boards of RAIRO Theoretical Informatics and Applications, Bulletin of the Brazilian Mathematical Society, and Matemática Contemporânea. She has also been a key organizer and committee member for major international conferences such as LAGOS, WG, LATIN, and FCT, reflecting her leadership in the fields of algorithms and combinatorics. She coordinates the Center of Excellence in Randomized, Quantum, and Approximative Algorithms and has been a driving force in promoting women in science, serving on the jury of the L'Oréal–UNESCO–ABC Program for Women in Science. Her Erdős number is 2, highlighting her extensive collaborative network in mathematics and computer science.
Professor Anuj Dawar is a leading academic in Theoretical Computer Science at the University of Cambridge's Department of Computer Science and Technology. He holds a PhD from the University of Pennsylvania (1993) and has been a faculty member since 1999. His research focuses on computational complexity via logic, descriptive complexity, and finite model theory, with applications to databases, verification, and games. Education: PhD in Computer Science, University of Pennsylvania (1993) Masters, University of Delaware Bachelor's, Indian Institute of Technology (Delhi) Research Interests: His work bridges logic and computation, investigating limits of symmetric algorithms and complexity through formal languages. Notable themes include: Descriptive complexity and homomorphism preservation Finite model theory and its applications Algorithmic model theory and constraint satisfaction Professional Activities: Editor-in-Chief, ACM Transactions on Computational Logic Former president of European Association for Computer Science Logic Committee roles for Gödel Prize, Church Award, and Nerode Award Advising & Teaching: Supervised over 15 PhD students and taught advanced courses like Quantum Computing, Complexity Theory, and Foundations of Functional Programming. Currently on sabbatical (2024–25).
Nutan Limaye is a Professor at the Department of Theoretical Computer Science , IT University of Copenhagen , specializing in Algorithms , Computational Complexity , and Algebraic Circuits . She actively contributes to research on polynomial complexity, quantum computation, and lower bound techniques. Key Research Areas : Algebraic Circuit Complexity, Polynomial Computation, Graph Isomorphism, Boolean Satisfiability Current Projects : FLows : Formula complexity and lower bounds (2024-2026) DIREC: OnlineAlgo : Digital research initiatives (2022-2025) BARC2 : Basic Algorithms Research Copenhagen (2024-2029) Scientific Recognition includes the FOCS Best Paper Award (2022) . Her work frequently appears in top conferences like CCC , FSTTCS , and SIGACT News , with recent collaborations in Denmark and international institutions. She contributes to public understanding through media appearances on topics like basic computer science research and BARC's initiatives .
Dmitriy (Tim) Kunisky is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University's Whiting School of Engineering. He is also affiliated with the Data Science and AI Institute, the Department of Mathematics, and the Algorithms and Complexity Group at Johns Hopkins. Dr. Kunisky received his bachelor's degree in mathematics from Princeton University, worked as a software engineer for Google, earned his PhD in mathematics from the Courant Institute at NYU under the supervision of Afonso Bandeira and Gérard Ben Arous, and was a postdoctoral associate in computer science at Yale University before joining Johns Hopkins. His research broadly concerns how probability theory and mathematical statistics interact with computational complexity and the theory of algorithms. He investigates the mathematical phenomena that govern the power and limitations of algorithms processing massive and high-dimensional inputs, drawing on asymptotic statistics, convex geometry, random matrix theory, statistical physics, and representation theory. His work includes studying convex relaxation algorithms on combinatorial optimization problems, computational intractability in high-dimensional statistics, pseudorandomness, and experimental approaches to number theory and combinatorics. His recent publications demonstrate a consistent focus on the intersection of computational complexity, statistical inference, and random matrix theory. There's a clear trajectory from theoretical foundations to practical algorithmic applications, with particular emphasis on information-computation gaps, spectral methods, and the sum-of-squares hierarchy. His work often bridges theoretical computer science with statistical physics approaches. Dr. Kunisky actively advises graduate students at Johns Hopkins, including PhD candidates in Applied Mathematics and Statistics. He has taught courses on Random Matrix Theory in Data Science and Statistics, Probability Theory, Sum-of-Squares Optimization, and Modern Probability for Theoretical Computer Science, demonstrating his commitment to both research and education in mathematical data science.
Yi Liu is an Assistant Professor of Data Science jointly appointed in the Department of Applied Mathematics & Statistics and the Department of Computer Science at Stony Brook University. Previously, he served as an Assistant Professor in the Department of Computer Science at Florida State University. His academic background includes a Ph.D. in Computer Science from Texas A&M University (2022), an M.E. in Biomedical Engineering (2015), and a B.E. in Electronic Engineering (2012), both from the University of Science and Technology of China. Dr. Liu's research focuses on cutting-edge developments in artificial intelligence and computational science, with particular emphasis on: Geometric deep learning architectures for molecular and material systems AI-driven scientific discovery (AI4Science) Large language models for scientific applications 3D graph neural networks for molecular modeling Neural operators for physical system simulations His recent publications demonstrate a strong focus on developing novel AI methodologies for scientific applications, particularly in computational chemistry and materials science. Research trends include geometric deep learning for 3D molecular graphs, neural operators for physical modeling, explainable AI for chemical systems, and transformer-based approaches for material property prediction. The work consistently bridges fundamental AI research with applications in drug discovery, materials design, and scientific computing.
Simone Severini is a Professor of Physics of Information at the University College London , affiliated with the Department of Computer Science . He is a Royal Society University Research Fellow and contributes to multidisciplinary groups including Intelligent Systems , UCL CS Quantum , UCL Quantum Science and Technology Institute , and CoMPLEX . Research Interests: His work bridges Quantum computing Machine learning Graph theory Quantum information theory Computational biology with a focus on quantum algorithms, classical simulation of quantum systems, and mathematical frameworks for physical correlations. Scientific Contributions: Recent publications span quantum state learning, non-Markovian dynamics, adversarial quantum learning, and graph isomorphism. His projects include Quantum Computing, Information, and Algebras of Operators and the Distributed Information initiative . Awards: Royal Society University Research Fellowship Best Paper Award at FCT2017
