James Mcquillan is a Professor of Computer Science at the School of Computer Sciences , Western Illinois University. His research spans theoretical and applied computer science, with a focus on combinatorial structures and their applications in computational problems. Ph.D. , University of Western Ontario (1994) Book Author : Cryptography, Information Theory, and Error-Correction: A Handbook for the 21st Century (2021, Wiley) Research interests include: Core areas: Satisfiability of Boolean expressions, Combinatorics, Finite geometries, Desargues configurations, Graph labellings, Hyperovals Applications: Cryptography, Error correction, Pattern matching in strings, Bioinformatics Teaching awards highlight his excellence in graduate and undergraduate education. His recent work in Discrete Mathematics (2025) explores graph theory and combinatorial algorithms. Scientific contributions include Every even cycle of order at least 8 has a mirror labeling (2025) Awards : 2020-2021: College of Business and Technology Faculty Award for Excellence in Teaching 2018-2019: School of Computer Sciences Graduate Teacher of the Year 2017-2018: School of Computer Sciences Undergraduate and Graduate Teacher of the Year
Lucian Ilie is an Adjunct Professor in the Department of Computing and Software at McMaster University. His scholarly activity spans multiple disciplines including Computer Systems Theory, Bioinformatics, and Algorithm Design, with significant contributions to string algorithms and computational biology. Key research areas: Formal Languages, String Algorithms, Data Compression, Computational Biology, Bioinformatics, and Automata Theory Recent publications focus on protein interaction site prediction (2025-2023), string algorithm improvements (2022-2007), and spaced seed design for genomic analysis (2007). His work combines theoretical computer science with practical biomedical applications. Notable collaborative projects include developing explainable AI for medical report generation (2022) and creating optimized algorithms for genome assembly (2018). While no formal awards are listed, his research has been widely adopted with extensive Mendeley readership.
Gen-Huey Chen is a Distinguished Professor in the Department of Computer Science and Information Engineering at National Taiwan University, where he has served since 1987 (Associate Professor 1987-1992, Professor since 1992). He previously held leadership roles as Dean of the College of Science and Technology (2001-2005) and Department Chair (2001-2002) at National Chi Nan University. Education Ph.D. in Computer Management Decision, National Tsing Hua University, 1987 B.S. in Computer Science and Information Engineering, National Taiwan University, 1981 Research Interests Professor Chen's work centers on Graph Theory , Combinatorial Optimization , and Algorithm Analysis and Design , with significant contributions to discrete mathematics and network theory. His research bridges theoretical foundations and practical applications, particularly in structural graph problems and wireless network protocols, emphasizing efficient algorithmic solutions for complex computational challenges. Publications Trends His 2007-2009 publications reveal a cohesive research trajectory focused on graph-theoretic applications in networking. Key patterns include structural analysis of specialized graphs (chordal/circular-arc), fault tolerance in multiprocessor topologies (grids/tori), and bandwidth-optimized routing in mobile ad-hoc networks. These works consistently apply combinatorial optimization to solve real-world networking constraints while advancing theoretical graph algorithms. Advising and Grants No specific information regarding student advising or research grant funding is provided in the source material. Laboratory He directs the Discrete Algorithm Lab at National Taiwan University, which specializes in discrete algorithm development and wireless network protocol research.
Mark Daniel Ward is a Professor of Statistics and (by courtesy) of Agricultural & Biological Engineering, Computer Science, Mathematics, and Public Health at Purdue University. He currently serves as Executive Director of The Data Mine, a pioneering initiative that connects corporate partners with students for data science projects. His academic appointments span multiple departments reflecting his interdisciplinary approach to data science education and research. Ward earned his Ph.D. in Mathematics with Specialization in Computational Science from Purdue University in 2005, with a dissertation on the analysis of the multiplicity matching parameter in suffix trees under advisor Wojciech Szpankowski. He received his M.S. in Applied Mathematical Sciences from the University of Wisconsin-Madison in 2003 and his B.S. in Mathematics and Computer Science summa cum laude from Denison University in 1999. Ward's research spans probabilistic, combinatorial, and analytic techniques for the analysis of algorithms and data structures. His work connects theoretical mathematics with practical applications in data science, focusing on how mathematical principles can inform the development of efficient algorithms and data processing techniques. He has made significant contributions to the analysis of random structures, particularly in the context of digital trees, suffix trees, and combinatorial games. His recent work demonstrates a strong emphasis on data science education, developing innovative learning communities and curricular approaches that prepare students for data-intensive careers. His extensive publication record shows an evolution from purely theoretical work in combinatorics and probability toward increasingly applied research in data science education and interdisciplinary applications. While maintaining a strong foundation in mathematical theory, his recent work focuses on creating effective educational models that bridge academia and industry, particularly through The Data Mine initiative. Pillar of CERIAS Award to The Purdue Data Mine, 2023 Learning Community Academic Connection Award, 2022-2023 College of Science Diversity Award, 2022-2023 Mu Sigma Rho William D. Warde Statistics Education Award, 2016 Fellow of the Purdue University Teaching Academy, 2015-present Excellence in Research Award (multiple years) Ward has advised numerous students across multiple publications and initiatives, with a particular focus on creating inclusive learning environments. His grant portfolio includes significant funding from the National Science Foundation, National Institute of Food and Agriculture, and Lilly Endowment, totaling millions of dollars for projects related to data science education, computational infrastructure, and interdisciplinary research. The Data Mine initiative coordinates corporate partnerships with over 50 organizations including major companies like Microsoft, Google, John Deere, and government agencies. As Executive Director of The Data Mine, Ward leads a transformative educational model that integrates data science across the Purdue curriculum. This initiative creates meaningful partnerships between students and corporate/government entities to solve real-world problems. The program has grown significantly under his leadership, becoming a national model for experiential data science education that emphasizes accessibility and inclusion for students from diverse backgrounds.
Oswin Aichholzer is an Associate Professor in the Department of Algorithms and Theory at TU Graz, Austria. He is affiliated with the Institute of Algorithms and Theory and the Institute of Software Engineering and Artificial Intelligence. His research focuses on computational geometry, graph theory, and combinatorial geometry, with an emphasis on geometric graphs, matching problems, and crossing minimization in graph drawings. He is also involved in teaching theoretical computer science and actively contributes to research projects and courses through the TU Graz's Research Portal (PURE). His recent work explores structures like crossing-free Hamiltonian cycles, bicolored order types, and folding algorithms for polyominoes. Key research interests include algorithmic problems in geometric configurations, graph isomorphisms, and the development of efficient algorithms for problems in discrete mathematics. He has published extensively on topics such as flip operations in graphs, geometric matchings, and the analysis of complete graph drawings. His work often bridges theoretical foundations with practical algorithmic solutions, addressing challenges in both computational geometry and combinatorics. Dr. Aichholzer’s contributions span multiple areas, including the study of polyomino folding, bichromatic matchings, and the characterization of graph rotation systems. He maintains an active research presence, with recent publications in top conferences like the Symposium on Computational Geometry (SoCG). His research portal provides further details on ongoing projects and collaborations.
Leen Stougie is a part-time Full Professor of Operations Research at Vrije Universiteit Amsterdam and holds positions as Senior Researcher and Head of the Life Sciences and Health group at CWI (Centrum Wiskunde & Informatica). He has been affiliated with TU Eindhoven and Tinbergen Institute. His research focuses on Operations Research, Combinatorial Optimization, Scheduling, Approximation Algorithms, and Computational Biology. Education: PhD in Operations Research from Erasmus Universiteit Rotterdam (1985). Teaching includes the Advanced Linear Programming course via LNMB/Mastermath/DIAMANT. His work contributes to UN Sustainable Development Goals through algorithmic solutions in biology and optimization. Research highlights include phylogenetic network construction, string matching algorithms, and scenario-based scheduling. He has secured grants like the Marie Skłodowska-Curie Actions (2020) and NWO-GROOT (2001). Advised six PhD theses but no student names listed. Active in organizing conferences and reviewing activities.
Romain Raveaux is an Assistant Professor at the University of Tours' Computer Science Laboratory (LIFAT). His research intersects machine learning, operations research, and graph theory for environmental monitoring and document analysis. Applications include insect counting for biodiversity assessment and document image quality evaluation. Raveaux holds a PhD from the University of La Rochelle and has authored over 40 publications. His methodologies combine combinatorial optimization with structural pattern recognition, addressing problems like graph matching and edit distance computation. Research Highlights Graph-based representations for pattern recognition Mixed-integer programming for graph matching Machine learning models for insect classification Document image analysis frameworks
Zsuzsanna Liptak is an Associate Professor in the Department of Computer Science at the University of Verona, Italy, specializing in algorithmic bioinformatics and string algorithms within the INFO-01/A Informatics academic sector. Her research focuses on: Algorithmic solutions for biological data String and sequence algorithms Mass spectrometry data interpretation Non-alignment based sequence comparison Applications to EST genomic sequences and nanopore technologies Dr. Liptak actively contributes to research projects including 'Securing Decentralized Finance and Remote Healthcare Systems - SHIELD' (starting October 2024) and 'Novel Methodologies and Tools for Next Generation Cyber Ranges - NOMEN' (starting May 2024), working at the intersection of theoretical computer science and practical bioinformatics applications. She serves on scientific committees for major international conferences: WABI (Workshop on Algorithms in Bioinformatics) CPM (Combinatorial Pattern Matching) SPIRE (String Processing and Information Retrieval) IWOCA (International Workshop on Combinatorial Algorithms) Her teaching portfolio spans multiple programs: Computational Analysis of Genome-Scale Sequences (Medical Bioinformatics Master's) Discrete Biological Models (Bioinformatics Bachelor's) Fundamental algorithms for Bioinformatics Advanced Data Structures for Textual Data (PhD program) She conducts research within the Algorithms and Algorithmic Bioinformatics research groups at the University of Verona, with connections to the INdAM Research Unit.
Robert Susik is a researcher at the Institute of Applied Computer Science within the Faculty of Electrical, Electronic, Computer and Control Engineering at Lodz University of Technology. His core research focuses on algorithm design, particularly in pattern matching, blockchain applications, and machine learning implementations for healthcare technology. Research interests span: Computational efficiency in algorithm design Blockchain-based academic certification systems Neural networks for mobile health monitoring Human-computer interaction optimizations Recent publication trends demonstrate: Progressive shift toward applied machine learning (2021-2025) Consistent focus on optimization techniques in pattern matching Emerging work in blockchain and cryptocurrency applications Laboratory engagement includes computational algorithm development and interdisciplinary collaborations bridging computer science with healthcare technology.
Torsten Mütze is a Professor at the Institute of Mathematics at the University of Kassel. Previously, he held positions as an Assistant Professor at the University of Warwick (2019–2024) and was affiliated with Charles University Prague's Department of Theoretical Computer Science and Mathematical Logic. His academic journey includes a postdoc under Martin Skutella at TU Berlin, research stays at Georgia Tech and ETH Zurich, and a software engineering role at Supercomputing Systems Zurich. Education: PhD in 2011 from ETH Zurich under Angelika Steger's supervision Master's and Bachelor's degrees in related fields Research Interests: Focuses on discrete mathematics and theoretical computer science, including combinatorial algorithms, graph theory, computational geometry, order theory, Ramsey theory, and combinatorial games. His work bridges foundational theory and real-world applications, particularly in algorithm design and combinatorial generation. Advising & Grants: Supervised students: Francesco Verciani, Nastaran Behrooznia, Namrata, Arturo Merino, Frieder Smolny, Karl Däubel, Jerri Nummenpalo, and Ondřej Mička Funded projects: DFG Heisenberg grant 522790373, Chancellor's International Scholarships, and EU funding Labs & Collaborations: Organizes workshops like 'Combinatorics, Algorithms and Geometry' and contributes to research networks such as the 'Combinatorial Optimization and Graph Algorithms' group. His work involves collaborations with institutions globally, including ETH Zurich, Georgia Tech, and TU Berlin.
Ely Porat is an Associate Professor at the Department of Computer Science, Bar-Ilan University, where he has been since 2000. He holds visiting professorships at the University of Michigan and Tel Aviv University, and has worked at Google (Mountain View in 2007 and Tel Aviv in 2011). His academic contributions include redefining the BSc degree in Computer Science at Bar-Ilan University and significant involvement in teaching committees. Research Interests: Algorithms and Data Structures Streaming Algorithms Pattern Matching Coding Theory Compressed Sensing His work focuses on advancing efficient algorithms for data processing, with applications in information retrieval, signal processing, and bioinformatics. He has organized multiple conferences including Stringology (2009–2011), ICALP2011GT, and UM Coding. He has served on program committees for CPM, SPIRE, and ESA. Advising & Collaboration: Current advisees include Ariel Shiftan, Guy Feigenblat, and others. Former students include Ohad Lipsky and Klim Efremenko. He hosts short-term researchers from abroad and collaborates with institutions like Google and Weizmann Institute. Publications span FOCS , STOC , ICALP , and other top venues, emphasizing theoretical foundations and practical algorithmic solutions.
Christian Bean is a Lecturer in Mathematics at Keele University, joining in 2023. Previously, he held postdoctoral positions at Reykjavik University (Iceland) from 2020 and LIPN, Université Paris Nord (France) in 2019. He earned his PhD in Computer Science from Reykjavik University in 2018 under Henning Ulfarsson. His research focuses on enumerative combinatorics and algorithmic methods for proving mathematical statements, particularly in permutation patterns and combinatorial structures. Key areas include permutation enumeration, pattern avoidance, and algorithmic frameworks for combinatorial exploration. Bean has authored/co-authored influential papers in journals like The Electronic Journal of Combinatorics , Information and Computation , and Journal of Symbolic Computation , with recent work emphasizing insertion encodings, mesh patterns, and generating functions for Motzkin paths. His Combinatorial Exploration framework has become a cornerstone in algorithmic enumeration. Supervision includes current PhD student Abigail Ollson and past MSc/BSc advisees. Active in academic collaboration, his work bridges theoretical computer science and discrete mathematics, with applications to structural analysis and automated discovery in combinatorial domains.
Andrea L. Bertozzi is a Distinguished Professor of Mathematics and Mechanical and Aerospace Engineering at the University of California, Los Angeles (UCLA). She holds the Betsy Wood Knapp Chair for Innovation and Creativity and serves as Director of Applied Mathematics. She is affiliated with the California NanoSystems Institute (CNSI) and leads a research group in applied mathematics with focuses on nonlinear partial differential equations, fluid dynamics, and machine learning. Education: Ph.D. (1991), M.A. (1988), and A.B. (1987) in Mathematics from Princeton University. Research interests include fluid dynamics, mathematical modeling, and machine learning applications. Her work spans interdisciplinary areas such as particle-laden flows, crime modeling, and environmental systems. She has published extensively in top journals and conferences, contributing to fields like computational physics, graph-based learning, and social network analysis. Her research group actively collaborates on projects involving undergraduate and graduate students, focusing on problems in applied mathematics and its intersections with engineering and social sciences. She advises students on PhD and postdoctoral research, emphasizing rigorous mathematical approaches to real-world challenges. Labs/Teams: Leads the UCLA Applied Mathematics group and collaborates with the CNSI on nanoscale systems and advanced materials.
Dr. Loïc Cellier is an Associate Professor specializing in Mathematics and Computer Science at the School of Aerospace Engineering (ELISA Aerospace, ISAE group) in Bordeaux, France. He holds a Ph.D. in Applied Mathematics from the University of Toulouse and has extensive qualifications from the French National Council of Universities. His research focuses on Optimal Control, Operations Research, and Game Theory, with applications in Air Traffic Management, Combinatorial Games, and Mathematics Education. Education includes a Ph.D. from the University of Toulouse (2015), M.Sc. in Applied Mathematics from Sorbonne University (2010), and B.Sc. in Mathematics from the University of Montpellier (2008). He has received multiple awards, including top rankings in national competitions and international recognition for his work in Hex game theory and optimization. Research interests span Optimal Control (aircraft conflict avoidance), Operations Research (combinatorial optimization), Game Theory (Hex and Chomp), and Mathematics Education. He has published extensively in journals like Optimal Control Applications and Methods, and contributed to educational platforms like POEM for personalized learning systems. Teaching responsibilities include courses in Mathematics, Computer Science, and Numerical Analysis at universities in France, Azerbaijan, and Switzerland. His pedagogical materials cover advanced topics like Probability, Optimization, and Programming in Python/C++. Notable projects include leading the ATOMIC project on air traffic optimization, co-founding the HEX&CO organization to promote combinatorial games, and developing educational resources for UNESCO’s POEM initiative.
Lixin Fu is an Associate Professor and Director of Undergraduate Studies in the Department of Computer Science at The University of North Carolina at Greensboro (UNCG). They hold a Ph.D. in Computer and Information Sciences and Engineering from the University of Florida. Their research focuses on Databases, Data Mining, Machine Learning, Social Networks, and Algorithms, with notable contributions to optimization in delivery systems, shortest path discovery in social graphs, and data cube technologies. Education: Ph.D. in Computer and Information Sciences and Engineering from University of Florida. Research interests emphasize scalable algorithms for social network analysis, graph theory applications, and efficient data mining techniques. Their work often bridges theoretical advancements with practical implementations in areas like recommendation systems, spam detection, and privacy-preserving data processing. Recent publications highlight innovations in pathfinding algorithms, clustering methodologies, and optimizing multi-source delivery systems. Teaching responsibilities include advanced courses such as CSC 671 (Advanced Database Systems), CSC 674/474 (Principles of Data Mining), and CSC 654/454 (Design and Analysis of Algorithms). Labs/Teams: While no specific lab is mentioned, their research collaborations likely involve data science and computational methods groups at UNCG.
