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José NetoView profile

Professor

• Combinatorial Optimization
• Mathematical Programming
• Operations Research
• +3 more

Prof. José Neto is a Professor at Télécom SudParis, part of the Institut Polytechnique de Paris, affiliated with the SAMOVAR research department. His work focuses on combinatorial optimization, mathematical programming, and graph theory. He has contributed to polyhedral studies of cut and assignment polytopes, spectral bounds for graph partitioning, and optimization algorithms for mixed-variable and blackbox problems. His research spans topics like network pricing complexity, domination in graphs, and robustness verification in neural networks. He has published extensively in top journals such as Mathematical Programming, Discrete Applied Mathematics, and Networks. Key contributions include developing efficient algorithms for combinatorial pricing, analyzing optimization relaxations, and exploring structural properties of discrete mathematical objects. His work bridges theoretical foundations and practical applications in operations research, with recent interests in binarized neural network verification and mixed-variable optimization. He has collaborated on projects related to cloud virtual machine mapping and combinatorial pricing models. Labs/Teams: Active member of the SAMOVAR laboratory, specializing in applied mathematics and optimization research.

Florian PommereningView profile

Professor

• Classical Planning
• Heuristic Search
• Linear Programming
• +3 more

Dr. Florian Pommerening is a Professor at the Department of Mathematics and Computer Science within the Faculty of Science at the University of Basel, Switzerland. He holds an email address of florian.pommerening@unibas.ch and can be reached via +41 61 207 05 37. His academic career is deeply rooted in classical domain-independent planning, with a focus on optimal planning through heuristic search and the application of linear and mixed integer programming in planning domains. University of Basel, Switzerland Faculty of Science Department of Mathematics and Computer Science Professor Rank Florian completed his PhD at the University of Basel in 2017 under the supervision of Malte Helmert. He also holds a Master's degree in Computer Science from the University of Freiburg (2012) and a Master's in Information Technology from Monash University (2010). His research spans topics such as classical planning, heuristic search, linear programming, mixed integer programming, and automated planning systems. His publication record from 2012 to 2025 demonstrates sustained contributions to planning algorithms, including cost partitioning heuristics, Lagrangian decomposition, and transition landmark generation. His work has been recognized with multiple awards, including the ICAPS 2024 Influential Paper Award, ECAI 2023 Quality Champion PC award, and the Dux of the Master of Information Technology at Monash University. ICAPS 2024 Influential Paper Award Quality Champion PC award (ECAI 2023) Runner Up, Best Student Paper Award (ICAPS 2023) CoRe Challenge 2022 Awards Best Student Paper Runner-Up (ICAPS 2022) Best System Demonstration (ICAPS 2022) Outstanding Senior Program Committee Award (AAAI 2021) ICAPS 2019 Best Paper Award Faculty Award 2018 (University of Basel) EurAI Artificial Intelligence Dissertation Award 2017 ICAPS 2018 Best Dissertation Award Outstanding PC Member Award (SoCS 2016) Winner, Unsolvability IPC 2016 AAAI 2015 Outstanding Paper Award ICAPS 2014 Outstanding Paper Award Dux of the Master of Information Technology 2010 (Monash University) Florian actively contributes to the planning community through tutorials at AAAI 2019 and ICAPS 2015, and through software development such as the Fast Downward planner and Planutils framework. His notable side project includes creating a quine in the Shakespeare Programming Language (SPL) with Thomas Mayer, showcasing technical creativity beyond core research.

Diane DonovanView profile

Professor

• Combinatorics
• Design theory
• Latin squares
• +6 more

Diane Donovan is an Affiliate Professor in the School of Mathematics, Faculty of Science at the University of Queensland. Her research spans combinatorics, design theory, and their interdisciplinary applications to cryptography, coding theory, and biological systems. Her work demonstrates strong theoretical foundations while increasingly incorporating real-world applications across diverse scientific domains. Professor Donovan's research interests focus on combinatorial structures, particularly Latin squares, orthogonal arrays, and design theory. Her work explores critical sets in Latin squares, biembeddings of combinatorial structures, and applications to coding theory and cryptography. She has made significant contributions to the understanding of partial Latin squares, Heffter arrays, and their connections to graph theory and finite geometry. Her recent work shows an expanding focus on interdisciplinary applications, particularly in biological systems and energy modeling. Analysis of her recent publications reveals a trajectory from purely theoretical combinatorial mathematics toward increasingly interdisciplinary applications. While maintaining strong theoretical contributions to design theory and combinatorics, her work now frequently intersects with biological modeling, medical research, and energy systems. This evolution demonstrates how fundamental mathematical structures can provide powerful tools for addressing complex problems in diverse scientific fields, from cancer modeling to gene expression analysis and petroleum engineering. Professor Donovan maintains an active research profile with numerous collaborations across disciplines, particularly with researchers in mathematics, biology, and engineering. Her publication record shows consistent productivity with significant contributions to top journals in combinatorics and discrete mathematics. Her work demonstrates the power of combinatorial methods to address both theoretical questions and practical applications across scientific domains.

Marko ĐukanovićView profile

Assistant Professor

• Combinatorial Optimization
• Graph Theory
• Bioinformatics
• +4 more

Marko Đukanović serves as an Assistant Professor at the Department of Computer and Information Sciences within the Faculty of Natural Sciences and Mathematics at the University of Banja Luka. His academic career spans combinatorial optimization, graph theory, and bioinformatics applications, with significant contributions to algorithm design for complex computational problems. His research focuses on combinatorial optimization , particularly in graph domination problems and longest common subsequence variants . His work bridges theoretical computer science with practical applications in bioinformatics, social network analysis, and computational biology. Recent publications demonstrate expertise in developing advanced algorithms including biased random-key genetic algorithms, variable neighborhood search, and neural network-guided optimization techniques. Key publication trends show his specialization in solving constrained optimization problems on graphs, with growing emphasis on integrating machine learning components into traditional combinatorial algorithms. His 2024 publications in IEEE Transactions on Evolutionary Computation and Applied Mathematics and Computation represent significant contributions to the field. Best Paper Award at EvoStar Conference 2024 for 'A Neural Network Based Guidance for a BRKGA: An Application to the Longest Common Square Subsequence Problem' Dr. Đukanović actively participates in international academic collaboration, including recent panel discussions on knowledge transfer between Bosnia and Herzegovina and global expert communities. His work demonstrates strong international collaboration, particularly with researchers from TU Wien and institutions across Europe.

Domagoj ŠeverdijaView profile

Assistant Professor

• Combinatorial Optimization
• Computational Linguistics
• Natural Language Processing
• +4 more

Domagoj Ševerdija is an Assistant Professor at the School of Applied Mathematics and Informatics at Josip Juraj Strossmayer University of Osijek, where he leads the Computer Science and Machine Learning Research Group. He holds a PhD in Electrical Engineering (2013) and a dual BS in Mathematics and Computer Science (2007) from the University of Osijek. His research spans computational linguistics, natural language processing, machine learning, and combinatorial optimization, with applications in bioinformatics, robotics, and energy systems. Recent work focuses on neural language models, domain adaptation techniques, and efficient algorithm design. Publications show strong interdisciplinary trends: computational linguistics (Croatian morphology, sentence embeddings), bioinformatics (cell typing, RNA splicing), and algorithm optimization (terrain guarding, matrix operations). Recent papers emphasize knowledge distillation, domain adaptation, and compressed representations. Awards: Best paper award in AIS - Artificial Intelligence Systems track (MIPRO 2023) Research funding includes: Computer-Assisted Corpus Linguistics (UNIOS, 2019-2020) Croatian Identity Network Framework (Adris Foundation, 2020-2021) Croatian Language in Global Cloud (Adris Foundation, 2019-2020) Leads the Computer Science and Machine Learning Research Group, coordinating projects in NLP, computational geometry, and AI applications.

Martin KouteckýView profile

Associate Professor

• Parameterized Complexity
• Integer Programming
• Combinatorial Optimization
• +1 more

Martin Koutecký is an Associate Professor at the Institute of Informatics, Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic. His research primarily focuses on parameterized complexity of integer programming, combinatorial optimization, and computational social choice. He completed his PhD under Petr Kolman at Charles University and conducted postdoctoral research at Technion (Haifa, Israel) under Asaf Levin and Shmuel Onn. Research Interests: Koutecký's work bridges theoretical computer science and optimization, with emphasis on: Designing efficient algorithms for integer programming under structural constraints Applying optimization techniques to computational social choice problems Developing parameterized approaches for combinatorial optimization Exploring geometric perspectives in election modeling and bribery problems Publication Focus: His recent articles (2020-2025) demonstrate consistent work in algorithm design for optimization problems, particularly in integer programming variants and computational social choice. Key themes include block-structured IP, parameterized complexity, election modeling, and convex optimization. Methodological innovations frequently involve polyhedral theory, approximation algorithms, and complexity analysis.

• Algebraic Number Theory
• Analytic Number Theory
• Cryptanalysis
• +8 more

Carlo Sanna is an Associate Professor at the Department of Mathematical Sciences (DISMA) of Politecnico di Torino, Italy. His research focuses on algebraic and analytic number theory, cryptography, and their intersections, particularly in post-quantum and public-key cryptographic systems. Research Interests : Algebraic Number Theory, Analytic Number Theory, Cryptanalysis, Post-Quantum Cryptography, Public-Key Cryptography, Discrete Mathematics. Teaching : He teaches Linear Recurrences (PhD level) and Advanced Cryptography (Master's level) at Politecnico di Torino, while also lecturing Linear Algebra and Geometry for Aerospace and Management Engineering. Publications : His recent work spans Fibonacci partitions, Lucas sequences, post-quantum cryptographic algorithms, and properties of linear recurrences. Supervised PhD Students : Leonardo Errati (Post-Quantum Cryptography, 2025) Giulia Salvatori (2024) Federico Accossato and Enrico Guglielmino (2022) Giuliano Romeo (2021-2025, p-adic continued fractions) Research Projects : Algebraic Methods in Cryptanalysis (2025-2027, Scientific Director) QUBIP (Quantum-oriented browser updates, 2023-2026, Scientific Head) SERICS Spoke 5 (Cybersecurity, 2023-2025, Scientific Manager) Email : carlo.sanna@polito.it

• Number Theory
• Partition Theory
• Ramanujan's Mathematics
• +3 more

Dr. Mike Hirschhorn is an Honorary Associate Professor in the School of Mathematics & Statistics at the University of New South Wales (UNSW) Sydney. His academic career spans several decades with continuous research contributions in number theory and combinatorics. Professor Hirschhorn's research primarily focuses on Number Theory , particularly Partition Theory and Ramanujan's Mathematics . His work extensively explores q-series, modular forms, and combinatorial identities. He has made significant contributions to understanding partition congruences, colored partitions, and various specialized partition functions. His research often involves deep connections between combinatorial structures and modular arithmetic properties. The analysis of his recent publications reveals a consistent focus on partition congruences modulo various integers (particularly powers of 5), colored partitions, and q-series identities. His work demonstrates remarkable technical skill in manipulating generating functions and modular forms to derive partition-theoretic results. Many of his papers build upon Ramanujan's legacy, extending classical results in novel directions while maintaining rigorous mathematical standards. Professor Hirschhorn has maintained an exceptionally productive research career, with publications spanning from the 1970s through 2022. His work appears in prestigious journals including the Ramanujan Journal, Journal of Number Theory, and Discrete Mathematics.

• Optimisation
• Operational Research
• Applied Probability
• +4 more

Jamie Fairbrother is a Lecturer in Operational Research (Optimisation) at the Data Science Institute , Lancaster University , within the Department of Management Science . His research integrates optimisation, applied probability, and statistics to solve real-world problems in logistics and telecommunications, with a particular focus on stochastic programming and scenario generation. Education: Jamie Fairbrother completed his PhD research in scenario generation for stochastic programming, which laid the foundation for his subsequent work in optimisation under uncertainty. Research Interests: His expertise spans several domains: Optimisation & Operational Research: Development of mathematical models and algorithms for complex decision-making problems. Applied Probability & Statistics: Use of probabilistic methods to model uncertainty in planning and operations. Logistics & Telecommunications: Practical applications in mail centre staffing, airport slot scheduling, and wireless communication networks. Stochastic Programming: Scenario generation and robust optimisation techniques for strategic planning under uncertainty. Research Funding & Projects: Dr Fairbrother has led or contributed to multiple collaborative projects, including: Coordination of Strategic and Tactical Interventions for Reducing Air Traffic Delays (2023–2024) – a case study at Heathrow Airport. STOR-i: Optimising In-Store Price Reductions (2022–2025) – with PhD student Katie Howgate. Resource Allocation under Uncertain Demand in Royal Mail Mail Centres (2020–2023). Industrial Mathematics KTP with BT Research (2013) – modelling TV whitespace interference. PhD Supervision: He currently supervises Katie Howgate , a PhD student in Bayesian and Computational Statistics within the STOR-i Centre for Doctoral Training . Professional Affiliations: Dr Fairbrother is affiliated with the STOR-i Centre for Doctoral Training , the Centre for Transport & Logistics (CENTRAL) , and the OR-MASTER project on airport resource allocation.

Anna De Mier VinueView profile

Associate Professor

• Combinatorics
• Graph Theory
• Matroid Theory
• +3 more

Anna De Mier Vinue is an Associate Professor at the Department of Mathematics, Universitat Politècnica de Catalunya (UPC), affiliated with the Facultat d'Informàtica de Barcelona (FIB). She leads research in combinatorics, graph theory, and matroid theory as part of the UPC's GAPCOMB research group (Geometric, Algebraic and Probabilistic Combinatorics). Her work focuses on structural combinatorics, polynomial invariants, and discrete geometry. Education: Holds a PhD in Applied Mathematics from UPC. Research Interests: Combinatorial enumeration, graph polynomials (Tutte, U-polynomials), matroid representations, non-crossing structures, and algorithmic combinatorics. Her contributions bridge algebraic methods with geometric and probabilistic approaches. Publications: Over 77 works including articles in Discrete Mathematics, European Journal of Combinatorics, and SIAM Journal on Discrete Mathematics. Recent research explores marked graphs, Tutte polynomial evaluations, and lattice approximations in clutter theory. Active in international projects like COCOA and R+D+I initiatives. Grants & Collaborations: Co-leads projects on extremal combinatorics, geometric algorithms, and mathematical education. Collaborates with researchers from BarcelonaTech and international institutions. Involved in initiatives addressing gender equality in STEM education through statistical analysis. Labs/Teams: Core member of the UPC's GAPCOMB group, contributing to interdisciplinary projects at the Institut de Matemàtiques de la UPC-BarcelonaTech. Supervises research in combinatorial geometry and algebraic structures.

• Operational Research
• Optimization Methods
• Logistics and Supply Chain Management
• +7 more

Manuel Iori is a Full Professor at the Department of Engineering Sciences and Methods, University of Modena and Reggio Emilia (UNIMORE), Italy. His primary research focuses on operational research, optimization methods, and logistics systems. He specializes in vehicle routing problems, scheduling algorithms, and decision support systems with applications in industrial automation, healthcare, and service industries. Iori is actively involved in teaching advanced optimization courses for engineering students, emphasizing practical applications in data-driven decision-making and simulation. Research Interests: His work addresses complex optimization challenges such as multi-trip vehicle routing with time windows, scheduling under resource constraints, and tool switching in manufacturing systems. He integrates machine learning and metaheuristics to develop innovative solutions for logistics, production planning, and healthcare operations. Collaborations with industry partners (e.g., pharmaceutical distributors, printing companies) ensure practical relevance of his research. Teaching: Iori teaches courses like Optimization Methods for Data-Driven Engineering Processes , Models for Logistics and Production Optimization , and Methods and Algorithms for Optimization in Digital Industries . These courses combine theoretical foundations with hands-on labs using tools like Python, Xpress, and Anylogic. Key Contributions: He developed decision support systems for multi-trip routing in pharmaceutical distribution and supplier selection in facility management. His research on satellite scheduling and attended home delivery systems advances both theoretical and applied domains. As a member of CIRRELT (Canada), he collaborates on logistics optimization projects. Professional Activities: Iori’s work is reflected in over 80 peer-reviewed publications and contributions to conferences. He advises graduate students on optimization challenges and serves as a reviewer for top journals in operations research.

Brian HopkinsView profile

Professor

• Combinatorics
• Number Theory
• Discrete Mathematics
• +3 more

Brian Hopkins is a Professor of Mathematics at Saint Peter’s University in Jersey City, New Jersey. He holds a Ph.D. from the University of Washington and undergraduate degrees (B.S. and B.A.) from the University of Texas at Austin. His research focuses on combinatorics, number theory, and discrete mathematics, with notable contributions to partition theory, integer compositions, and combinatorial game theory. He is a 2015 recipient of the prestigious Haimo Award for Distinguished College or University Teaching from the Mathematical Association of America. Dr. Hopkins has authored over 20 research publications, edited three books, and presented globally. He served as editor of The College Mathematics Journal from 2014–2018 and contributed editorial work to multiple volumes on combinatorics and discrete mathematics education. His teaching philosophy emphasizes active learning strategies, such as group work and project-based learning, reflected in courses like Discrete Mathematics (MA-246) and Modern Algebra (MA-441). He has also engaged in outreach through book reviews on combinatorics and mathematical history, including works on Martin Gardner’s legacy and Euler’s combinatorial contributions. His research spans topics like Bulgarian solitaire variants, Garden of Eden states in combinatorial models, and enumeration modulo congruences. Beyond academia, he enjoys NYC cultural experiences with his husband and advocates for accessible mathematics education through pedagogical innovations documented in publications like Resources for Teaching Discrete Mathematics .

Renata SotirovView profile

Professor

• Optimization
• Operations Research
• Mathematical Programming
• +3 more

Renata Sotirov is a Full Professor at Tilburg University's Department of Econometrics & Operations Research within the Tilburg School of Economics and Management. She holds a PhD in Mathematics and has held academic positions including Associate Professor (2009–2014) and Assistant Professor (2007–2009). Her research focuses on optimization, operations research, and semidefinite programming with applications in combinatorial optimization and discrete mathematics. She has supervised PhD students such as Dr. Ning Ma and Dr. Uwe Truetsch and collaborates internationally, including with institutions like the University of Melbourne and McMaster University. Her work includes pioneering contributions to semidefinite relaxation techniques, integer SDPs, and algorithmic optimization. Recent projects involve the Chvátal-Gomory procedure for integer SDPs and complex cut polytopes. She has served as a research coordinator, director of graduate studies, and treasurer for her department. Her expertise spans teaching operations research methods, combinatorial optimization, and quantitative business analytics.

Niraj KhareView profile

Academic

• Graph Theory
• Hypergraphs
• Combinatorics
• +4 more

Niraj Khare is an Associate Teaching Professor in the Department of Mathematics at Carnegie Mellon University Qatar, part of the Mellon College of Science. He holds a Ph.D. from Ohio State University. His research focuses on graph theory, hypergraphs, combinatorics, and permutation statistics, with emphasis on matching theory, extremal graph properties, and combinatorial enumeration. His work includes studies on Erdős–Gallai edge bounds, Gončarov polynomials, and structural analysis of hypergraphs. Khare's academic contributions span theoretical and applied combinatorics, with notable publications on permutation statistics, hypergraph size analysis, and edge partitioning in graphs. His teaching role emphasizes mathematics education at the university level. He can be contacted at nkhare@cmu.edu and is located in Office 2194 at CMU Qatar's Education City campus.

Bjorn BergView profile

Associate Professor

• Healthcare Operations Management
• Health Policy
• Robust Optimization
• +3 more

Bjorn Berg is an Associate Professor in the Department of Health Policy and Management at the University of Minnesota. His work focuses on healthcare operations management, with particular emphasis on optimizing clinical scheduling systems and care team structures. Current research projects include NSF-funded work on family medicine residency scheduling Collaborations span operations research, clinical workflow optimization, and health policy analysis His methodological expertise includes robust optimization, discrete event simulation, and stochastic programming models applied to healthcare delivery challenges. Recent publications analyze surgical scheduling algorithms, patient panel designs, and clinic operational efficiency. Key research themes include: Improving physician continuity through structured clinic scheduling Developing mathematical models for healthcare resource allocation Evaluating trade-offs in specialty care team configurations Designing incentive mechanisms for patient attendance compliance