Dr Matthew Crossley is a Senior Lecturer and Deputy Head of the Department of Computing and Mathematics at Manchester Metropolitan University, where he has served since 2013. Since 2018, he has led the BSc (Hons) Computer Games Development program, implementing innovative teaching methods such as 'Game a Week' challenges and 'Simulated Studio' experiences to enhance students' portfolios and industry readiness. His research spans computational intelligence, game development, and public health education. Key contributions include algorithmic approaches to puzzle-solving (e.g., ant colony optimization for Nurikabe puzzles), epidemiological modeling of fictional apocalypses (Zombie, Vampire, Werewolf scenarios), and educational simulations like SimFection for vaccination awareness. His work often merges technical rigor with creative applications. Article trends reveal a focus on nature-inspired algorithms (2013-2014), game development pedagogy (2018-present), and interdisciplinary applications of mathematical modeling in public health and fictional epidemiology. He also explores player behavior analysis and game analytics in his recent work. His professional contributions emphasize bridging academic theory with practical industry experience, ensuring students develop competitive portfolios through immersive, studio-like environments.
Dr. Liang Chen is a Professor in the Department of Computer Science at the Faculty of Science and Engineering, University of Northern British Columbia. His research spans multiple areas of computer science, computational intelligence, and interdisciplinary studies. PhD in Computer Science from Institute of Software, Chinese Academy of Sciences (Academia Sinica) Key research interests include Artificial Intelligence , Machine Learning , Computer Vision , Big Data Analysis , and Voting Theory in political elections. His work also explores Neural Networks , Fuzzy Systems , and Fast Approximate Algorithms for solving NP-hard problems. Dr. Chen's scientific contributions include membership as a Senior Member of IEEE and recognition in the Who's Who in the World editions. He supervises graduate students in MSc programs in Computer Science and Interdisciplinary Studies. Languages spoken: English Office location: 10-2046, Prince George Campus Contact: liang.chen@unbc.ca | Phone: 250-960-5838
Michael Biro is an Assistant Professor in the Department of Mathematics at the University of Connecticut, affiliated with the College of Liberal Arts and Sciences. His research focuses on Geometry, Topology, and Computational Complexity, with notable contributions to algorithmic geometry and puzzle-solving complexity. He holds an office in MONT 403 and can be reached at michael.biro@uconn.edu. His work bridges theoretical computer science and discrete mathematics, addressing challenges in geometric routing, polyomino folding, and NP-hardness proofs for puzzles like Shakashaka and LITS. Recent publications explore beacon-based algorithms for routing efficiency and visibility problems in polygonal domains. Though no formal educational details are provided here, his research trends indicate a deep engagement with combinatorial geometry and algorithm design. No advising roles or scientific awards are listed in the provided materials.
Dr. Ivan Žulj is a scientific staff member at the University of Hohenheim, affiliated with the Institute of Business Administration within the Faculty of Business, Economics and Social Sciences. He works under the Chair of Operations Management led by Prof. Dr. Katja Schimmelpfeng, where he contributes to teaching and research in logistics and operations management. His responsibilities include leading exercises and seminars in Operations Management. Doctorate in Political Science, Technical University of Darmstadt M.Sc. in Management, University of Hohenheim B.Sc. in Economics, University of Hohenheim Dr. Žulj's research centers on quantitative methods for solving planning problems in logistics and healthcare, with a strong emphasis on combinatorial optimization. His work develops efficient heuristic, metaheuristic, exact, and hybrid algorithms to improve warehouse operations, particularly in order picking systems. Applications extend to both corporate logistics and healthcare logistics, aiming to enhance efficiency and safety through algorithmic solutions. The recent publications show a consistent trend in optimizing picker-to-parts systems using advanced algorithmic approaches. His work integrates robotics (AMRs), infection risk reduction, batch sequencing, and storage assignment, demonstrating interdisciplinary innovation in warehouse logistics. The use of hybrid metaheuristics like adaptive large neighborhood search and tabu search highlights his technical depth in solving NP-hard problems. Dr. Žulj has not been publicly recognized with any scientific awards based on the available information. He is actively involved in academic supervision, having contributed to final theses at the bachelor’s and master’s levels, though specific student names are not listed. There is no mention of external research grants, but his involvement in DFG projects and completed research initiatives suggests potential grant participation. His role includes developing optimization and simulation software for practical application in logistics planning. His research is conducted within the research focus of the Chair of Operations Management, which includes DFG-funded projects and completed studies on combinatorial optimization in logistics. The team includes research assistants and doctoral candidates working on related topics in operations management and supply chain optimization.
Avi Wigderson is the Herbert H. Maass Professor at the School of Mathematics, Institute for Advanced Study (IAS), Princeton. He leads the Computer Science and Discrete Mathematics (CSDM) activities at IAS, focusing on foundational questions in computational complexity, randomness, and their applications. His research bridges theoretical computer science, mathematics, and physics, addressing challenges such as P vs NP, quantum computing, and cryptographic security. Education: Ph.D. in Mathematics from Princeton University (1983), with prior roles including professorships at The Hebrew University of Jerusalem and visiting positions at Princeton University and UC Berkeley. Research interests span computational complexity theory, randomness in computation, cryptography, quantum computation, and pseudorandomness. He has pioneered work on derandomization, probabilistic proof systems, and the interplay between algebraic structures and computational efficiency. Awards include the 2023 ACM Turing Award, 2021 Abel Prize, Rolf Nevanlinna Prize (1994), and Gödel Prize (2009). His contributions to theoretical computer science and mathematics have profoundly impacted areas like algorithm design, cryptography, and computational geometry. Key contributions include work on pseudorandomness, proof complexity, and the role of randomness in computation. He has also explored applications of computational methods to mathematics, such as algebraic geometry and number theory.
Vicente Campos Aucejo is a Titular de Universidad (Associate Professor) in the Department of Statistics and Operations Research at the Universitat de València . He holds a Ph.D. in Mathematics, awarded in 1982 for his thesis on optimal Eulerian circuits and the mixed postman problem, supervised by Dr. Marco A. López Cerdá. His academic career is deeply rooted in combinatorial optimization and operations research. His primary research interests lie in arc routing problems , vehicle routing , matrix bandwidth minimization , and permutation problems . He is a leading expert in metaheuristic methods, particularly tabu search , GRASP (Greedy Randomized Adaptive Search Procedure), and scatter search , applying these techniques to solve complex NP-hard optimization problems. His work bridges theoretical developments with practical algorithmic implementations. An analysis of his 15 most recent publications reveals a consistent focus on combinatorial optimization using metaheuristics . His research spans applications in graph theory (e.g., hull number, dispersion problems), algorithm design (e.g., branch and bound, adaptive memory programming), and comparative studies of optimization frameworks (e.g., GRASP vs. genetic algorithms). The dominant keywords across his work are Operations Research, Computer Science, and Combinatorics, with a clear emphasis on developing and refining heuristic and exact solution methods. Collaboration is a hallmark of his research. He has co-authored extensively with prominent scholars such as Rafael Martí , Manuel Laguna , Angel Corberán , and Fred Glover , indicating strong international ties and integration within the optimization research community. His work is regularly published in high-impact journals like the European Journal of Operational Research , Computers & Operations Research , and Discrete Applied Mathematics . While specific details about student advising, grants, or laboratory affiliations are not available in the provided text, his long-standing publication record and senior academic title suggest a significant role in mentoring and research leadership. There is no indication of any scientific awards in the provided information.
Michal Walicki is an Associate Professor in the Department of Informatics at the University of Bergen, Norway. His research lies at the intersection of mathematical logic, theoretical computer science, and philosophy of logic. He specializes in graph-theoretic methods for analyzing logical paradoxes, paraconsistency, and the algebraic semantics of nondeterminism. His primary research interests include self-reference, semantic paradoxes, digraph kernels, paraconsistent logic, multialgebras, and bounded reasoning in epistemic logic. He has developed graph-based semantics for logical theories, where paradoxes correspond to graph cycles and consistent theories relate to digraph kernels. His recent work focuses on the Poison Game for infinite digraphs and a Logic of Sentential Operators (LSO) that handles unrestricted sentential quantification without explosion. His publications span journals such as the Journal of Philosophical Logic, Journal of Symbolic Logic, Synthese, and Discrete Mathematics. He has authored the textbook Introduction to Mathematical Logic and maintains comprehensive lecture notes for courses like INF-227. His work shows a strong trend in using combinatorial and algebraic structures to model logical and computational phenomena, especially in handling inconsistency and self-reference. Strongly complete axiomatizations of finite syntactic epistemic states Complete calculus for multialgebraic semantics of nondeterminism Graph Normal Form for first-order logic Paraconsistent resolution with local kernels Applications of digraph kernels to SAT solving Michal Walicki has supervised several PhD students, including Sjur Dyrkolbotn, Thomas Ågotnes, and Yngve Lamo. He has also received research funding for projects in logic and algebra, though specific grants are not detailed. He leads a research group focused on logical foundations of computation and collaborates with colleagues in logic, computer science, and philosophy. His work continues to explore the deep connections between logic, graphs, and algebraic structures.
Jared Saia is a Professor in the Department of Computer Science at the University of New Mexico, within the College of Engineering. His research spans theoretical computer science, with a focus on distributed algorithms, security, game theory, and spectral methods. He has taught numerous advanced courses in algorithms, data structures, blockchains, and game theory. University: University of New Mexico School: College of Engineering Department: Department of Computer Science Academic Rank: Professor Email: saia@cs.unm.edu Education: PhD in Computer Science, University of Washington, 2002 BS in Computer Science, Stanford University, 1993 His research interests lie at the intersection of theory and practical systems, particularly in enabling large-scale groups to function effectively without centralized control. He has made significant contributions to distributed consensus, blockchain technologies, and algorithmic game theory. The recent publications and course topics reflect a strong trend toward interdisciplinary work combining algorithms with economics, security, and machine learning. His work often employs probabilistic and geometric methods to solve complex distributed computing problems. Scientific Awards: NSF CAREER Award School of Engineering Junior Faculty Research Excellence Award School of Engineering Senior Faculty Research Excellence Award Several best paper awards Prof. Saia has been actively involved in mentoring through graduate courses and research supervision. He has led projects on secure distributed systems, blockchain applications, and algorithmic resilience. His teaching includes core graduate courses such as CS 561 (Algorithms and Data Structures), CS 506 (Advanced Geometric and Probabilistic Methods), and specialized seminars on Bitcoin and game theory. Labs and Research Groups: While not explicitly named, his course websites and research themes suggest leadership in a theoretical computer science and distributed systems research group at UNM, focusing on algorithm design for secure and decentralized environments.
Leonid Gurvits is a Professor of Computer Science at The City College of New York, part of the City University of New York (CUNY) system. His career spans institutions like Los Alamos National Laboratory (lanl.gov) and collaborations with leading researchers in mathematics and computer science, including Ankit Garg and Rafael Oliveira. He has held visiting scientist roles at programs such as the Simons Institute's Geometry of Polynomials (Spring 2019) and Bridging Continuous and Discrete Optimization (Fall 2017). Fields of Interest: Quantum computing, hyperbolic polynomials, operator scaling, and computational complexity. Key Contributions: Pioneering deterministic polynomial-time algorithms, solving problems in mixed discriminants, and advancing understanding of quantum entanglement and matrix stability. His work is deeply rooted in the interplay between mathematics and theoretical computer science, with applications to optimization, control theory, and quantum information. Notably, he resolved conjectures related to the permanent of matrices and developed capacity-preserving operators for combinatorial applications. Publications include groundbreaking articles on the complexity of quantum entanglement (2003), stability of switched linear systems (2007), and operator scaling for Brascamp-Lieb inequalities (2017). These works have been cited extensively, reflecting their impact on theoretical computer science and mathematical optimization.
Francesco Zammori is an Associate Professor at the Department of Systems Engineering and Industrial Technologies (DISTI) , University of Parma. His career spans academic research, teaching, and industry collaboration, focusing on lean manufacturing, supply chain innovation, and simulation modeling. Scientific High School Diploma, 100/100, Leonardo da Vinci Scientific High School (2000) Bachelor's in Management Engineering (logistics and production), 110/110 cum laude , University of Pisa (2002) Master's in Management Engineering (logistics and production), 110/110 cum laude , University of Pisa (2004) PhD in Mechanical Engineering (plants and technologies), University of Pisa (2009) Zammori's research centers on lean manufacturing , where he develops techniques for identifying inefficiencies using Value Stream Mapping (VSM) and Overall Equipment Effectiveness (OEE). He specializes in Vendor Managed Inventory (VMI) modeling, discrete-event simulation of complex systems with multivariate statistical analysis, and solving NP-hard problems in operations management using mathematical programming and heuristics. His work also explores process mapping with IDEF/BPMN methods and IT system prototyping for transactional data. Recent publications highlight trends in supply chain circularity (2024), automated storage systems (2023), and educational gamification (2023). He applies Monte Carlo simulation , Markov chains , and life cycle analysis to industrial challenges. Scientific Contributions: Editor-in-Chief (European area), Decision Science Letters (Growing Science) Organizer & Chair, IESM2015 special session on 'Lean in Make To Order Firms' Member, AIIE2015 international conference scientific committee Zammori has taught 40+ corporate training modules for Tuscan companies and delivered university courses on ERP systems , business process management , and management information systems across multiple Italian universities since 2005. His funded projects include RFID tracking systems for paper coils and plasma bags, extended enterprise logistics frameworks, and lean process reengineering for manufacturing firms.
Santiago Porras Alfonso is a Professor in the Department of Applied Economics at the Faculty of Economics and Business Sciences, University of Burgos. His research centers on quantitative methods for business and economics with specialization in metaheuristic optimization and machine learning applications across diverse sectors including energy systems, tourism analytics, and educational research. He earned his PhD from the University of Burgos in 2016 with a dissertation on hybrid solution methods (tabu search-VNS) for orienteering and planning problems, supervised by Dr. Joaquín A. Pacheco Bonrostro and Dr. Bruno Baruque Zanón. His academic foundation combines theoretical optimization techniques with practical business applications. Porras Alfonso's research program demonstrates exceptional breadth within quantitative business analysis: Development and application of metaheuristics for complex optimization problems Machine learning integration in economic forecasting and consumer behavior analysis Time-series modeling for energy systems (wind turbines, geothermal) and educational outcomes Clustering methodologies for transportation trajectory analysis and student profiling Text mining applications in tourism reputation management and wine consumer reviews His 2022-2025 publications reveal a strategic pivot toward interdisciplinary AI applications, particularly in energy management (LSTM networks for wind forecasting), educational analytics (gamification for statistics education, comparative learning modalities), and pandemic-impacted sectors (funeral demand forecasting). Methodologically, he consistently bridges metaheuristics with deep learning techniques to solve real-world business problems. No scientific awards were documented in the available information. While specific grant details and student supervision records are absent from current sources, his active research group membership in GRINUBUMET indicates ongoing collaborative projects. His work shows strong industry engagement through applications in funeral sector management, tourism analytics, and energy systems. He actively contributes to the GRINUBUMET research group specializing in metaheuristic development and application, focusing on solving NP-hard problems in logistics, energy optimization, and business analytics through hybrid algorithmic approaches.
Konstantinos Draziotis is an Associate Professor at the Department of Informatics, School of Informatics, Aristotle University of Thessaloniki. His academic career at the university began as a Lecturer from 2013 to 2019, and he has been serving as an Associate Professor since 2019. His educational background includes a PhD in Mathematics from Aristotle University of Thessaloniki (2005) and a Bachelor's Degree in Mathematics from the same institution (1998). Draziotis specializes in Computational Number Theory and Cryptography , with a particular focus on Diophantine equations, elliptic curves, and cryptanalysis of digital signature schemes. His research bridges theoretical mathematics with practical cryptographic applications, especially in analyzing the security of cryptographic protocols and developing mathematical methods for solving complex number-theoretic problems. He has developed a Greek-language textbook on Introduction to Cryptography with Creative Commons licensing, demonstrating his commitment to accessible education in this field. His recent publications demonstrate a strong trend toward cryptanalysis of digital signature schemes (particularly DSA and ECDSA), lattice-based attacks , and number-theoretic approaches to cryptographic problems . His work spans both theoretical aspects of number theory (Diophantine equations, integer points on curves) and practical applications in cryptography (cryptosystem attacks, knapsack problems). The interdisciplinary nature of his research connects deep mathematical theory with real-world security applications, with publications consistently appearing in reputable journals like Information and Computation, Advances in Mathematics of Communications, and Theoretical Computer Science. Draziotis leads a crypto research group at the Aristotle University of Thessaloniki and actively contributes to open-source cryptographic and mathematical software ecosystems. His academic service includes participation in departmental administrative functions as indicated by various announcements on examination schedules, graduation ceremonies, and departmental operations visible on his website. He maintains collaborations with researchers including Dimitrios Poulakis, Marios Adamoudis, and Anastasia Papadopoulou, among others, reflecting an active research network in both Greek and international academic communities.
Prof. Dr. Andreas Wiese is an Associate Professor at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Mathematics. He previously held academic positions at the Vrije Universiteit Amsterdam (2021-2022), Universidad de Chile (2016-2021), and the Max-Planck-Institut für Informatik (2012-2016). Andreas Wiese earned his PhD in Mathematics from TU Berlin (2008-2011) and studied Mathematics and Computer Science at TU Berlin (2002-2008). His research focuses on combinatorial optimization , approximation algorithms , and geometric problem-solving , particularly for NP-hard problems in packing, scheduling, and network flow. His recent work includes A 2024 SODA publication on outlier-aware minimum sum of radii approximation A 2023 STOC paper improving weighted flow time minimization Foundational contributions to unsplittable flow and geometric knapsack problems with consistent appearances at top-tier conferences like STOC, FOCS, and SODA. Andreas Wiese leads the Discrete Optimization research group at TUM, supervising PhD students Alexander Armbruster, Elisa Dell'Arriva, and Sandy Heydrich. He has organized major conferences including LATIN 2024 and ADFOCS 2015 , and served on program committees for STOC, FOCS, and SODA.
Aleksandar Kartelj is an Associate professor at the Department for Computer Science, Faculty of Mathematics, University of Belgrade. He has been working at the Faculty of Mathematics since 2008, starting as a Teaching assistant, then becoming Assistant professor (2015-2023), and currently Associate professor (since October 2023). He also serves as a Guest professor at the Faculty of Natural Science and Mathematics, University of Banjaluka since 2018. Education: B.Sc. in Computer Science from Faculty of Mathematics, University of Belgrade (2005-2008, GPA 9.94/10.00) M.Sc. in Computer Science from Faculty of Mathematics, University of Belgrade (2008-2010, GPA 9.92/10.00) PhD in Computer Science from Faculty of Mathematics, University of Belgrade (2010-2014, GPA 10.00/10.00) M.Sc. in Quantitative Finance from Faculty of Economics, University of Belgrade (2010-present) Aleksandar Kartelj's research primarily focuses on optimization and data mining. He is a member of the "Modelling and optimization group" at the Faculty of Mathematics. His work involves designing exact and non-exact optimization techniques such as integer (linear) programming models, branch & bound, beam search, variable neighborhood search, and genetic algorithms. These techniques are applied to solve NP-hard problems in computational biology, transportation, logistics, social networks, and for improving machine learning algorithms. His recent publications (2021-2025) span diverse areas including symbolic regression, constrained longest common subsequence problems, graph protection algorithms, Roman domination in graphs, and applications in computational biology and epidemiology. His work frequently appears in high-impact journals such as Journal of Big Data, Expert Systems with Applications, and Knowledge-Based Systems, with many publications classified as M21a (Q1). Scientific Awards: Best paper award at the 2023 Second Serbian International Conference on Applied Artificial Intelligence (SICAAI) for "Integrating Top-level Constraints into a Symbolic Regression Search Algorithm" Aleksandar Kartelj has been involved in several research projects, including being a Research member on the project "Mathematical Models and Optimization Methods for Large-Scale Systems" (2011-2020) and a Research leader on the project "Combinatorial optimization for cancer progression inference and comparison" (2019-present). He has also developed numerous software applications for various organizations including the UN FAO, International Monetary Fund, and several private companies. He has been actively involved in teaching, serving as a Computer science teacher at Computer Gymnasium and Mathematical Grammar School in Belgrade, in addition to his university teaching responsibilities.
Dr. Florian Rösel is a researcher affiliated with the Department of Data Science at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He works under the Professorship of Optimization under Uncertainty & Data Analysis led by Prof. Dr. Frauke Liers, focusing on data-driven optimization techniques and their applications in aviation logistics and complex system modeling.
