Rafael Martí Cunquero is a Full Professor ( Catedrático de Universidad ) in the Department of Statistics and Operations Research at the Faculty of Mathematics, University of Valencia, Spain. He leads the OPTIMATH Optimization and Mathematical Modelling research group and maintains an active research profile with a focus on optimization and heuristics. Research Interests: His primary fields of interest include Operations Research, Combinatorial Optimization, Metaheuristics (especially Scatter Search, GRASP, and Tabu Search), Global Optimization, Graph Drawing, and Network Design. His work bridges theoretical development and practical application in complex decision-making problems. The analysis of his recent publications reveals a consistent focus on developing and applying advanced metaheuristic methods—particularly hybridizations of GRASP, path relinking, scatter search, and variable neighborhood search—to challenging combinatorial optimization problems. Key application areas include graph drawing (e.g., edge crossing minimization), location problems (e.g., p-median, hub location), diversity and dispersion maximization, and routing. His work often emphasizes both algorithmic innovation and empirical validation. Dr. Martí has made substantial contributions to the field, co-authoring foundational texts on scatter search and editing comprehensive handbooks on heuristics. His research is supported by extensive collaborations, particularly with Manuel Laguna, Fred Glover, and Abraham Duarte.
Sven Polak is an Assistant Professor of Operations Research at Tilburg University, specifically in the Department of Econometrics and Operations Research within the Tilburg School of Economics and Management. He joined Tilburg University in May 2023 after completing a postdoctoral position at Centrum Wiskunde en Informatica (CWI) where he worked with Monique Laurent in the Networks and Optimization research group. His educational background includes a PhD in mathematics under Lex Schrijver at the University of Amsterdam (completed September 2019), a master's degree in Dutch Law from the same university (2017), and undergraduate studies culminating in a bachelor's thesis on quadratic forms under Gerard van der Geer (2012). Prior to his academic positions, he worked as an operations research engineer at ORTEC - Optimize Your World in Zoetermeer (October 2019 - August 2020). Dr. Polak's research focuses on the intersection of discrete mathematics and optimization, with particular expertise in convex relaxations of hard optimization problems and using algebraic techniques to reduce complexity in combinatorial, polynomial and semidefinite optimization. His work spans coding theory, graph theory, and information theory, with recent publications demonstrating sophisticated applications of semidefinite programming to problems in graph theory, coding theory, and quantum information. His research consistently bridges theoretical mathematics with practical computational approaches. His publication record shows a clear trajectory of increasingly sophisticated work in semidefinite programming applications, with recent papers focusing on Shannon capacity of graphs, crossing numbers of complete bipartite graphs, and mutually unbiased bases in quantum information theory. The consistent theme across his work is the application of algebraic and optimization techniques to solve fundamental problems in discrete mathematics. Dr. Polak teaches courses including Linear Algebra, Operations Research Methods, Essential Mathematics for Data Science, and Networks and Semidefinite Programming (as part of an LNMB PhD course with Monique Laurent). He has also presented his research at numerous international conferences including EURO, SIAM conferences, and workshops on combinatorial optimization.
Pierre-Yves LOUIS is a Professor at Institut Agro Dijon, part of Université Bourgogne Franche-Comté, France. He serves as the main responsible for the Data & Digital Specialization (DN2A) for engineers at the Dijon Agro Institute. Previously, he was a Maître de conférences (Associate Professor) at Université de Poitiers from 2009 to 2020. His affiliations include CNU 26 (Applied Mathematics and Applications of Mathematics), the Department of Engineering and Process Sciences (DSIP), UMR PAM IAD/UBE/INRAE (Food and Microbiological Processes), and the Institute of Mathematics of Burgundy (UMR 5584 CNRS). His research focuses on applied probability, stochastic algorithms, learning/adaptive algorithms, MCMC methods, and stochastic simulations and modeling. He applies statistical methods to life sciences, including clustering, data analysis, and text mining. His work encompasses statistical computing with R programming, random models, random dynamics, and stochastic models for large interacting systems in physics and life sciences. He has made significant contributions to the study of random fields, Gibbs measurements, spin systems, interacting particle systems, and probabilistic cellular automata (PCA), with mathematical and probabilistic aspects of statistical mechanics. His recent book Probabilistic cellular automata (Theory, Applications and Future Perspectives) published by Springer demonstrates his leadership in this field. His recent publications reveal a strong interdisciplinary approach, applying probabilistic methods to diverse domains. He has developed theoretical advances in urn models and interacting stochastic processes while simultaneously applying these methods to medical problems (pain assessment, chronic conditions), sports science (athlete performance analysis in alpine skiing), and food technology (nutritionally balanced meal generation through the HOX project). This demonstrates his ability to bridge theoretical probability with practical applications across multiple disciplines. Winner of the mathematics aggregation competition Editor of Probabilistic cellular automata (Theory, Applications and Future Perspectives) published by Springer While specific students aren't named in available materials, his authorization to direct research indicates he supervises PhD candidates. He has successfully collaborated with various institutions across Europe, including notable projects like 'HOX, mathematics for smart meals' in collaboration with Wuji and co (My Chef is Smart) with AMIES support, creating AI to generate nutritionally balanced menus. His work demonstrates strong grant acquisition capabilities and successful industry partnerships. LOUIS is affiliated with several research groups including the PMB team at UMR PAM IAD/UBE/INRAE in Dijon, the SPOC team at the Institute of Mathematics of Burgundy (UMR 5584 CNRS), and participates in networks like MAthématiques de l'Imagerie, Apprentissage et GEométrie Stochastique (RT MAIAGES) and Alea network (CNRS GDRI). His collaborative approach is evident through his co-organization of numerous scientific events and his international visiting researcher positions at institutions including IMT Lucca, University of Padova, and EURANDOM at TU Eindhoven.
Alessandra Tappini is a tenure-track Assistant Professor at the University of Perugia, Italy, since March 2025. Previously, she served as a Postdoctoral Researcher at the same institution from 2020 to 2025 and completed a visiting Ph.D. stint at the University of Tübingen, Germany (2018-2019). Her academic background includes: Ph.D. in Industrial and Information Engineering (2020), University of Perugia Master's degree cum laude in Computer and Automation Engineering (2016), University of Perugia Her research concentrates on graph theory applications, geometric computation, and data visualization methodologies. Key areas involve developing algorithms for structural data representation, spatial analysis frameworks, and interactive systems for complex information interpretation, with strong emphasis on computational efficiency and visual cognition principles.
Catherine Labruère Chazal serves as a Lecturer at the Institute of Mathematics of Burgundy (IMB), UMR CNRS 5584, within the University of Burgundy's Faculty of Science and Technology. She is an active member of the Statistics, Probability, Optimization and Control (SPOC) research team and holds significant administrative responsibilities including President of the L1 jury, Chair of Parcoursup and Study in France application review committees, and Head of Mathematics for L1 AGIL programs. Her research demonstrates exceptional interdisciplinary breadth, anchored in statistical methodology development. Primary expertise includes topological data analysis, functional principal components analysis, and biostatistical modeling, with applications spanning archaeology (computer-assisted pottery reconstruction), microbiology (Candida albicans pathogenesis), evolutionary biology (tooth morphology and echinoderm architecture), perinatal health (birth-weight curve modeling), and social sciences (ageism in fashion media and adolescent sports psychology). This cross-domain approach highlights the versatility of statistical frameworks in solving complex real-world problems. Publication trends since 2007 reveal consistent methodological innovation in statistical theory, particularly in handling high-dimensional and topological data structures. Her work increasingly bridges theoretical advances with practical applications in health sciences and cultural studies, evidenced by collaborations with medical researchers on perinatal networks and microbiologists studying fungal pathogenesis. Recent publications indicate growing engagement with social science questions, notably age representation in media. Within the IMB structure, she contributes to the SPOC team's mission of advancing research in stochastic processes, optimization theory, and statistical learning. Her teaching portfolio directly supports this research ecosystem through courses in machine learning (Python), algorithms (C++), and statistical methodology for doctoral students, fostering the next generation of quantitative researchers.
Dr. Henry Förster is a Post-Doc researcher at the Technical University of Munich (TUM) within the Chair of Efficient Algorithms, working under Prof. Stephen Kobourov. His research focuses on graph embeddings and network visualization , with applications in computer engineering, economics, and everyday contexts like metro maps. Ph.D. (Dr. rer. nat.) in Computer Science, summa cum laude, University of Tübingen (2020) M.Sc. in Computer Science, University of Tübingen (2016) B.Sc. in Engineering & Computing, TU Bergakademie Freiberg (2014) His work explores algorithmic efficiency , theoretical bounds for aesthetic criteria , and practical solutions for visualizing relational data. He has contributed to domains such as VLSI layouting, floor planning, and UML/Petri net diagram generation. Henry has received multiple accolades, including: Best Paper Award at GD 2024 First Place in GD Contest Live Challenges (2018–2022) Best Presentation Award at GD 2022 He actively contributes to academic communities as a PC member for GD2025, WG2025, and SafeToC advocate for GD. His teaching includes courses like Advanced Algorithms and Practical Course on Network Visualization .
Julien Albert is a researcher within the Namur Digital Institute (NaDI) and the Research Center on Information Systems Engineering at the University of Namur . He completed his master’s degree in computer science with a data-science focus in 2020 and is active in explainable AI, recommender systems, and human–computer interaction. Education: M.S. in Computer Science (data-science focus), University of Namur, 2020 – thesis on scientific-literature recommendation systems. Research Interests: Albert investigates how machine-learning decisions can be made transparent and trustworthy, with a particular emphasis on explainability for computer-vision models and fairness-aware personalized ranking . His work combines rigorous algorithmic development with human-centered empirical studies, bridging machine-learning research and interactive-systems design. Recent publications reveal a clear strand on explainable AI (XAI): evaluating saliency-based explanations, designing human-in-the-loop studies, and accelerating interpretation of nonlinear embeddings. A parallel track focuses on recommender systems , exploring both explanation interfaces powered by large language models and bias-aware Bayesian ranking algorithms. A third emerging theme tackles civic technologies , studying requirements for idea-browsing tools on digital participation platforms. He has (co-)supervised at least three master’s students and regularly participates in international venues such as the Francophone Conference on Human–Computer Interaction and the European Symposium on Artificial Neural Networks, indicating a growing footprint in the XAI and HCI communities.
Benedikt Hofer is a researcher at the Technical University of Munich (TUM), affiliated with the Professorship for Thermofluid Dynamics. His work focuses on the intersection of civil engineering, computer science, and digital modeling. University: Technical University of Munich Role: Researcher Contact: benedikt.hofer@tum.de Hofer’s research interests include Digital Twin , Building Information Modeling (BIM) , Graph Rewriting , and Parametric Modeling . His work emphasizes automated model generation, infrastructure digitization, and optimization through AI-driven methods. Recent publications highlight advancements in BIM collaboration , point cloud analysis for bridges , and graph-based design systems . These studies focus on scalability, version control, and integration of digital fabrication with BIM. The Technical University of Munich’s Thermofluid Dynamics group, led by Prof. Wolfgang Polifke, supports Hofer’s research in thermoacoustic interactions and fluid dynamics.
Amal Dev Parakkat is a tenured Assistant Professor at Telecom Paris (Institut Polytechnique de Paris) since September 2021, affiliated with the Computer Graphics Group and the IMAGES research team. His work focuses on sketch-based interfaces and geometry processing , particularly for digital content creation tasks like 3D shape inference and mesh generation. PhD in Computer Science from IIT Madras (2019), under Ramanathan Muthuganapathy Postdoctoral experience at Ecole Polytechnique (2019) and TU Delft (2020-2021) Research highlights include Surface Reconstruction , Interactive Modeling , and Geometric Algorithms . He leads the ANR SketchMAD project (2024-2028) and serves as International Mobility Chair at Telecom Paris. His publications span top conferences like SIGGRAPH Asia , Eurographics , and ACM CHI . Scientific Awards: SMI Young Investigator Award (2024) BEST PAPER AWARD (SIGGRAPH Asia Technical Communication 2023) Administrative roles include Head of Interaction, Graphics & Design at Telecom Paris. He mentors PhD students Tara Butler , Anandhu Sureshkumar , and Leiheng Qin , and collaborates with international researchers.
Lisha Chen is an Assistant Professor in the Department of Statistics at Yale University, located at 24 Hillhouse Avenue, New Haven. Her academic work focuses on developing statistical methodologies for high-dimensional data analysis, with particular emphasis on applications in machine learning and medical research. She maintains active research collaborations as evidenced by her publication record in top statistical journals. Her core research explores: Dimension reduction techniques including multidimensional scaling and sparse modeling Advanced regression methods such as reduced-rank regression and variable selection Statistical learning applications in autism research using eye-tracking data Novel algorithms for imbalance learning and multivariate testing Her work bridges theoretical statistics with practical applications in medicine and social sciences. Analysis of her 12 most recent publications (2008-2014) reveals three primary research streams: Development of dimension reduction frameworks for visualization and analysis Innovation in variable selection methodologies for regression and classification Application of statistical learning to autism spectrum disorder research Her publications demonstrate consistent focus on solving high-dimensional problems through novel statistical computing approaches. At Yale, she has taught multiple courses including: Data Analysis (Fall semesters: 2006-2008, 2011-2013) Introductory Statistics (Spring 2013) Data Mining and Machine Learning (Spring semesters: 2007-2009, 2011-2013) Unsupervised Learning: Dimension Reduction and Clustering Analysis (Spring 2009)
Darren Strash is an Associate Professor and Chair of the Computer Science department at Hamilton College. His research focuses on solving computationally challenging graph problems, including cliques, independent sets, and cuts, by integrating algorithm theory, combinatorial optimization, and operations research. He holds a Ph.D. and M.S. from the University of California, Irvine, and a B.S. from Cal Poly Pomona. Prior to Hamilton, he was a visiting professor at Colgate University and worked as a postdoctoral researcher at Karlsruhe Institute of Technology in Germany, followed by a career as a software engineer at Intel. Strash’s research interests emphasize practical data reduction techniques for graph problems, computational geometry, and dynamic data structures. His work often bridges theoretical foundations with real-world applications, such as maximum independent set algorithms and distributed graph generation. He has contributed to open-source projects like OpenStreetMap and teaches courses including Algorithms, Computational Geometry, and Design Principles. His recent publications highlight advancements in exact algorithms for edge clique cover, vertex clique cover, and scalable kernelization for maximum independent sets. He has advised students on topics like synergistic data reduction and exact solutions for graph problems. His work has been featured in conferences like ESA and ALENEX, and journals such as the ACM Journal of Experimental Algorithmics and SIAM proceedings.
Arild Hoff is a Professor of Quantitative Logistics at Molde University College, Faculty of Logistics. He teaches courses including LOG500 Management Models and Operations Research (Bachelor - Fall semester) and LOG530 Distribution Planning (Bachelor - Spring semester). Previously, he has taught several subjects within informatics, inventory and production management and exact optimization methods, both at bachelor and master level. His educational background includes: PhD in Logistics – Molde University College (2006) Cand. Scient. – University of Bergen (1998) Cand. Mag. – Molde University College (1994) Marketing – Trondheim Business School (1991) Business Economist – Trondheim Business School (1987) Professor Hoff's research focuses on operations research, planning, optimization and decision support, with particular emphasis on solution methods for combinatorial optimization problems, such as vehicle routing and hub location. His PhD thesis addressed "Heuristics for Rich Vehicle Routing Problems," exploring search methods for solving advanced vehicle routing problems with special constraints. His recent publications demonstrate a strong focus on maritime logistics, inventory routing problems, and optimization methods under uncertainty. His work spans applications in aquaculture, disaster relief, waste collection, and transportation planning, with a consistent emphasis on developing and applying advanced optimization techniques to complex real-world problems. Professor Hoff is actively involved in research groups including Energy Logistics (EneLog) and Planning, Optimization and Decision Support, with additional interests in aquaculture and other marine industries.
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.
Yuanzhe Jin is a DPhil Candidate at the Oxford e-Research Centre, University of Oxford. Their research focuses on interdisciplinary areas including AI integration in systems, machine learning applications, and data visualization. They contribute to advancing technologies like adversarial attack mitigation, generative AI workflows, and smart IoT systems. Key research interests span machine learning robustness, AI-driven education tools, and robotics navigation algorithms. Their work bridges theoretical advancements with practical implementations in domains such as environmental monitoring (via aerial imagery), UAV obstacle avoidance, and music generation through neural networks. Yuanzhe has collaborated on projects like the 'Otaku' dormitory management system and developed novel algorithms for mobile robotics path planning. Their publications reflect a strong computational focus with applications in both technical and creative fields. Currently affiliated with the Oxford e-Research Centre, they are actively involved in cutting-edge research at the intersection of artificial intelligence and real-world systems.
Ileana Streinu is an Adjunct Professor at the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst, a position she assumed in 2002. Previously, she joined the faculty at Smith College in 1994. Her research focuses on computational and combinatorial geometry, geometric algorithms, rigidity theory, graph theory, and applications in molecular biology (protein structure/folding), computer graphics, robotics, and graph drawing. Education: PhD in Computer Science, Rutgers University, 1994 Doctorate in Mathematics/Computer Science, University of Bucharest, Romania Professional Activities: Organized workshops, including the 2004 Bellairs Workshop on the Geometry of Modeling Proteins Co-organizer of sessions on protein modeling at the American Math Society Meeting (2004) Program committee member for the 2004 Fall Workshop on Computational Geometry Awards: George Moisil Award (2006) from the Romanian Academy for theoretical computer science contributions Her work bridges geometric foundations with applied domains, emphasizing interdisciplinary applications in biology and robotics.
