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Martin LoeblView profile

Professor

• Discrete Mathematics
• Graph Theory
• Combinatorial Optimization
• +10 more

Professor Martin Loebl is a distinguished academic at the Department of Applied Mathematics, Faculty of Mathematics and Physics, Charles University in Prague. His extensive teaching portfolio includes Discrete and Continuous Optimization, Linear Programming, Nonlinear Optimization, Game Theory for intelligent networks, and Linear Algebra. His office is located in room S 325 on the 3rd floor in Lesser Town, Malostranské nám. 2/25, Prague 1. Loebl's research spans the breadth of discrete mathematics with particular emphasis on graph theory, combinatorial optimization, and applications to statistical physics. His work bridges theoretical mathematics with practical applications in network optimization, market equilibria, and critical distribution systems. He has made significant contributions to Kasteleyn theory, dimer models, Ising model applications, matroid theory, and matching theory. Analysis of his recent publications reveals a strong focus on the intersection of discrete mathematics with economic and social systems. His work increasingly addresses real-world challenges such as resource allocation during crises, market design, and fair division problems. The publications demonstrate sophisticated applications of graph theory and combinatorial optimization to game-theoretic models and distribution systems, with growing emphasis on practical implementations of theoretical frameworks. Scientific Awards: Prize of Rector of Charles University for the book 'Topics in Discrete Mathematics: Dedicated to Jarik Nešetřil on the Occasion of his 60th birthday' Professor Loebl coordinates the H2020-MSCA-RISE-2018 project 'Combinatorial Structures and Processes (CoSP)' and previously coordinated the Czech Ministry of Interior's 'Critical Distribution System (CRISDIS)' project motivated by the COVID-19 pandemic. His research has practical applications in winter road maintenance routing, market equilibria design, and critical goods distribution systems. He maintains an active research laboratory focused on discrete mathematics applications, with particular emphasis on translating theoretical results into practical algorithms for optimization problems in network design, resource allocation, and market mechanisms. His group has produced significant work on dimer models, Kasteleyn orientations, and their applications to statistical physics problems.

• Global Optimization
• Optimization Algorithms
• Mathematical Analysis
• +4 more

James Calvin is a Professor in the Department of Computer Science at the New Jersey Institute of Technology (NJIT). His research focuses on optimization algorithms, global optimization techniques, convergence rate analysis, and mathematical modeling. He has led multiple federally funded projects, including studies on optimization algorithms for decision problems, efficient simulation of large-scale systems, and stochastic optimization methods. Education : Not explicitly detailed in the text. His research interests emphasize algorithmic development for complex systems, with specializations in global optimization algorithms, convergence rate analysis, and applications in image processing and mathematical modeling. Recent work includes advancements in centroid-based clustering algorithms and bi-objective decision-making frameworks. He has received grants from the National Science Foundation for projects such as 'Optimization Algorithms For Decision Problems With Many Variables' and 'Efficient Simulation of Large-Scale Systems.' Media coverage highlights his contributions to understanding mitochondrial proteins and wood elasticity. Grants & Projects : Optimization Algorithms For Decision Problems With Many Variables (2016–2019) Efficient Simulation of Large-Scale Systems (1999–2004) Calvin collaborates internationally on optimization challenges and has produced over 66 peer-reviewed publications since 1988.

Antoine DaillyView profile

Research Fellow

• Graph Algorithms
• Combinatorial Optimization
• Sensor Networks
• +3 more

Antoine Dailly is a Research Fellow at INRAE within the TSCF research unit. Previously, he held postdoctoral positions at LIMOS (2022–2024) under Florent Foucaud, UNAM Juriquilla (2019–2021), and teaching roles (ATER) at IUT2 Grenoble (2021–2022) and University of Grenoble Alpes (2018–2019). He completed his PhD at Université Claude Bernard Lyon 1 (2015–2018) in the LIRIS laboratory. Research Focus: Dailly specializes in graph algorithms, combinatorial optimization, and sensor networks. His work includes: Algorithmic approaches to metric problems in graphs, uncertainty representation, and autonomous agent trajectory optimization. Combinatorial games, graph criticality, and structural/algorithmic challenges in vertex deletion, coloring, and equilibrability. Current projects: ANR GRALMECO (metric covering in graphs) and ConnecSenS (sensor networks). Publication Trends: His 15 most recent articles (2017–2025) primarily explore combinatorics, discrete mathematics, and algorithmic theory. Key themes include: Planar graphs, geodetic games, and temporal graph resolving sets. Complexity of Arc-Kayles, path covers in DAGs, and digraph metric dimensions. Applications in game theory, optimization, and information theory. Teaching & Outreach: Dailly has taught algorithms, programming (C/Python/XML), and graph theory at ISIMA, IUT, UNAM, and Grenoble. He co-leads the ISO group, promoting computer science education without computers. Affiliations: Member of AlCoLoCo (algorithmic research group) and labs including G-SCOP, LIRIS, and LIMOS.

Robert HoffmanView profile

Researcher

• Point-of-Care Ultrasound
• Pediatric Emergency Medicine
• Critical Care Ultrasound
• +7 more

Dr. Robert Hoffman is a researcher and clinician in the Division of Emergency Medicine at Boston Children's Hospital, with a focus on point-of-care ultrasound (POCUS) in pediatric critical care. He actively contributes to clinical research, education, and ultrasound program development. Research Interests: Dr. Hoffman's work centers on the application of lung and cardiac ultrasound in diagnosing pediatric emergencies such as asthma-related pneumonia and sepsis. He also investigates nutritional assessment using ultrasound and bioimpedance in critically ill children. His research bridges clinical innovation with medical education. Publication Trends: His recent publications (2021–2025) emphasize emergency ultrasound and pediatric critical care, while earlier works (2014–2017) explore cancer metabolism, suggesting a shift in research focus over time. The interdisciplinary nature of his work reflects both clinical and translational expertise. Education and Training Role: He is actively involved in training pediatric and emergency medicine residents, contributing to curriculum design and ultrasound credentialing for fellows and attending physicians. Advising and Grants: While no specific students or grants are listed, his leadership in educational programs and research units indicates a mentoring role within the institution. Labs and Teams: Dr. Hoffman is a member of the Ultrasound Subdivision within the Division of Emergency Medicine, where he collaborates on research, education, and clinical implementation of POCUS technologies.

Giulia CodenottiView profile

Professor

• Discrete Geometry
• Combinatorial Geometry
• Convex Geometry
• +6 more

Prof. Giulia Codenotti is a Junior Professor in the Discrete Geometry and Topological Combinatorics Group at the Institute of Mathematics, Freie Universität Berlin. Her research focuses on lattice polytopes, convex geometry, and simplicial complexes, with an emphasis on unimodular covers, triangulations, and algebraic-topological invariants. She holds an office at Arnimallee 2, Room 105/103, and can be reached at giulia.codenotti@fu-berlin.de. Her academic roles include teaching courses like "Discrete Geometry 1" and supervising research in combinatorial and convex geometry. Prior to her position at FU Berlin, she taught at Goethe University Frankfurt, leading seminars and exercise sessions in discrete mathematics and geometric optimization. Research Interests: Discrete and combinatorial geometry Lattice polytopes and their subdivisions Triangulations and unimodular covers Algebraic and topological invariants of simplicial complexes Covering minima and convex body geometry Outreach Activities: Soapbox Science Berlin (2019) - Public engagement on higher-dimensional geometry Girls' Day initiatives for promoting STEM among schoolgirls Co-creator of Polytopia, a project showcasing polyhedrons to the public Teaching Philosophy: Emphasizes foundational concepts in discrete geometry through problem-solving, with active participation in exercise sessions and rigorous assessment criteria including coursework and exams.

• Numerical Analysis
• Scientific Computing
• Matrix Theory
• +5 more

Nicola Guglielmi is a Professor of Numerical Analysis at the Gran Sasso Science Institute (GSSI) in L'Aquila, Italy, since February 2018. He currently serves as Deputy Rector of GSSI (2022–2026) and Director of the Division of Mathematics (2019–2025). Previously, he held positions at the University of L'Aquila, including Full Professor of Numerical Analysis (2006–2018). His education includes a Laurea from the University of Bologna (1991, summa cum laude) and a PhD in Computational Mathematics from the University of Padova (1996). His research focuses on numerical analysis, particularly delay differential equations, matrix theory applications, and stability analysis of dynamical systems. He has authored over 100 publications and developed the RADAR5 software for delay differential equations. Recent grants include the 2023 MIUR/PNRR project on geothermal energy modeling and the 2022 PRIN project on numerical methods for PDEs. Notable awards include the New Talent Award at SciCADE-99 (1999) and a plenary lecture at the UMI-23 conference (2023). He has supervised numerous PhD students, many now in academia and industry. Upcoming activities include visits to the Courant Institute (2025) and McGill University (2025). His work bridges theoretical and applied mathematics, with contributions to scientific computing, control theory, and data science.

• Biomechanics
• Orthopedic Implants
• Medical Simulators
• +5 more

Luc Labey is an Associate Professor in the Department of Mechanical Engineering at KU Leuven's Faculty of Engineering Technology, based at the Geel Campus where he serves as head of Subdivision 14 and contact person for the BioMechanics (BMe) research group, also known as BEADs (Biomechanical Engineering of Assistive Devices). His work bridges engineering principles with clinical applications, focusing on the development and optimization of medical devices that interact with the human body. Dr. Labey's research interests center on biomechanics of human joints, orthopedic implants, medical simulators, and motion analysis. His work particularly emphasizes the design and testing of assistive devices for pediatric neurological patients and those with orthopedic conditions. The BEADs research group employs a multidisciplinary approach combining in vivo patient measurements, laboratory experiments with simulators, and computer simulations to optimize device functionality. His recent publications reveal a strong focus on exoskeleton development, particularly for hip and ankle assistance in children with cerebral palsy, alongside research on knee mock-ups for orthopedic training. The work spans biomechanical testing, device design, and clinical validation, demonstrating a consistent trajectory toward creating practical medical solutions through engineering innovation. Dr. Labey serves in multiple academic roles including membership on the Council of the Faculty of Engineering Technology, the Mechanical Engineering Department Council, and the Teaching Portfolio Peer Review Committee. He teaches various engineering courses covering mechanics, manufacturing technologies, and materials science.

• Geometry processing
• Massive model rendering
• Procedural generation
• +4 more

Antonio Chica is an Associate Professor at the Department of Computer Science, Universitat Politècnica de Catalunya (UPC), specializing in geometry processing, real-time rendering, and virtual reality applications. His research focuses on 3D reconstruction, procedural landscape generation, and LiDAR data optimization. Teaching at Terrassa School of Engineering and Barcelona School of Informatics Member of the Modeling, Visualization, Interaction and Virtual Reality Group Key research areas include: Geometry processing techniques for signed distance fields Procedural generation of 3D landscapes and vegetation Game development frameworks and VR training systems Efficient algorithms for massive point cloud rendering His recent publications emphasize Bayesian reconstruction methods, adaptive SDF approximations, and optimized VR training tools. He actively collaborates on LiDAR data calibration, terrain modeling, and cultural heritage visualization projects. Antonio Chica's work integrates advanced graphics algorithms with practical applications in urban modeling, medical training, and historical preservation. He develops open-source tools like MeshPipe to simplify geometry processing workflows.

• Parametric design
• Computer graphics
• Algorithm robustness
• +4 more

Neil Frederick Stewart serves as Associate Professor in the Department of Computer Science and Operations Research at the Faculty of Arts and Sciences, University of Montreal. He joined the department in 1971 after teaching at the University of Guelph (1970-1971) and previously served as department director from 1983-1985. His academic background includes undergraduate studies in mathematics at the University of British Columbia (1964) and a PhD in computer science at the University of Toronto (1968), which he believes was the third PhD in computer science proper awarded in Canada. His research focuses on algorithm design for subdivision surfaces in solid modeling, with applications in computer graphics and vision systems. Professor Stewart's key research interests include: Parametric design Computer graphics Algorithm robustness Subdivision surfaces Computer vision Solid modeling Geometric modeling His recent research has been funded by NSERC and MITACS, with projects spanning from subdivision surface methods (1994-2016) to Document AI algorithms (2023) and Transactional Fraud Detection (2022). Throughout his career, Stewart has supervised numerous graduate students whose thesis work covers reliable solid modeling, geometric computation, and spatial deformation visualization. His laboratory work centers on robust computation for geometric models and subdivision surface applications in computer graphics.

Wenjie HeView profile

Associate Professor

• Wavelet Analysis
• Multiresolution Tight Frames
• Computer Graphics
• +5 more

Wenjie He is an Associate Professor in the Department of Computer Science at the University of Missouri-St. Louis. He holds a PhD from the University of Georgia. His research focuses on wavelet analysis, multiresolution tight frames, computer graphics (e.g., subdivision surfaces), image processing, evolutionary algorithms, and noise removal techniques. His interdisciplinary work bridges mathematics and computational methods, with applications in signal processing and data analysis. Education: PhD, University of Georgia Research Interests: Wavelet Analysis and Multiresolution Frames Image Processing and Noise Removal Computer Graphics (Subdivision Surfaces, Stroke-based Rendering) Evolutionary Algorithms His research output includes publications on topics such as nonstationary wavelet frames, spline-based signal processing, and evolutionary optimization techniques for rendering. His work emphasizes mathematical rigor and practical algorithmic solutions for real-world problems. Grants and Labs: No specific grants or labs mentioned in the provided text, but his research aligns with computational and mathematical methodologies common in academic computer science departments.

• Combinatorics
• Theoretical Computer Science
• Discrete Mathematics
• +2 more

Matthieu Rosenfeld is an Assistant Professor at the University of Montpellier, affiliated with the LIRMM laboratory and the ESCAPE team. He teaches in the Computer Science department of IUT Montpellier-Sète. His research interests include combinatorics , theoretical computer science , and discrete mathematics , focusing on combinatorics on words and graph theory . He frequently employs computer-assisted proofs for avoidability problems. Recent publications explore topics like nonrepetitive colorings in Euclidean space, undecidability in bilinear systems , Vizing's problem for triangle-free graphs, Shur's conjecture in power-free languages, and word reconstruction via subword queries . His work often intersects formal languages , graph coloring , and algebraic structures , with applications to automata theory, logic, and algorithm design.

• Operations Research
• Logistics Optimization
• Scheduling Algorithms
• +5 more

Jeroen Belien is a Full Professor at the Faculty of Economics and Business (FEB) at KU Leuven. He leads the Operations Management Research Group and serves as Head of Subdivision 42 at the Brussels Campus. His roles include coordinating research activities and contributing to the KU Leuven Institute for Mobility (LIM). His research focuses on operations management, with emphasis on optimization models for logistics, waste collection, sports scheduling, and healthcare operations. He has pioneered educational tools like role-playing games for teaching linear programming and inventory management concepts. His work integrates simulation, mathematical programming, and real-world case studies. Key contributions include developing timetabling algorithms for international competitions, optimizing bio-waste collection in urban areas, and analyzing collaborative shipping strategies in the sharing economy. His publications span over 50 articles in journals like Operations Research for Health Care and INFORMS Transactions on Education. Belien actively engages in educational innovation through game-based learning and case competitions, emphasizing practical applications of operations research methods.

Tiberiu PopaView profile

Associate Professor

• Computer Graphics
• 3D Modeling
• 3D/4D Acquisition
• +4 more

Tiberiu Popa is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University since 2013. Previously, he was a Senior Researcher at ETH Zurich's computer graphics lab and earned his PhD from the University of British Columbia. Education: PhD in Computer Science from University of British Columbia His research focuses on geometric modeling and optimization, 3D/4D acquisition, performance capture, physics simulation, and machine learning applications. He emphasizes user-centric tools for creative modeling, 3D reconstruction techniques, and integration of physical properties in design workflows. His courses include Geometric Modeling, Computer Animation, and Computer Graphics, structured as a five-chapter narrative linking theory to real-world applications. Popa’s work spans computational design, medical modeling, and interactive systems like MARIN, an open-source mobile augmented reality platform. He actively mentors students and collaborates on projects such as cloth modeling for manufacturing and architecture optimization.

Kaie KubjasView profile

Associate Professor

• Algebraic Statistics
• Nonlinear Algebra
• Matrix and Tensor Decompositions
• +5 more

Kaie Kubjas is an Associate Professor at Aalto University in the Department of Mathematics and Systems Analysis, School of Science. Since 2024, she has held a tenured position, following a tenure-track role from 2017–2024. She earned her PhD in Mathematics at Freie Universität Berlin (2013) under Professors Christian Haase and Klaus Altmann, with postdoctoral research at institutions including the Max Planck Institute and MIT. Her research focuses on applied nonlinear algebra, algebraic statistics, and their applications in biology (e.g., phylogenetics and 3D genome reconstruction), as well as matrix/tensor decompositions. She has organized major events like the European Women in Mathematics General Meeting 2022 and the 2025 workshop on Algebraic Statistics and Multistate Models. Kubjas serves on editorial boards of journals like SIAM Journal on Applied Algebra and Geometry and Annales Fennici Mathematici . Recent work includes advances in log-concave maximum likelihood estimation, 3D genome reconstruction, and structured matrix decompositions. Her students, such as Olga Kuznetsova (Second Place MEGA 2021 Poster Award winner), have contributed to these areas. She regularly contributes to seminars like the Algebra and Discrete Mathematics at Aalto, fostering interdisciplinary collaboration.

• Algorithms
• Graph Theory
• Network Analysis
• +5 more

Dr. Erik Jan van Leeuwen is an Assistant Professor in the Department of Algorithms and Complexity within the Faculty of Science at Utrecht University. His research focuses on theoretical computer science, particularly in algorithms, graph theory, and computational complexity. He teaches courses including Algorithms, Network Science, and ICS tutorials. His primary research interests include algorithms, graph theory, network analysis, computational complexity, data structures, and computational geometry. Van Leeuwen's work often centers on forbidden subgraph problems, parameterized complexity, and graph algorithms, with significant contributions to understanding the complexity of problems on specific graph classes. His recent publications (2023-2025) demonstrate a strong focus on complexity frameworks for forbidden subgraphs, with multiple papers exploring different aspects of this framework. He has made significant contributions to understanding the complexity of problems like vertex covers, disjoint paths, and diameter problems on H-free graphs. His research also extends to parameterized complexity, streaming algorithms, and network design problems, showing a diverse yet cohesive research program in theoretical computer science. Dr. van Leeuwen is actively involved in the Foundations of Complex Systems and the Utrecht Platform for Applied Data Science, connecting his theoretical work with broader applications. His research has been consistently published in top theoretical computer science venues including Algorithmica, SIAM Journal on Discrete Mathematics, and conference proceedings from major algorithms conferences.