جست‌وجوی استاد و پژوهشگر

Kai Hormannمشاهده پروفایل

استاد

• Computational Sciences
• Geometric and Visual Computing
• Informatics
• +۳ مورد دیگر

Kai Hormann is a full professor in the Faculty of Informatics at Università della Svizzera italiana (USI Lugano). He earned his Ph.D. in computer science from the University of Erlangen-Nürnberg in 2002 and has held positions at Clausthal University of Technology, Caltech, and the CNR in Pisa. His research focuses on mathematical foundations of geometry processing, including barycentric coordinates, subdivision algorithms, and rational interpolation. Hormann has authored over 100 publications and serves as an associate editor for journals like Computer Aided Geometric Design and Dolomites Research Notes on Approximation . In 2024, he received the prestigious John A. Gregory Award for his contributions to geometric modeling. Education : Ph.D. in Computer Science, University of Erlangen-Nürnberg (2002) Diploma in Mathematics, University of Erlangen (1997) Abitur, Leibniz-Gymnasium Bad Schwartau (1992) Research Interests : Hormann’s work bridges computer science, applied mathematics, and engineering. His current projects include interactive shape deformations, time-dependent object processing, 3D visualization, and GPU-accelerated algorithms. His research areas encompass geometry processing, computational sciences, and numerical analysis. Publications : His recent articles focus on barycentric coordinates, subdivision schemes, and surface reconstruction. Themes include transfinite coordinates, curvature continuity, and efficient interpolation methods. Awards : John A. Gregory Award (2024) Chair of SIAM Activity Group on Geometric Design (2017–2018) Advising & Grants : He has advised numerous PhD students, including work on barycentric rational curves and subdivision schemes. He has organized conferences such as the SIAM GD and GMP series, reflecting his leadership in computational geometry. Labs & Teams : Hormann is affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI), collaborating on interdisciplinary projects in AI and geometry processing.

Chee Yapمشاهده پروفایل

استاد

• Computer algebra
• Symbolic-numeric computation
• Robotics
• +۵ مورد دیگر

Chee Yap is a Professor of Computer Science at the Courant Institute of Mathematical Sciences, New York University, where he has been a faculty member since 1979. He previously held positions at the University of Southern California and has served as a Visiting Professor at several prestigious institutions including Oxford University, Seoul National University, and the Chinese Academy of Sciences. Dr. Yap received his BS in Computer Science & Mathematics (Double Major) from MIT in 1975 and his PhD in Computer Science from Yale University in 1980. His academic journey began at the Boys' Wing, Royal Military College in Malaysia. His research spans several areas in theoretical computer science and computational mathematics, with primary focus on Computer algebra and symbolic-numeric computation , Robotics , Visualization , Theory of algorithms , Exact computation , Complexity theory , and Computational geometry . His work bridges theoretical foundations with practical implementations, exemplified by his development of the Core Library for exact computation and the G-Vis System for visualization. Dr. Yap's publication record shows a consistent focus on robust geometric computation, algorithmic motion planning, and exact numerical methods. His recent work (2021-2026) demonstrates continued innovation in parameter estimation for differential equations, subdivision models, path planning algorithms, and root clustering techniques. His research maintains a strong connection between theoretical computer science and practical applications in robotics and scientific computing. Among his notable honors are the CAS President's International Visiting Scientist Fellowship (2018), KIAS Scholar award (2006-2009), Lester R. Ford Award (1990), and Fulbright Scholar Award (1988). He has also received multiple ACM Recognition of Service Awards for his conference leadership. Dr. Yap has been continuously funded by the National Science Foundation since 1984, securing approximately 20 grants valued at about $5 million. He has mentored 17 PhD students and 4 postdocs throughout his career. His editorial service includes long tenures on the boards of SIAM Journal of Computing (1985-2002), Journal of Computer and System Sciences (1986-2014), and Journal of Symbolic Computation (1988-2004), among others. He is the principal architect of the Core Library (http://cs.nyu.edu/exact) and co-architect of the G-Vis System (http://cs.nyu.edu/visual), and holds two visualization patents. His leadership in the academic community is evident through his roles as General Chair of the 42nd ISSAC conference and various program committee positions for major conferences including FOCS, ISSAC, SoCG, and WAFR.

Tamy Boubekeurمشاهده پروفایل

استاد

• 3D Computer Graphics
• Computer-Aided Design
• Real-Time Rendering
• +۳ مورد دیگر

Tamy Boubekeur is a Senior Director and Senior Principal Research Scientist at Adobe Research France, leading Adobe Research France and the Computer Graphics Group at Telecom Paris. He is a part-time Professor in Computer Science at École Polytechnique, Institut Polytechnique de Paris. Previously, he held roles including Full Professor at Telecom Paris (on leave since 2019), Chief Scientist at Allegorithmic, and Founder/Director of the Computer Graphics Group at Telecom Paris. His work bridges academia and industry, focusing on 3D computer graphics, geometry processing, and real-time rendering. Education: HDR (2012), PhD (2007), and MSc (2004) in Computer Science from French universities. Professional experience spans roles at INRIA, TU Berlin, and UBC. He has been involved in significant projects like FEMONUM (medical modeling) and 3DLife (European project). Research interests include 3D shape analysis, rendering techniques, GPU programming, and global illumination. His work emphasizes efficient processing of geometric data, with applications in medical imaging, animation, and real-time visualization. Notable contributions include SQEM for shape approximation and pioneering point-based global illumination techniques. Honors include multiple Best Paper Awards at Eurographics, SIGGRAPH, and Shape Modeling International. He has supervised numerous PhD students, many of whom now hold academic and industry roles. Boubekeur has chaired major conferences (e.g., EGSR 2019) and served on program committees for SIGGRAPH, EUROGRAPHICS, and others.

• Software Testing
• Functional Safety of Embedded Software
• Embedded Software Resilience in Harsh Environments
• +۱ مورد دیگر

Jeroen Boydens is a full-time professor at the Department of Computer Science, Faculty of Engineering Technology, KU Leuven. He is also the department vice chair, contact person for the Distributed and Secure Software (DistriNet) group at Bruges Campus, and head of the M-Group Software Engineering subdivision. His work focuses on software testing, functional safety, and resilience in embedded systems, particularly in harsh environments and autonomous systems. Research Interests: Software Testing, Functional Safety, Embedded Software Resilience, Autonomous Systems Grants & Projects: Leading/Co-leading 10+ projects (2021–2027) on safety-security co-engineering, ML safety in autonomous vessels, and resilience frameworks for embedded systems Advising: Supervising PhD candidates Yang Yang, Mahdi Nikseresht, and Bart De Blaere Labs: Active in DistriNet (Bruges Campus) and M-Group Software Engineering

• Operational Research
• Combinatorial Optimisation
• Mathematical Programming
• +۷ مورد دیگر

Greet Vanden Berghe is a full professor at the Faculty of Engineering Technology of KU Leuven. She leads the Subdivision Combinatorial Optimisation and Decision Support at Ghent and Aalst Campuses, and contributes to research networks like Leuven.AI and LIM. Her work focuses on industrial combinatorial optimization problems through mathematical programming, heuristics, and decomposition approaches. Affiliations: Numerical Analysis and Applied Mathematics (NUMA), Leuven.AI, LIM Institute Research: Logistics optimization, personnel scheduling, vehicle routing, and algorithm development for industrial decision support Projects: MoVeRS (Modular Vehicle Routing Solver), Trustworthy Algorithms for Industrial Decisions, Fairness in Allocation Problems, and Electric Vehicle Charging Station Optimization Her research emphasizes bridging theoretical advancements with real-world industrial challenges, particularly in maritime logistics, healthcare scheduling, and production planning. She has developed scalable solvers and integrated legal constraints into routing algorithms.

• Artificial Intelligence
• Machine Learning
• Industrial Applications of AI
• +۳ مورد دیگر

Mathias Verbeke is an Associate Professor at the Faculty of Engineering Technology , affiliated with the Department of Computer Science at KU Leuven. He serves as Contact Person for the Declarative Languages and Artificial Intelligence (DTAI) group at the Bruges Campus and leads the M-Group Industrial Artificial Intelligence subdivision. His research focuses on Artificial Intelligence, Machine Learning , and their industrial applications, particularly in predictive maintenance, digital twin technology, and process optimization. His recent work explores uncertainty quantification in multi-objective optimization, anomaly detection frameworks, and physics-guided deep learning for thermal modeling. He actively contributes to Leuven.AI and collaborates with groups like Infinity and M-Group . At the institutional level, Verbeke participates in the Faculty of Engineering Technology Council and Computer Science Department Council . Verbeke teaches courses including Machine Learning , Artificial Neural Networks and Deep Learning , and Master’s Thesis Artificial Intelligence in Business and Industry .

Markus Steinbergerمشاهده پروفایل

دانشیار

• GPU Computing
• Computer Graphics
• Parallel Processing
• +۳ مورد دیگر

Markus Steinberger is an Associate Professor at Graz University of Technology (Austria), leading the GPU Computing and Visualization Group at the Institute for Computer Graphics and Vision. He holds an MSc and PhD in Computer Science from TU Graz, with a postdoc and multiple leadership roles in academic and industry labs, including NVIDIA and the Max Planck Institute. His research focuses on GPU scheduling, parallel computing, and high-performance visualization techniques. He has led teams at TU Graz, Huawei Technologies (Cloud Rendering Lab), and the Max-Planck-Center for Visual Computing. Education 11/2020: Habilitation in Practical Computer Science at TU Graz 10/2013: PhD in Computer Science (Dynamic Resource Scheduling on Graphics Processors) 10/2010: MSc in Telematics 10/2005: BSc in Telematics Research Interests Steinberger’s work centers on optimizing GPU computing for real-time rendering and parallel processing. He explores efficient resource scheduling, dynamic graph management, and high-performance visualization techniques, with applications in cloud rendering, scientific visualization, and interactive graphics. His methods emphasize scalability and real-world applicability in both academic and industrial settings. Awards Heinz Zemanek Prize (2016) – First Austrian recipient GI Dissertation Prize (2014) – Best dissertation in German-speaking computer science Multiple Eurographics Best Paper Awards (2021, 2020, 2014) HPEC Best Student Paper (2017) ACM CHI Honorable Mention (2014) Advising & Grants Steinberger has advised numerous students and led projects funded by EU initiatives and industry partnerships. His research has been recognized through grants supporting GPU scheduling, cloud rendering, and parallel computing. Labs & Teams Leads the GPU Computing and Visualization Group at TU Graz and directed the Cloud Rendering Lab at Huawei, focusing on scalable rendering solutions for distributed systems.

• Computational Geometry
• Combinatorial Geometry
• Discrete Geometry
• +۷ مورد دیگر

Natan Rubin is a faculty member in the Computer Science Department at Ben-Gurion University of the Negev, Beer-Sheba, Israel, where he has been conducting research in combinatorial and computational geometry since 2014. He is the principal investigator of a 5-year ERC Starting Grant project titled 'Combinatorial Aspects of Computational Geometry' (CombiCompGeom), which supports graduate students and postdocs in geometric algorithms and structures. Ph.D., Tel Aviv University, 2012 Advisor: Prof. Haim Kaplan and Prof. Micha Sharir His research focuses on fundamental problems in computational geometry, including geometric transversals , epsilon-nets , Voronoi diagrams , Delaunay triangulations , and intersection patterns of geometric objects . He has made significant contributions to the understanding of combinatorial bounds in geometric settings, such as resolving the Richter-Thomassen conjecture for pairwise intersecting Jordan curves and improving long-standing bounds on weak epsilon-nets. The recent publications reveal a consistent trend toward improving asymptotic bounds in high-dimensional and planar geometric configurations, with a strong emphasis on combinatorial methods and topological reasoning. His work often intersects with extremal combinatorics and discrete geometry, particularly in analyzing crossing and touching structures in planar graphs and families of convex sets. His scientific recognition includes: Best Paper Award at FOCS 2013 Best Paper Award at SoCG 2012 Rubin actively contributes to the academic community through service, having organized major workshops such as SODA 2018 and SoCG 2022, and hosting international researchers. He collaborates widely with leading figures in the field, including Pankaj Agarwal, János Pach, Micha Sharir, and Haim Kaplan. Though no formal list of students is provided, his ERC-funded project explicitly advertises multiple graduate and postdoctoral positions, indicating active mentorship. He is also involved in organizing international workshops and fostering collaboration within Israel’s strong computational geometry community, including researchers at BGU, Tel Aviv, and Jerusalem. His research is supported by competitive grants and involves the development of robust kinetic data structures and stable geometric graphs, with applications in dynamic environments and algorithmic stability.

Caroline Moosmuellerمشاهده پروفایل

استادیار

• Computational Optimal Transport
• Geometric Data Analysis
• Numerical Analysis
• +۴ مورد دیگر

Caroline Moosmueller is an Assistant Professor in the Department of Mathematics at the University of North Carolina at Chapel Hill, where she leads the Geometric Data Analysis research group. She holds a B.Sc and M.Sc in Mathematics from the University of Vienna and a Ph.D in Technical Mathematics from Graz University of Technology. Her professional background includes postdoctoral work at Johns Hopkins University (2017-2019) and a Visiting Assistant Professor position at the University of California, San Diego (2019-2022). Her research develops numerical methods for nonlinear and high-dimensional data analysis with focus on structure-preserving algorithms. Key areas include: Computational optimal transport and Wasserstein space analytics Geometric machine learning and classification tasks Approximation theory for biological and medical applications Dimensionality reduction techniques for complex datasets Publication analysis reveals strong focus on optimal transport theory applications in machine learning and biomedicine, with recent work exploring trajectory inference, dimensionality reduction, and stochastic measure transport methods. Her papers consistently integrate theoretical rigor with computational implementations. Awards include: J. Burton Linker Fellowship She leads multiple funded projects: NSF awards DMS 2111322, 2306064, 2410140 UNC School of Data Science Seed Grant for "Spatio-temporal analysis of brain functional connectome" (with Kovalsky/Styner/Wu) and mentors seven graduate students. Her lab focuses on geometric algorithms for cancer research and biomedical data analysis.

• Robotics
• Automation
• Mechatronics
• +۵ مورد دیگر

Eric Demeester is an Associate Professor at the Faculty of Engineering Technology, KU Leuven, affiliated with the Department of Mechanical Engineering and the Robotics, Automation and Mechatronics (RAM) group. He leads RAM and the Subdivision ACRO, with additional roles as contact person for robotics initiatives and council member for academic governance. Academic Rank: Associate Professor Leadership Roles: Head of RAM, Head of Subdivision ACRO Research Focus: State estimation, decision-making under uncertainty, robotic wheelchairs, user modeling, plan recognition, shared control His research spans robotics applications in diverse domains including humanitarian demining, agricultural automation, pharmaceutical manufacturing, and radiological mapping. Projects involve sensor fusion (GNSS-IMU), machine learning for low-data environments, and haptic control systems. Current advisees include Wouter Abbeloos and Pieter Aerts. Key projects include MineInsight (2024-2028), APL-SuppOr (2024-2027), and ROBUST (2024-2025), where he serves as promotor or co-promotor. His work emphasizes practical implementation in industrial and challenging environments.

Pieter Vansteenwegenمشاهده پروفایل

استاد

• Public Transportation Optimization
• Metaheuristics
• Demand-Responsive Transport Systems
• +۴ مورد دیگر

Prof. Pieter Vansteenwegen is a full professor at KU Leuven's Faculty of Engineering Science, leading the Centre for Industrial Management/Traffic and Infrastructure (CIB) and chairing the KU Leuven Institute for Mobility (LIM). With a PhD in Operations Research (2008), he works on robust public transport systems, metaheuristics for logistics, and demand-responsive mobility solutions. Chair, KU Leuven Institute for Mobility Program Director, Master of Mobility and Supply Chain Engineering Board member, International Association of Railway Operations Research His research integrates transportation engineering , logistics optimization , and smart mobility systems , focusing on: Demand-responsive public transportation Metaheuristic algorithm development Inventory routing problems Smart waste collection strategies Multi-robot scheduling Recent publications demonstrate expertise in variable neighborhood search , Benders decomposition , and genetic algorithms applied to public transport, satellite scheduling, and circular economy logistics. His work has been cited over 9,400 times (Google Scholar H-index 51). Scientific recognition includes: Multiple IAROR best paper awards Best Reviewer European Journal of Operational Research BIVEC-GIBET PhD awards (2009, 2021, 2023) RASIG-INFORMS Student Paper Prizes He directs KU Leuven's Planning and Operational Research subdivision , serves on faculty councils, and founded spin-off dyNAVic for electronic tourist guides. His teaching portfolio includes courses in Public Transportation Design , Distribution Logistics , and Operational Management .

• Logistics
• Urban Freight Modelling
• Agent-Based Simulation
• +۳ مورد دیگر

Johan Joubert is a Professor at the Department of Mechanical Engineering , Faculty of Engineering Science , KU Leuven. His academic roles include leadership as Head of the Subdivision Logistics and Optimization. He actively contributes to research and teaching in logistics, urban freight modelling, and agent-based simulation. Academic Affiliation: Department of Mechanical Engineering, KU Leuven Leadership: Head of Subdivision Logistics and Optimization Research Collaborations: Member of LIM - KU Leuven Institute for Mobility His research focuses on logistics sprawl , urban freight dynamics , and agent-based simulation for travel behavior. Recent projects include Waste logistics in urban freight (2024-2028) and STRUAAS (2024-2026), emphasizing sustainable urban supply chains and operations research. Key publications span transport geography , network analysis , and driver risk evaluation . Examples include Logistics sprawl and freight activity dynamics (2022) and Accessibility in Post-Apartheid Cities (2018). His work integrates geospatial data, behavioral models, and sustainability metrics. Teaching involves courses like Supply Chain Engineering (H08J0A) and Multi-Agent Systems (H02H4A), supported by DataCamp for data science integration. Engagement includes mentoring students and industry collaborations through KU Leuven’s mobility research networks.

• Reliability of Mechanical Systems
• Structural Dynamics
• Uncertainty Quantification
• +۳ مورد دیگر

David Moens is Full Professor and Chair of the Department of Mechanical Engineering at KU Leuven's Faculty of Engineering Technology. He leads the Mecha(tro)nic System Dynamics (LMSD) research group at De Nayer Campus, where his research focuses on reliability engineering, structural dynamics, and computational mechanics. Moens serves in multiple leadership roles including membership in the university's Research Policy Council and heads Subdivision 10 of the De Nayer Campus. His research integrates computational mechanics with uncertainty quantification methods to address challenges in mechanical system reliability. Primary research domains include: Non-deterministic numerical analysis techniques for structural systems Fatigue and lifetime prediction methodologies Spatial uncertainty quantification in composite materials Physics-informed machine learning for engineering applications Robust design optimization under uncertainty Moens' recent publications demonstrate strong emphasis on computational uncertainty frameworks with applications spanning composite pressure vessels, manufacturing process twins, and neural network uncertainty estimation. Research consistently intersects finite element analysis, experimental validation, and emerging machine learning techniques to solve reliability challenges in mechanical systems. He leads significant research initiatives including RISQ (Random and Interval Spatial Uncertainty Quantification, 2025-2027) and projects on hydrogen storage vessel reliability (2024-2028). As primary supervisor for multiple PhD candidates, Moens actively contributes to academic training through courses including Reliability of Mechanical Systems, Dynamic Behavior of Machines, and Design for Safety and Reliability.

• Process Optimization
• Chemical Engineering
• Energy Engineering
• +۵ مورد دیگر

Dominik Bongartz is a tenure-track assistant professor at KU Leuven's Faculty of Engineering Sciences, Department of Chemical Engineering, where he joined in 2022. He serves as subdivision head of EnergyVille CReaS and is an active member of both the EnergyVille Division and KU Leuven Institute for Energy and Society (KIEM). His academic roles include membership in the Faculty Council of Engineering Sciences, Departmental Council for Chemical Engineering, and as observer of the POC Chemical Engineering Techniques. Dr. Bongartz's research focuses on developing optimization methods for process design and control in chemical and energy engineering. His work centers on accelerated solution methods for chemical problems and machine learning applications, with particular emphasis on process electrification including heat pumps, electrochemical reactors, and their integration in power-to-x process chains. He is a developer of the open-source optimization software MAiNGO, which has become an important tool in the field. His recent publications reveal a strong trend toward electrochemical systems optimization, with multiple papers on electrochemical reactors, hydrogen production technologies, and heat pump integration for sustainable energy systems. The research demonstrates sophisticated application of nonconvex optimization techniques to practical engineering problems in the energy transition space. Professionally, he serves as secretary of the European Committee for the Use of Computers in Chemical Engineering Education (EURECHA) and sits on the editorial board of Computers & Chemical Engineering. These roles highlight his leadership in advancing computational methods in chemical engineering education and research. Dr. Bongartz teaches multiple courses including Systems Analysis of Chemical Processes, Process Control in the Chemical Industry, Process Simulation in the Chemical Industry at the master's level, and Systems Theory and Control Theory at the bachelor's level. His teaching directly complements his research expertise in process optimization and control. His research portfolio includes numerous funded projects from 2022-2028, primarily focused on nonconvex optimization methods, electrochemical processes, and power-to-x systems. These projects demonstrate strong industry relevance and alignment with European energy transition goals.

Nira Dynمشاهده پروفایل

استاد

• Geometric Modeling
• Subdivision methods
• Multivariate approximation theory
• +۴ مورد دیگر

Nira Dyn is a Professor of Applied Mathematics at Tel Aviv University's School of Mathematics, where she has established herself as a leading researcher in geometric modeling and approximation theory. Her academic career spans decades of contributions to subdivision methods and computational mathematics, with a consistent focus on both theoretical foundations and practical applications in computer graphics and image processing. Research Interests Professor Dyn's primary research areas include Geometric Modeling , Subdivision methods , and Multivariate approximation theory , with significant contributions to Computer-Aided Geometric Design (CAGD) and Image Compression. Her current work centers on Nonlinear subdivision schemes and the Approximation of set-valued functions , representing cutting-edge extensions of classical approximation theory to handle complex geometric structures and uncertain data. These interests form a cohesive research program that bridges pure mathematical analysis with computational applications, particularly in handling geometric data through innovative subdivision techniques. Publication Trends Analysis of her recent publications reveals a strong emphasis on advancing subdivision methodologies beyond linear frameworks, with increasing focus on nonlinear schemes capable of handling complex geometries and set-valued data. Her work demonstrates consistent progression from foundational subdivision theory toward practical applications in image compression and geometric modeling, with notable contributions to metric-based approximation techniques. The publications showcase interdisciplinary reach spanning mathematics, computer science, and engineering applications, while maintaining rigorous mathematical foundations in approximation theory. Professional Activities While specific advising relationships and grant information aren't detailed in the available materials, Professor Dyn's extensive publication record in top-tier journals indicates active research leadership. Her collaborations span multiple institutions and disciplines, reflecting the interdisciplinary nature of modern geometric modeling research. The absence of explicit laboratory or team information suggests her work may be primarily theoretical or conducted through collaborative networks rather than a dedicated physical research space.