جست‌وجوی استاد و پژوهشگر

• Algorithms
• Optimization
• Combinatorial Optimization
• +۳ مورد دیگر

Bodo Manthey is an Associate Professor in the Department of Mathematics of Operations Research and the Digital Society Institute. His research focuses on algorithms, combinatorial optimization, and computational complexity. He has contributed to areas such as the Traveling Salesman Problem (TSP), clustering algorithms, and smoothed analysis of heuristics. His work bridges theoretical computer science and operations research, with a strong emphasis on algorithm design, approximation algorithms, and probabilistic analysis. Key research interests include the analysis of local search algorithms, approximation inefficiencies in optimization heuristics, and probabilistic models for algorithm performance. He has published extensively on topics like k-means clustering, TSP variants, and the application of smoothed analysis to understand algorithm behavior under realistic conditions. Manthey has organized multiple conferences and workshops, including the Cologne-Twente Workshop on Graphs and Combinatorial Optimization. His contributions to the field are reflected in over 100 research outputs, with recent work addressing the complexity of Euclidean clustering and the effectiveness of ant colony algorithms. His academic activities span editorial roles, committee memberships, and invited talks on topics such as random metrics in algorithm analysis. Despite the absence of explicitly listed awards, his prolific output and conference involvement highlight his significant contributions to theoretical computer science and operations research.

Sander Oude Elberinkمشاهده پروفایل

دانشیار

• 3D Reconstruction
• Point Cloud Analysis
• Laser Scanning
• +۶ مورد دیگر

Sander Oude Elberink is an Associate Professor at the Department of Earth Observation Science, International Institute for Geo-Information Science and Earth Observation (ITC), University of Twente. He is also affiliated with the Digital Society Institute. He holds a PhD from ITC (2010) and a Geodetic Engineering degree from Delft University of Technology (2000). His research focuses on information extraction from point clouds and 3D reconstruction using fused topographic maps and laser data. Education: PhD: 'Acquisition of 3D Topography' (ITC, 2010) Master's: Geodetic Engineering (Delft University of Technology, 2000) Research Interests: His work spans 3D point cloud analysis , LiDAR technology , UAV-based remote sensing , and digital twin development . Recent efforts include semantic segmentation algorithms for urban modeling and energy-related infrastructure analysis. His research bridges geospatial data science with practical applications in urban planning and environmental monitoring. Notable Projects: 3D BGT tool development for hydrological applications 3DTOP10NL national 3D topography model collaboration Capacity-building initiatives in India (IIRS joint program) Awards: ISPRS Giuseppi Inghilleri Award (2016), ITC Research Award (2009), and ISPRS Young Author Award (2008). Advising: Supervised over 25 master’s students and co-promoted PhD candidates including Biao Xiong, Sudan Xu, and Amrollah Seifoddini. Current projects involve UAVPal dataset development and semantic segmentation in complex urban environments. Labs & Teams: Active in the ITC research group, contributing to UN SDG initiatives related to sustainable cities (SDG 11) and responsible consumption (SDG 12) through 3D modeling innovations.

• Agricultural Robotics
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Gert Kootstra is an Associate Professor in Agricultural Biosystems Engineering at Wageningen University & Research. His research focuses on robotics, computer vision, and machine learning applied to agricultural challenges, including plant phenotyping, UAV path planning, and automated harvesting systems. He leads projects involving 3D plant reconstruction, multi-object tracking in greenhouses, and deep learning for agricultural automation. His work bridges theoretical advancements with practical applications in precision agriculture and autonomous agricultural systems. Education: Not explicitly stated in the text, but his academic rank implies advanced qualifications in agricultural engineering or robotics. Research Interests: Robotics, computer vision, deep learning, UAV-based monitoring, plant phenotyping, and autonomous systems for agriculture. His projects often involve collaboration with industry and other academic institutions to solve real-world agricultural challenges. Recent Trends in Articles: Recent publications emphasize 3D plant segmentation, UAV navigation, and machine learning for object tracking in complex environments. He explores adaptive path planning for drones, automated harvesting techniques using learning from demonstration, and frameworks for uncertainty-aware object assessment in uncontrolled settings. Advising & Grants: Supervises multiple PhD candidates (e.g., Yuan J., Cakaj H., Versmissen T.) in projects funded by grants related to plant phenotyping, UAV applications, and robotics. Collaborates on initiatives like the PicknPack project for automated food processing. Labs & Teams: Involved in robotics and agricultural technology groups at Wageningen, focusing on developing tools for precision agriculture and sustainable farming practices.

Simon Hengeveldمشاهده پروفایل

پژوهشگر

• Distance Geometry
• Computational Geometry
• Bioinformatics
• +۳ مورد دیگر

Simon Hengeveld is a Researcher and Software Developer for Health Research at Wageningen University. He holds a PhD from Université de Rennes (2024), and bachelor's/master's degrees in Computer Science from Utrecht University. His research focuses on computational geometry, protein structure determination via Distance Geometry, and human motion analysis. He has developed applications like the UNESCO World Heritage Collect app, combining software engineering with historical and travel interests. Education: Bachelor's/Master's in Computer Science, Utrecht University PhD in Structural Biology/Computational Geometry, Université de Rennes 1 Research Interests: His work bridges computational methods with biological applications, including protein structure determination using NMR data and optical computing. He also explores human motion retargeting and geometric algorithms for adaptive maps. Simon emphasizes practical implementations, such as GPU-based solutions for 1D Distance Geometry problems. Publications: His articles span computational geometry, bioinformatics, and algorithm design, with contributions to journals like Journal of Optics and conferences such as SOCG, GSI, and BIBM. Key themes include optical processors for matrix operations and geometric algorithms for skeletal posture analysis. Awards: No scientific awards explicitly mentioned. Advising/Grants: No formal advising roles or grants detailed in the text. Labs/Teams: Developed the UNESCO World Heritage Collect app with collaborators, though no formal university lab affiliation is noted.

• Craniofacial Disorders
• Dental Health
• Pediatric Surgery
• +۳ مورد دیگر

Prof. Eppo Wolvius is a Full Professor of Oral and Maxillofacial Surgery at Erasmus University Medical Center. His research focuses on craniofacial disorders, including craniofacial synostosis, dental caries, cleft lip and palate, and Pierre Robin Syndrome. He leads studies on surgical techniques, AI in medical imaging, and preclinical surgical outcomes. Recent work includes case series on silent sinus syndrome and advancements in 3D surgical navigation using augmented reality. With 238+ research outputs, his contributions span clinical insights, therapeutic strategies, and technological innovations in craniofacial surgery. He has supervised 26 academic works, emphasizing interdisciplinary collaboration in pediatric and reconstructive surgery.

Esther Bronمشاهده پروفایل

استادیار

• Alzheimer's Disease
• Medical Imaging
• Cerebrovascular Disease
• +۳ مورد دیگر

Dr. Esther Bron is an Assistant Professor at Erasmus MC's Department of Radiology & Nuclear Medicine. Her research focuses on neuroimaging applications in Alzheimer's disease, cerebrovascular disorders, and the integration of artificial intelligence in medical diagnostics. She has led collaborative projects such as the EUCAIM consortium for cancer imaging infrastructure and the Netherlands consortium of dementia cohorts. Research Interests: Alzheimer's Disease Mechanisms Cerebrovascular Pathophysiology AI-driven Medical Image Analysis Cognitive Dysfunction Imaging Recent work emphasizes translating neuroimaging findings into clinical practice, particularly in cardiovascular-neurological interfaces and dementia prevention strategies. Her articles explore PET/MRI techniques, confounder correction algorithms, and multi-center data integration challenges.

Jan H.M. ten Thije Boonkkampمشاهده پروفایل

دانشیار

• Numerical Analysis
• Partial Differential Equations (PDEs)
• Finite Volume Methods
• +۱۰ مورد دیگر

Jan H.M. ten Thije Boonkkamp is an Associate Professor at Eindhoven University of Technology (TU/e), affiliated with the Intelligent Lighting Institute and the Department of Mathematics and Computer Science. His work bridges mathematical theory and applied computational sciences. Academic Background: MSc in Applied Mathematics (University of Twente, 1984) Current Roles: Associate Professor, Project Manager for EU-funded initiatives Key Collaborations: IIT Kanpur (2015 Visiting Professor), Philips Research Laboratories His research focuses on numerical methods for partial differential equations (PDEs) with applications in computational illumination optics and conservation laws . He develops: Freeform optical systems using optimal transport theory Complete flux schemes for hyperbolic systems Nonlinear PDE models for refracting surfaces Least-squares solvers for complex geometrical optics Recent publications (2025) highlight his interdisciplinary contributions to: Novel optical design frameworks combining Hamiltonian theory and optimal transport Advanced flux approximation schemes for fluid dynamics Computational optics solutions for energy-efficient lighting He leads projects like: MALIOD (Machine Learning for Illumination Optics Design, 2021-2026) PhotoLitho (Aberration compensation, 2020-2027) Intelligent Lighting (3rd tier, 2020-2025) His work contributes to UN Sustainable Development Goals through energy-efficient optical technologies.

Eric Nalisnickمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Probabilistic Modeling
• +۵ مورد دیگر

Eric Nalisnick is an Assistant Professor in the Department of Computer Science at Johns Hopkins University. He holds affiliations with the Institute for Assured Autonomy, Mathematical Institute for Data Science, and Data Science and AI Institute. His research focuses on developing safe and robust intelligent systems through probabilistic modeling and computational statistics, with applications in healthcare, online content moderation, and sign language processing. His work emphasizes human-centered design, exploring how to incorporate prior knowledge, detect system failures, and integrate human-machine decision-making. Research Trends: Recent publications highlight advancements in uncertainty quantification (e.g., Lightning UQ Box tool), early-exiting neural networks for risk control, and generative models for symmetry transformations. He also investigates calibration in multi-distribution learning and ethical considerations in hate speech detection systems. Advising & Contributions: Eric mentors a diverse cohort of PhD students and has developed influential open-source tools like Lightning UQ Box and Learning to Defer frameworks. His teaching includes courses on Deep Learning, Human-in-the-Loop Machine Learning, and Bayesian Methods.

• Algorithm Engineering
• Computational Geometry
• Spatial Networks
• +۵ مورد دیگر

Kevin Buchin is a Professor in the Faculty of Computer Science at TU Dortmund University, where he leads Lehrstuhl XI: Algorithm Engineering. His work focuses on the design and analysis of efficient algorithms, particularly in computational and geometric domains. His research interests include Algorithm Engineering , Computational Geometry , Spatial Networks , and Algorithms for GIS and Motion Planning . These areas reflect a strong emphasis on both theoretical foundations and practical applications in data-intensive systems involving spatial and movement data. The absence of listed publications in the provided text prevents a trend analysis of recent scholarly output. However, his research profile suggests consistent contributions to algorithmic problem-solving in geometric and spatial contexts. There are no scientific awards listed in the provided information. There is no information available about student advising or research grants. There is also no explicit mention of leadership in labs or research teams beyond his role as head of Lehrstuhl XI: Algorithm Engineering, which implies leadership in a dedicated research group focused on algorithm engineering.

• Machine Learning
• Natural Language Processing
• Language Modelling
• +۷ مورد دیگر

Wilker Ferreira Aziz is an Assistant Professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam, where he leads the Probabilistic Language Learning group. His primary affiliation is with the Natural Language Processing & Digital Humanities research unit. His research focuses on the intersection of machine learning, natural language processing, and probabilistic modeling. Key areas of interest include language modeling, machine translation, syntactic parsing, text classification, and question answering. He develops techniques for probabilistic inference, gradient estimation, and uncertainty quantification in neural language models. Dr. Aziz's recent publications demonstrate a strong focus on uncertainty in natural language generation, with multiple papers at top-tier conferences like EACL, EMNLP, and ICLR. His work examines how language models represent uncertainty compared to humans, calibration issues when humans disagree on labels, and methods for more robust decision-making in text generation. Best Paper Award at Coling 2020 He actively supervises both PhD and MSc students, with several ongoing PhD projects focusing on uncertainty in language models and neural text generation. Dr. Aziz serves on program committees for major ML and NLP conferences including ACL, EMNLP, NeurIPS, and ICLR, and has acted as area chair for several of these venues. His research has been supported through positions at the Mercury Machine Learning Lab, a collaboration between Booking.com, TU Delft, and the University of Amsterdam.

Lex Schrijverمشاهده پروفایل

استاد

• Discrete Mathematics
• Optimization
• Algorithms
• +۳ مورد دیگر

Lex Schrijver is a Professor of Mathematics at the University of Amsterdam since 1990 and a CWI-Fellow at Centrum voor Wiskunde en Informatica (CWI) since 1989. His research focuses on discrete mathematics, optimization, and algorithms, with significant contributions to graph theory, combinatorics, and coding theory. Research Interests: Discrete Mathematics, Optimization, Algorithms, Graph Theory, Combinatorics, Coding Theory. Awards: EURO Gold Medal (2015) Honorary Doctorate from Eotvos Lorand University (2011) Franz Edelman Award (2008) SIGMA Prize (2008) John von Neumann Theory Prize (2006) Spinoza Prijs (2005) Ridder in de orde van de Nederlandse Leeuw (2005) Lanchester Prize (2004, 1986) Fulkerson Prize (2003, 1982) Van Dantzig Prize - SIAM / MPS (2003) Eredoctoraat from University of Waterloo (2002) Projects: Involved in the Networks project, funded by the Netherlands Organisation for Scientific Research (NWO). Professional Activities: Fellow of the American Mathematical Society (AMS) and SIAM.

Rob Eggermontمشاهده پروفایل

استادیار

• Algebraic Geometry
• Discrete Mathematics
• Polynomial Representations
• +۵ مورد دیگر

Rob Eggermont is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), specializing in the Discrete Algebra and Geometry research group. His work focuses on finiteness properties in infinite-dimensional algebraic settings with symmetry, connecting algebraic geometry, representation theory, and combinatorics. His research has significant applications in fields including phylogenetics, chemistry, and algebraic statistics. Education: MSc in Mathematics from Leiden University (2011), cum laude PhD from Eindhoven University of Technology (2015), cum laude Rob Eggermont's research centers on how algebraic structures with symmetry maintain finiteness properties even in infinite-dimensional settings. His work on topological noetherianity demonstrates that in systems with sufficient symmetry, problems involving millions of variables can be as tractable as those with only a few variables. This has profound implications for computational algebra and geometric modeling. His research group plays a leading role in algebraic graph theory, finite and incidence geometry, and discrete Lie theory. An analysis of his recent publications (2017-2025) reveals a consistent focus on polynomial representations, GL-varieties, and symmetric matrix structures. His work frequently explores how finiteness properties emerge in infinite-dimensional settings through symmetry constraints. Collaborations with Jan Draisma and other mathematicians form a significant portion of his output, indicating strong research networks in algebraic geometry and representation theory. Scientific Awards: Jong Talent Aanmoedigingsprijs (2007) - awarded to the best first-year mathematics student at Leiden University Veni grant 016.Veni.192.113 from the Netherlands Organization for Scientific Research (NWO) Dr. Eggermont has secured research funding through competitive grants and has participated in numerous international workshops and conferences, including the AIM workshop on 'Representation stability' (San Jose, 2016) and the BIRS workshop on 'Free Resolutions, Representations, and Asymptotic Algebra' (Banff, 2016). His research group actively contributes to the DIAMANT symposium and the Intercity Number Theory Seminar. Within the Discrete Algebra and Geometry group at TU/e, Eggermont collaborates with colleagues on advancing the theoretical foundations of algebraic structures with symmetry. His work has helped establish connections between representation theory, combinatorics, and practical applications in scientific computing.

Mireille Boutinمشاهده پروفایل

استاد

• Applied Mathematics
• Signal Processing
• Machine Learning
• +۵ مورد دیگر

Mireille Boutin is a Professor at the Eindhoven University of Technology , affiliated with the Department of Mathematics and Computer Science within the Discrete Algebra and Geometry group. Her research spans applied mathematics, signal processing, and machine learning, with a focus on discrete inverse problems and the integration of geometric tools in probabilistic frameworks. Academic Background : She earned a Ph.D. in Mathematics from the University of Minnesota under Peter Olver, followed by postdoctoral positions at Brown University and the Max Planck Institute for Mathematics in the Sciences . Before joining TU/e, she served on the faculty at Purdue University . Research Trends : Recent publications demonstrate her interdisciplinary approach, including solutions to global positioning uniqueness problems via algebraic geometry, computer-aided qualitative data analysis in education, symmetry-breaking in acoustic path tracking, and novel clustering models for high-dimensional data. Her work often bridges theoretical mathematics with practical applications in engineering and education. Educational Contributions : She actively teaches Computational Algebra at TU/e and has collaborated on studies applying machine learning to qualitative education research, such as analyzing student explanations in programming and faculty teaching conceptions.

• Mathematics Education
• Geometry
• Algebra
• +۳ مورد دیگر

Martin Kindt is a Lecturer in Mathematics Education at the Freudenthal Institute, Faculty of Science, Utrecht University. He has maintained an active presence in mathematics education research and practice since at least 2001, with particular focus on geometry, algebra, and historical perspectives in mathematics teaching. His research interests include: Geometry and spatial reasoning, particularly symmetry and regularity in plane and space Algebraic thinking and conceptual understanding pathways History of mathematics and its application to modern educational practices Integration of geometry and algebra in teaching frameworks Real-world mathematical applications, especially in engineering contexts Curriculum development for secondary mathematics education Kindt's scholarly work demonstrates a distinctive approach that connects historical mathematical developments with contemporary educational challenges. His presentations on figures like Albrecht Dürer and ancient systems like Babylonian mathematics reveal a consistent theme of using historical context to illuminate modern teaching practices. The progression of his work shows increasing sophistication in connecting geometric visualization with algebraic structures, as evidenced by titles like 'Geometry and Algebra, a Happy Marriage' and 'Regularity in Plane and Space.' His international engagement is notable, with presentations delivered across multiple continents including Asia, South America, and Europe. This global perspective informs his contributions to the field of mathematics education, particularly through the lens of Realistic Mathematics Education, which originated in the Netherlands.

E. Eisemannمشاهده پروفایل

استاد

• Computer Science
• Computer Graphics
• Virtual Reality
• +۴ مورد دیگر

E. Eisemann is a Professor at the Computer Graphics and Visualisation department within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology (TU Delft). Their research focuses on computer graphics, virtual reality, and 3D modeling, with recent publications addressing ray-box intersections, neural scene representations, and interactive modeling systems. Current research trends include Advancements in real-time rendering algorithms Applications of neural networks for 3D scene reconstruction Innovative approaches to human-computer interaction in VR environments Optimization techniques for large-scale environment rendering Research output spans 164 publications, with recent work appearing in venues like Computer Graphics Forum and ACM SIGGRAPH conferences. Supervised work includes 18 formal advisees.