جستجوی استاد راهنمای دانشگاهی

Leentje Volkerمشاهده پروفایل

استاد

• Construction Management
• Asset Management
• Public Procurement
• +۷ مورد دیگر

Leentje Volker is a Full Professor (formal rank: Professor ) at Delft University of Technology specializing in Integrated Project Delivery. With a PhD in Design and Construction Management (2005-2010) and a Master in Applied Innovation Sciences from Eindhoven University of Technology (1996-2002), her career bridges engineering and social sciences. Education: PhD in Design & Construction Management (Delft, 2010), Master in Applied Innovation Sciences (Eindhoven, 2002) Her research focuses on innovation ecosystems , circular economy , and public procurement in infrastructure and construction contexts. She examines multidimensional value creation, institutional plurality, and digital transformation through serious games and empirical case studies. Key research trends include sustainability transitions in infrastructure, value tensions in collaborative innovation, and institutional barriers to circular economy adoption. Her work combines qualitative comparative analysis with fieldlab experiments. Scientific awards: ACROM Best Paper (2021), ARCOM Peter Townsend Award (2023), David Langford Award (2024), Les Plumes Des Achets (2023), Public Procurement (2023), Nevi Scriptieprijs (2024) Active in interorganizational governance and project-based learning , she serves on PhD committees and external councils. Media contributions cover topics like cost overruns in construction and human-centric infrastructure .

Aleida Braaksmaمشاهده پروفایل

مدرس

• Artificial Intelligence
• Health and Well-being
• Reinforcement Learning
• +۷ مورد دیگر

Aleida Braaksma is a Lecturer at the University of Twente, affiliated with the TechMed Centre and Mathematics of Operations Research department. Her work bridges Artificial Intelligence with Health and Well-being , focusing on optimizing healthcare systems through Operations Research methodologies. Key Affiliations: Digital Society Institute, TechMed Centre, Mathematics of Operations Research department Research Themes: Reinforcement Learning, Data Mining, Process Mining, and Queueing Theory applications in healthcare logistics Her recent publications highlight advancements in medical diagnostic scheduling , bed allocation , and adaptive clinical trial designs . She has pioneered dynamic robust optimization frameworks for time-sensitive pharmaceutical workflows and developed sampling-based methods for Gittins index approximation in stochastic environments. Scientific contributions include: Optimization of rheumatology outpatient clinics via patient classification algorithms Response-adaptive procedures in clinical trials using constrained Markov decision processes Real-time forecasting systems for pandemic-related hospital capacity planning Computerized decision support for nurse-to-patient assignment

Christoph Bruneمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Computer Vision
• +۷ مورد دیگر

Christoph Brune is a Full Professor at the University of Twente , affiliated with the Digital Society Institute and TechMed Centre . His research bridges Artificial Intelligence , Deep Learning , and Medical Imaging with applications in Robotics and Computational Fluid Dynamics . Keywords: Machine Learning , Network Analysis , Optimal Transport , Medical Image Segmentation Research Trends : Recent publications focus on physics-informed neural networks , equivariant learning for vascular modeling, and operator learning for PDE-constrained problems. His work emphasizes geometric deep learning , implicit neural representations , and robust AI for clinical settings. Scientific Awards : CMI-NEN seed-money award (2022) for medical imaging innovation Collaborative Networks : Active in graph neural networks , optimal transport , and Barron space theory . Key collaborations include Zenodo dataset contributions and ArXiv preprints with multidisciplinary teams in AI, medicine, and engineering.

Agata Leszkiewiczمشاهده پروفایل

استادیار

• Digital Marketing
• Customer Relationship Management
• Marketing Analytics
• +۷ مورد دیگر

Agata Leszkiewicz is Assistant Professor of Marketing at the University of Twente, Department of High-tech Business and Entrepreneurship. She specializes in marketing analytics, digital marketing, and customer-relationship management, employing machine-learning and econometric techniques to study customer churn, reacquisition, and marketing-channel optimization. Education: PhD (cum laude) in Business & Quantitative Methods, University Carlos III of Madrid – awarded the King Felipe VI Extraordinary Doctoral Dissertation Prize (2014) MSc in Business & Quantitative Methods, University Carlos III of Madrid MA in Quantitative Methods in Economics & Information Systems, Warsaw School of Economics Research interests centre on the intersection of big-data analytics and substantive marketing problems. She develops new statistical and machine-learning approaches to model customer lifetime value, design optimal experiments, and personalize digital interactions. Current themes include AI-enabled social commerce, cultural drivers of online purchase, and the societal value of AI in business. Her work appears in top-tier journals such as Journal of Marketing Research , Journal of Interactive Marketing , and Statistics and Computing , and has been presented at EMAC, INFORMS Marketing Science, AMA Winter Educators, and leading statistics symposia. Scientific awards: 2023 Emerald Literati Outstanding Author Contribution Award 2023 ISBM Doctoral Award Finalist (supervisor of nominated student) 2014 Premio Extraordinario de Doctorado, Kingdom of Spain Grant & advisory activity: She coordinates multiple marketing courses within the (I)BA bachelor, pre-master and master programmes at Twente, serves as associate editor of BRQ Business Research Quarterly , and reviews for Journal of Marketing , Journal of Marketing Research , European Journal of Marketing , among others. She is chair of the European Marketing Academy (EMAC) network. External collaboration: Since 2018 she is visiting scholar at the Center for Excellence in Brand & Customer Management, Georgia State University, and previously conducted doctoral research at Columbia Business School.

Fabian Akkermanمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Autonomous Agent Systems
• +۷ مورد دیگر

Fabian Akkerman is a researcher at the Digital Society Institute within the Industrial Engineering & Business Information Systems department at the University of Twente . His work bridges theoretical advancements in machine learning with practical applications in logistics, energy sustainability, and transportation systems. Primary Affiliation : University of Twente, Industrial Engineering & Business Information Systems Research Focus : Artificial Intelligence, Reinforcement Learning, Autonomous Systems, and Sustainable Logistics Fabian's research specializes in sequential decision-making problems, particularly in dynamic and stochastic environments. A key area of his contributions lies in applying reinforcement learning and optimization techniques to address challenges in: Vehicle routing with uncertain demand Inventory management and warehouse operations Time slot pricing for delivery services Smart transportation and freight logistics Stochastic modeling for supply chain resilience Industry 4.0 adoption in production systems His work demonstrates a strong emphasis on developing algorithmic frameworks (e.g., DynaPlex) that combine statistical modeling with real-world implementation. Recent publications highlight applications in autonomous vehicles, intelligent transport systems, and circular economy strategies. Scientific Awards : 2024 Transportation Science Meritorious Service Award 2025 2nd Place in ISIR Research Challenge for Production-Inventory Planning at ASML

• Aircraft Maintenance
• Airline Operations
• Predictive Maintenance
• +۵ مورد دیگر

Bruno F. Santos is an Assistant Professor in Airline Operations at Delft University of Technology's Faculty of Aerospace Engineering. His research bridges theoretical optimization methods with practical airline operations challenges, focusing on making aviation systems more efficient and sustainable through data-driven approaches. Dr. Santos's research interests center around aircraft maintenance optimization , predictive maintenance using AI , stochastic modeling for airline operations , and strategic planning for sustainable aviation . His work applies advanced computational techniques including Bayesian frameworks and deep reinforcement learning to solve real-world problems in the aviation industry. A key focus is transforming traditional fixed-schedule maintenance into condition-based approaches that respond to actual aircraft system health. His recent publications demonstrate a strong trajectory in high-impact journals, with a focus on counterfactual explanations for remaining useful life estimation, deep reinforcement learning for airport operations, and multidisciplinary coupling for hybrid-electric aircraft design. These works collectively address the critical challenge of making aviation more efficient, sustainable, and cost-effective through digital transformation. TRA VISIONS 2022 Senior Researcher Award Airborne AIAA Electrified Aircraft Technology Technical Committee Best Paper Award (2023) AIAA Software Best Paper Award (2024) Dr. Santos leads significant research initiatives including the €6.8 million ReMAP project, which successfully demonstrated through a six-month trial at KLM that AI models can predict aircraft system health and optimize maintenance scheduling. This project involved collaboration with multiple European universities and industry partners. His editorial roles for Transportation Research Procedia and Transport Policy demonstrate his standing in the academic community. Dr. Santos actively translates research into practical applications, with media coverage highlighting how his work can save airlines hundreds of millions while improving operational efficiency. His Aircraft Maintenance and Operations Research Group focuses on developing adaptive maintenance planning systems that use real-time data to optimize maintenance scheduling, reducing unnecessary maintenance while preventing system failures. The group's work represents a significant shift from traditional fixed-schedule maintenance to condition-based approaches that respond to actual aircraft system health.

N.J.J. Verdonschotمشاهده پروفایل

استاد

• Orthopaedic Biomechanics
• Implant Biomechanics
• Finite Element Modeling
• +۵ مورد دیگر

N.J.J. Verdonschot holds the following academic positions: Full Professor at the Radboudumc Institute for Health Sciences (RIHS) since February 2014, focusing on 'Reconstructive and regenerative medicine'. Part-time Professor in Implant Biomechanics at the University of Twente (1 day per week) since October 2007. Visiting Professor at the Politecnico di Milano since 2016. His educational background includes: Master's thesis at the Orthopaedic Research Laboratory (ORL), Nijmegen (1988). PhD from Radboud University (awarded in 1995). Verdonschot's research focuses on orthopaedic-biomechanical problems of the lower extremity. His group utilizes computer modeling, cadaveric testing, animal research, and clinical studies to optimize diagnostic tools and orthopaedic implants, particularly knee and hip prostheses. Additionally, his work addresses bone strength calculation for patients with osteoporosis or metastatic bone lesions. His notable scientific awards include: European Research Council Advanced Grant (2012). Honorary Doctorate from Aalborg University, Denmark (2018). Verdonschot has secured significant research funding, including the ERC Advanced Grant which supports the ERC-Biomechtools project (see www.erc-biomechtools.eu ). He also leads the TLEMsafe research line at the University of Twente ( www.tlemsafe.eu ), focusing on musculoskeletal biomechanics. He has supervised numerous students and maintains strong industry collaborations with orthopaedic companies worldwide. His research is conducted within the Orthopaedic Research Laboratory in Nijmegen and in collaboration with the Biomechanical Engineering department at the University of Twente.

Sandjai Bhulaiمشاهده پروفایل

پژوهشگر

• Machine Learning
• Artificial Intelligence
• Optimization
• +۴ مورد دیگر

Dr. Sandjai Bhulai is a Researcher at Centrum Wiskunde & Informatica (CWI) in Amsterdam, The Netherlands, and affiliated with Vrije Universiteit Amsterdam. He works in the Stochastics department within the Faculty of Science, focusing on applying machine learning and optimization techniques to solve complex real-world problems across diverse domains including telecommunications, mental health crisis intervention, and operations research. His research spans multiple critical areas of applied mathematics and computer science: Development of novel machine learning evaluation frameworks (Dutch Draw, Dutch Scaler) Optimization algorithms for facility location and supply chain problems Telecommunications signal processing using deep learning Mental health applications, particularly suicide prevention helpline analysis Natural language processing for conversation analysis in crisis intervention Dr. Bhulai's recent work demonstrates exceptional interdisciplinary impact. His Dutch Draw framework provides a universal baseline for binary classification problems that has been adopted across multiple research communities. His mental health research has directly influenced suicide prevention practices through machine learning analysis of helpline conversations, identifying specific counselor interventions that improve outcomes. In telecommunications, he's developed neural network approaches that simultaneously handle multiple modulation schemes, improving efficiency in wireless communications. His scientific contributions appear in leading venues including: Computers and Operations Research Journal of Classification IEEE Open Journal of the Communications Society JMIR Mental Health Journal of Applied Probability Dr. Bhulai maintains strong collaborative relationships with researchers across institutions, particularly with Rob van der Mei at CWI. He has secured funding for impactful projects including the collaboration between Stichting 113 Suicide Prevention and CWI, which applies machine learning to improve crisis intervention services. His work exemplifies how theoretical advances in machine learning can address critical societal challenges.

Gang Meiمشاهده پروفایل

دانشیار

• Numerical Simulation and Computational Modeling
• GPU Computing
• Machine Learning
• +۵ مورد دیگر

Dr. Gang Mei is an Associate Professor in Scientific Computing within the School of Engineering and Technology at China University of Geosciences (Beijing), where he has held academic positions since 2014. His career progression includes Postdoctoral Researcher (2014-2016), Lecturer (Oct-Dec 2016), and current Associate Professor (since Jan 2017). His research bridges computational science and engineering applications with significant editorial contributions to computer science literature. Education: Ph.D. in Computer Science, University of Freiburg, Germany (2014) Research Interests: Dr. Mei specializes in Numerical Simulation and Computational Modeling, GPU Computing, Machine Learning, and Data Mining, with strong applications in Network Science and Spatial Information Systems. His work integrates Distributed and Parallel Computing techniques for large-scale scientific simulations, particularly in geospatial modeling and network analysis. The research demonstrates consistent focus on computational efficiency through hardware acceleration and algorithmic optimization across diverse domains including satellite imagery processing, financial event detection, and medical image classification. Publication Trends: His editorial portfolio reveals strong interdisciplinary patterns connecting computer science fundamentals with domain-specific applications. Recent works emphasize GPU-accelerated methods for data-intensive problems (2020-2022), spatial-temporal modeling (2019-2020), and network science applications (2021). The publications consistently address computational scalability challenges while maintaining practical relevance across geospatial, financial, medical, and engineering contexts. Professional Recognition: As an IEEE Member, Dr. Mei serves on editorial boards for IEEE Access and PeerJ Computer Science, reflecting peer recognition in computational fields. His editorial contributions span 15+ publications demonstrating expertise in evaluating cutting-edge computer science research. Academic Service: Beyond editorial work, Dr. Mei's service includes advising on computational methodology across multiple disciplines. His role as Academic Editor demonstrates commitment to scholarly communication, particularly in bridging theoretical computer science with practical engineering applications. No grant funding details were specified in available materials.

Thomas Rothvossمشاهده پروفایل

استاد

• Theoretical Computer Science
• Discrete Mathematics
• Approximation Algorithms
• +۳ مورد دیگر

Thomas Rothvoss is a Professor at the University of Washington with a joint appointment in the Mathematics and Computer Science departments, conducting pioneering research at the intersection of theoretical computer science and discrete mathematics. His work has fundamentally advanced multiple subfields through breakthrough algorithmic developments. Rothvoss is renowned for solving longstanding problems including exponential lower bounds for extension complexity of combinatorial polytopes, optimal approximation algorithms for Steiner tree and bin packing, and efficient discrepancy minimization techniques. His recent focus on integer programming culminated in the first major running-time improvement in over 30 years, resolving a conjecture by Kannan and Lovász that had stood since 1988. His publication trajectory demonstrates consistent innovation in leveraging geometric and number-theoretic methods to solve NP-hard optimization problems, with recent work bridging lattice theory and algorithmic design to achieve breakthrough complexity results in integer programming. Professor Rothvoss has received top honors in theoretical computer science: Delbert Ray Fulkerson Prize (2018) Gödel Prize (2023) Best Paper Award at FOCS 2023 While specific advising relationships aren't detailed in available sources, his sabbatical collaborations—like the May-June 2025 engagement with CWI's Networks & Optimization group—reflect active mentorship within international research communities. His work continues to attract significant funding, evidenced by support for extended research visits from institutions like CWI. Rothvoss maintains strong ties with European research groups, particularly through his ongoing collaboration with CWI's Networks & Optimization team during sabbatical periods, where he develops lattice-based approaches to combinatorial optimization problems.

• Marine Biology
• Ecology
• Genetics
• +۷ مورد دیگر

Robert J Toonen is a Research Professor of Marine Biology at the Hawaii Institute of Marine Biology within the School of Ocean & Earth Sciences & Technology at the University of Hawaii at Manoa. His academic career spans multiple institutions, with previous affiliation at the University of Texas at Brownsville, demonstrating his commitment to marine science research across different geographical contexts. Dr. Toonen's research interests encompass a wide range of marine science disciplines including Marine Biology, Ecology, Genetics, Genomics, Conservation Biology, Biodiversity, Zoology, Biological Oceanography, Evolutionary Studies, and Aquaculture. His work consistently bridges molecular techniques with ecological field studies to address critical questions in marine biodiversity and conservation. A distinctive feature of his research approach is the integration of genomic methodologies with traditional ecological studies to solve complex marine conservation problems. Analysis of Dr. Toonen's publication record reveals a strong emphasis on coral reef ecosystems, marine conservation genetics, and biodiversity assessment. His research spans from fundamental taxonomic work identifying new marine species to applied conservation efforts addressing coral bleaching, reef restoration, and fisheries management. A notable trend across his publications is the interdisciplinary nature of his work, which combines molecular biology, ecology, and conservation science to tackle pressing environmental challenges in marine ecosystems, particularly in the Pacific region. Dr. Toonen serves as an Editor for PeerJ, the Journal of Life & Environmental Sciences, where he has edited over 130 articles, reflecting his broad expertise across multiple areas of marine science. His editorial contributions complement his substantial research output, which includes over 33 published articles and 2 preprints. Dr. Toonen leads the ToBo Lab at the Hawaii Institute of Marine Biology, which focuses on marine genomics, coral reef ecology, and conservation biology. The lab employs advanced molecular techniques to investigate marine biodiversity, population connectivity, and conservation genetics, with particular emphasis on Hawaiian marine ecosystems and their global connections. The lab's work addresses critical issues related to climate change impacts on coral reefs, species conservation, and sustainable management of marine resources.

• Offshore Wind Energy
• Geotechnical Engineering
• Wind Turbine Foundations
• +۴ مورد دیگر

T. Raaijmakers is a Researcher at Delft University of Technology's Faculty of Aerospace Engineering and the TU Delft Wind Energy Institute (DUWIND), specializing in offshore wind energy systems engineering. His work bridges academic research with industrial applications through multiple funded projects addressing critical infrastructure challenges. His research spans geotechnical engineering for wind turbine foundations, cable systems optimization, composite materials development, corrosion fatigue mitigation, and environmental noise reduction. Key focus areas include preventing soil liquefaction in gravity-based foundations, optimizing cable routes in dynamic seabed environments, and developing smart composite joints for turbine structures using advanced materials science. Dr. Raaijmakers maintains an active research portfolio with current projects including CompoSmart (2025-2028) on composite joints and Bolt & Beautiful (2022-2026) on bolt manufacturing innovation. Previously, he contributed to BUBBLES JIP for noise reduction, DOT3000 PTS for power train systems, and C-FLO for corrosion fatigue optimization, demonstrating consistent success in securing competitive research funding. Within the DUWIND institute, he collaborates across interdisciplinary teams to solve complex engineering problems in offshore wind deployment. His approach emphasizes practical solutions for industry implementation, particularly in structural integrity and environmental sustainability of offshore wind farms.

• Acoustics
• Noise Measurement
• Microphone Array Technology
• +۵ مورد دیگر

Pieter Sijtsma serves as a Professor in the Department of Operations & Environment within Delft University of Technology's Faculty of Aerospace Engineering. His academic career spans over 15 years of specialized research in aeroacoustics, with significant contributions to microphone array technology and noise measurement methodologies. Current affiliations include active roles in the Berlin Beamforming Conference Committee and ongoing collaborations with major aerospace research entities. Professor Sijtsma's research focuses on advanced acoustic measurement techniques for aerospace applications. His work centers on beamforming algorithms , engine noise characterization , and wave propagation modeling in complex environments like wind tunnels and annular ducts. Key specialties include CLEAN-SC deconvolution methods for noise source mapping, directivity pattern analysis, and experimental validation of acoustic assessment techniques. His fingerprint reveals dominant expertise in acoustics (100%), noise measurement (97%), and microphone array technology (68%). Recent publication trends show concentrated activity in improving acoustic field assessment (2025), engine noise directivity determination (2024), and beamforming in swirling flows (2023). His work consistently addresses practical aerospace noise challenges through rigorous experimental validation and mathematical modeling, with strong emphasis on measurement accuracy and algorithmic innovation. Professor Sijtsma actively supervises research through collaborative projects, typically co-mentoring students with colleagues like Snellen and Avallone. His group maintains strong industry connections, evidenced by committee roles with the Ministry of Infrastructure and Environment and participation in international standard-setting conferences. Current projects involve EU-funded research on wind tunnel acoustic correction methods and engine noise source characterization. The research infrastructure leverages TU Delft's advanced wind tunnel facilities with specialized acoustic treatment and high-channel-count microphone arrays. Recent dataset releases confirm access to sophisticated wave propagation modeling tools and experimental validation platforms for closed-test-section environments.