جست‌وجوی استاد و پژوهشگر

• Natural Language Processing
• Applied Data Science
• Computational Linguistics
• +۳ مورد دیگر

Dr. Pablo Mosteiro Romero is an Assistant Professor at Utrecht University's Faculty of Social and Behavioural Sciences within the Department of Methodology and Statistics . His work bridges Natural Language Processing (NLP) , Applied Data Science , and Human-centered Artificial Intelligence , focusing on language change, morphology-syntax trade-offs, and applications in healthcare and open societies. Education: B.Sc. in Physics, Rutgers University (Summa Cum Laude, 2007) Ph.D. in Computer Science, Princeton University (2014, Centennial Fellowship) Research Interests revolve around computational linguistics, information-theoretic approaches to linguistics, and ethical AI. He investigates synonym evolution, morphosyntactic interactions, and fairness in mental health-focused AI systems. Recent work includes de-identification in medical texts and taxonomy induction via reinforcement learning. Publications span top venues like ACL, IEEE, and Springer, covering topics such as topic modeling interpretability, bias discovery in ML, and multimodal negation processing. Collaborations with colleagues like Scheepers, Spruit, and Kaymak highlight interdisciplinary applications in electronic health records. Awards include the Centennial Fellowship at Princeton and Summa Cum Laude at Rutgers. As a University Teaching Qualification holder, he teaches courses in data science, Python programming, and research methods, supervising theses in AI and Business Informatics. His work aligns with Utrecht's Institutions for Open Societies (IOS) theme, addressing gender diversity and global justice in data-driven systems.

• Proton Therapy
• Head and Neck Cancer
• Oropharynx Carcinoma
• +۳ مورد دیگر

Wens Kong is a Researcher in the Radiotherapy department at Erasmus University Medical Center, specializing in advanced proton therapy techniques. Their work focuses on optimizing treatment planning for head and neck cancers through computational innovations. Research interests center on Proton Therapy (100% fingerprint match), with significant contributions to Head and Neck Cancer (35%), Oropharynx Carcinoma (24%), and Treatment Planning (19%). Key methodologies include Bragg Peak engineering, DNA template modeling, and reduction of xerostomia through precision beam targeting. Current work emphasizes automated optimization systems for improving dose distribution while minimizing side effects. Recent publications demonstrate a strong trend toward fully-automated multi-criterial optimization systems, particularly for oropharyngeal cancer treatment. Research consistently addresses lateral dose penumbra reduction, robust treatment planning validation, and sparsity-induced computational acceleration - all aimed at enhancing proton therapy precision while maintaining clinical efficiency. Collaborations include prominent researchers at Erasmus MC: Huiskes M., Habraken S.J.M., Astreinidou E., Rasch C.R.N., Heijmen B.J.M., and Breedveld S. The research network shows significant international collaboration in medical physics and radiation oncology.

Marcel Bogersمشاهده پروفایل

استاد

• Open Innovation
• Collaborative Innovation
• Innovation Ecosystems
• +۶ مورد دیگر

Marcel Bogers is a Full Professor of Open & Collaborative Innovation at the Department of Industrial Engineering & Innovation Sciences (IE&IS) at Eindhoven University of Technology (TU/e) and an Affiliated Professor at the University of Copenhagen. He is also a Garwood Research Fellow at UC Berkeley. His research spans technology, innovation, and entrepreneurship, focusing on open innovation, business models, and ecosystem dynamics. He has contributed to fields like sustainability, digital innovation, and university-industry collaboration. Education: MSc in Technology and Society (TU/e, 2004) PhD in Management of Technology (EPFL, 2009) Research Interests: Open Innovation and Collaborative Processes Innovation Ecosystems and Societal Impact Business Model Design and Digital Transformation University-Industry Collaboration and Policy Recent Work Trends: His publications address open innovation in AI, public sectors, and sustainability, with a focus on ecosystem governance, circular business models, and societal challenges. Collaborative prototyping and imperfection-as-practice are recurring themes. Scientific Awards: Highly Cited Researcher (2020, 2019) SCIENCE Dissemination Award (2019) Best Teacher Award (2012) Top Professor on Twitter Academic Network: He has collaborated with institutions like EPFL, UC Berkeley, and the University of Southern Denmark. His teaching includes courses on open innovation and technology entrepreneurship.

Mohsen Kaboliمشاهده پروفایل

استادیار

• Embodied Robotics
• Tactile Intelligence
• Machine Learning
• +۳ مورد دیگر

Mohsen Kaboli is an Assistant Professor (Part-time) at Eindhoven University of Technology (TU/e) in the Netherlands, affiliated with the Department of Electrical Engineering and the Electronic Systems group. Since 2018, he has served as head of the AI, Robotics & Cognitive Vehicle research lab at BMW Group’s Center of Invention in Munich, Germany. Previously, he held positions at Radboud University’s Institute for Brain and Cognition (2019-2022) and Technical University of Munich (TUM) as a postdoctoral research fellow. His research focuses on Embodied Robotics, Visuo-Tactile Interactive Perception, and Machine Learning, with applications in Robotic Grasping, Human-Robot Collaboration, and Neuromorphic Computing. He leads European-funded projects like PHASTRAC (Oscillatory Neural Networks for AI Edge Computing) and INTUITIVE (Tactile User Interfaces). Keywords: Tactile Intelligence, AI, Robotics Application Domains: Mobile Robotics, Medical Instrumentation, Neuroprosthetics His recent publications (e.g., ViTract, Shared Visuo-Tactile Perception) emphasize robust object pose estimation and shape reconstruction using Bayesian filtering and Graph Neural Networks. He has authored ~40 academic works and contributed to 20 patents. Scientific Awards : IEEE Senior Member (2018) Georges Giralt Ph.D. Award (Finalist) IEEE ICRA Outstanding Paper Award (Finalist, 2023) IEEE FLEPS Outstanding Student Award (Winner, 2022) IEEE ROSE Outstanding Paper Award (Winner, 2024) Mohsen Kaboli serves as Editor/Associate Editor for IEEE ICRA, IROS, R-AL, T-RO, and IJRR. His work bridges tactile perception, AI, and robotics, with impacts in wearables, medical devices, and autonomous systems.

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

• Artificial Intelligence
• Autonomous Agent Systems
• Machine Learning
• +۴ مورد دیگر

Koray Karaca is an Assistant Professor in Philosophy , specializing in the intersection of Artificial Intelligence , Machine Learning , and Philosophy of Science . His work critically examines epistemic and ethical dimensions of AI, with a focus on Knowledge Representation and Reasoning , Human-AI Interaction , and Experimental Methodology in high-energy physics. Research interests span Artificial Intelligence , Autonomous Agent Systems , and Scientific Modeling . His studies analyze diagrammatic representations in collaborative particle physics experiments, inductive risk in ML modeling, and robustness in experimental results. Recent publications explore experimental robustness , Higgs mechanism epistemology , and ethical frameworks for AI . His work combines Physics , Machine Learning , and Philosophical Analysis , addressing challenges in theory-ladenness , data acquisition , and interdisciplinary collaboration . Karaca’s contributions include developing design thinking frameworks for AI systems and evaluating scientific unification across physics domains. His studies on binary classification models highlight the societal implications of technical decisions in machine learning.

• Aircraft Maintenance
• Condition-Based Maintenance
• Prognostics and Health Management
• +۴ مورد دیگر

Dr. W.J.C. Verhagen is a researcher in the Operations & Environment department at Delft University of Technology's Faculty of Aerospace Engineering. His scholarly work focuses on the critical intersection of aircraft maintenance systems, data analytics, and operational optimization. With 88 research outputs spanning articles, conference contributions, and book chapters, he maintains an active research profile in aerospace engineering. His research interests center on condition-based maintenance , prognostic methodologies , and anomaly detection systems for aircraft and spacecraft. Recent work emphasizes the integration of machine learning techniques like CNN-LSTM networks for remaining useful life prediction and the development of generic diagnostic frameworks applicable across aerospace platforms. His fingerprint analysis shows strong expertise in aircraft engineering (73%), maintenance systems (72%), and data-driven models (56%). Analysis of his 15 most recent publications reveals a clear trajectory toward practical implementation of predictive maintenance systems, with increasing focus on real-time decision support and economic viability. The 2023-2024 publications demonstrate growing industry relevance, particularly in aviation maintenance optimization and spacecraft health monitoring. Dr. Verhagen actively contributes to the academic community through editorial roles at IOS Press and Springer. While no specific awards are listed in the profile, his work shows significant scholarly impact with multiple publications accumulating high download counts and reader metrics. His supervision portfolio includes 5 graduate research projects, indicating active mentorship of the next generation of aerospace engineers. The completed ReMAP project demonstrates his capacity to lead multi-institutional research efforts focused on real-time maintenance planning for aircraft systems. Current research appears to be building on this foundation with increasingly sophisticated data-driven approaches to maintenance optimization.

Vicente Alarcon-Aquinoمشاهده پروفایل

استاد

• Cybersecurity
• Network Monitoring
• Anomaly Detection
• +۷ مورد دیگر

Dr. Vicente Alarcon-Aquino is a Professor in the Department of Computing, Electronics, and Mechatronics at Universidad de las Americas Puebla (UDLAP), Mexico. He received his Ph.D. and D.I.C. degrees in Electrical and Electronic Engineering from Imperial College London in 2003. He previously served as department head from October 2012 to June 2018 and spent a research stay at King's College London in 2017. Dr. Alarcon-Aquino is a Senior Member of IEEE, a Level I member of the Mexican National System of Researchers (SNI), and a Fellow of the Mexican Academy of Sciences. His educational background includes: Ph.D. and D.I.C. in Electrical and Electronic Engineering, Imperial College London, UK (2003) Dr. Alarcon-Aquino's research focuses on cybersecurity, network monitoring, anomaly detection, wavelet analysis, and machine learning. His work spans theoretical foundations to practical applications in network security, with significant contributions to intrusion detection systems, cryptographic techniques, and machine learning approaches for security applications. He has developed innovative methods combining wavelet transforms with neural networks for various security and signal processing applications. His scholarly contributions include over 180 research articles in refereed journals and conference proceedings, a book on MPLS networks, and numerous citations. As an editor, he serves as Associate Editor for IEEE Access Journal and as Academic & Section Editor for PeerJ Computer Science, focusing on Security & Privacy. Notable professional recognitions include: Senior Member of IEEE Mexican National System of Researchers (SNI Level I) Member of the Mexican Academy of Sciences Dr. Alarcon-Aquino has supervised over 70 theses, including 8 Ph.D. dissertations, 15 Master's theses, and more than 37 Bachelor's theses. His supervision spans topics including network intrusion detection, information security, encryption algorithms, wavelet-based signal processing, neural networks, EEG signal processing, and biometric cryptosystems. He has hosted international research students from institutions including the Polytechnic University of Madrid and Kiel University of Applied Sciences. His research group at UDLAP focuses on developing advanced security solutions for modern network environments, with particular emphasis on applying machine learning techniques to cybersecurity challenges. Current projects include blockchain-based security solutions, federated learning approaches for intrusion detection, and advanced anomaly detection systems for IoT environments.

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.