جست‌وجوی استاد و پژوهشگر

Servaas Kokkelmansمشاهده پروفایل

استاد

• Quantum Computing
• Ultracold Quantum Gases
• Quantum Materials
• +۳ مورد دیگر

Servaas Kokkelmans is a Full Professor leading the Coherence and Quantum Technology group in the Department of Applied Physics at Eindhoven University of Technology (TU/e). He also serves as Scientific Director of the Center for Quantum Materials and Technology Eindhoven (QT/e) , co-founded the Eindhoven Hendrik Casimir Institute , and is a key figure in quantum research. His academic journey includes a PhD from TU/e (2000), postdoctoral work at JILA (USA) and Laboratoire Kastler Brossel (France), and faculty positions at TU/e since 2004. Research Focus: His work centers on strongly-interacting quantum gases, neutral atom quantum computing, and ultracold atomic physics. Specific areas include Feshbach resonances, Efimov physics, quantum simulations, and quantum material development. Awards & Grants: Notable recognitions include the NWO Vici Award (2015) and Teacher Award of Appreciation (2021) . His research has been supported by grants such as the Coherence and Quantum Technology initiative and the Eindhoven University of Technology Fund for Excellence (2010) . Education: MSc (1996) and PhD (2000) in Physics from TU/e Leadership roles: Founded QT/e (2018), became Full Professor (2023) Current Activities: Teaches Quantum Optics and Quantum Information , Ultracold Quantum Physics , and Hybrid Quantum Computing . His lab focuses on advancing quantum technologies through experimental and theoretical studies of ultracold atoms and quantum materials.

Boris Škorićمشاهده پروفایل

دانشیار

• Quantum Cryptography
• Information Security
• Digital Forensics
• +۳ مورد دیگر

Boris Škorić is an Associate Professor in the Security of Embedded Systems (SEC) group at the Department of Mathematics and Computer Science, Technische Universiteit Eindhoven. His research focuses on quantum cryptography, security with noisy data, and collusion-resistant watermarking. He is affiliated with the CREST project and has contributed to advancements in quantum-secure authentication and quantum key distribution protocols. Education : Ph.D. in Physics (Quantum Hall Effect), M.Sc. in Physics His research interests span three core areas: (1) Security with noisy data, addressing challenges in cryptographic primitives using physical measurements; (2) Quantum physics for security, including quantum readout of Physical Unclonable Functions (PUFs) and quantum key recycling; and (3) Collusion-resistant watermarking codes for digital content protection. He has pioneered techniques such as quantum-secure authentication via laser speckle and error correction methods for secure key storage. His publications reflect a strong emphasis on quantum security, including developments in continuous-variable quantum key distribution, entropically secure encryption, and quantum position verification. Recent work explores applications in healthcare data protection and secure communication over turbulent optical channels. Grants & Labs : Active in the CREST project (Collusion-Resistant Embedding and Secure Tracing) and collaborates with industry on quantum-secure technologies. Leads research in the SEC group, mentoring projects on cryptographic protocols and embedded system security.

Bart Jansenمشاهده پروفایل

دانشیار

• Algorithms
• Complexity Theory
• Graph Theory
• +۳ مورد دیگر

Bart Jansen is an Associate Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). His expertise includes algorithms, complexity theory, and graph theory, with a focus on parameterized (graph) algorithmics, particularly kernelization. He is a member of the NWO Gravitation project NETWORKS, investigating algorithms and stochastics in network problems. Education: MSc in Applied Computing Science (Utrecht University, 2009), PhD (Utrecht University, 2013). Postdoctoral research at the University of Bergen (2013–2014) before joining TU/e as Assistant Professor, later promoted to Associate Professor. Research emphasizes systematic development and theoretical analysis of preprocessing algorithms for NP-complete problems. Notable contributions include kernelization complexity, cross-composition techniques, and parameterized preprocessing efficiency. Selected awards include the VENI Grant (NWO) and the Christiaan Huygens Prize in ICT (2014). He has served on program committees for SODA, ICALP, and others, co-chairing IPEC 2019. Editor roles include ACM Transactions on Algorithms and steering committees for PACE challenges. Research interests span preprocessing algorithms, kernelization theory, and the interplay between algorithm design and complexity reduction. Current educational activities include teaching advanced algorithms and formal analysis for pre-master students.

Sjef Cobbenمشاهده پروفایل

استاد

• Power Quality
• Electrical Grid Safety
• Smart Grid Technologies
• +۳ مورد دیگر

Sjef Cobben is a part-time Professor of Power Quality at the Eindhoven University of Technology's Department of Electrical Engineering. His primary affiliation is with Alliander, the Dutch grid operator, where he has served as a research scientist since 1979. He holds a Master's degree (cum laude) and a PhD from TU/e, with his doctoral work focused on power quality implications at the point of connection (2007). His research emphasizes low-voltage (LV) and medium-voltage (MV) grid safety, smart grid technologies, and the integration of renewable energy sources. Key research themes include voltage/current quality interactions, grid stability under renewable energy integration, and device/network compatibility. His recent publications (2024–2025) explore hosting capacity assessment, grid expansion planning, and EV charging infrastructure challenges. He co-founded CO-Education in 2009 to provide specialized training in electrical engineering domains like power quality and grid sustainability. Professional activities include advisory roles and educational initiatives through his company. No scientific awards are explicitly listed, but his long-standing contributions to grid research and education indicate significant industry recognition. He advises on grid modernization projects and collaborates with organizations like Alliander to address energy transition challenges. His work bridges academic research with practical grid solutions, focusing on maintaining grid safety and stability during rapid technological shifts.

Jose Pineda de Gyvezمشاهده پروفایل

استاد

• Low-Power Electronics
• Semiconductor Technology
• Systems Power Management
• +۳ مورد دیگر

José Pineda de Gyvez is a Full Professor of Resilient Nanoelectronics at Eindhoven University of Technology (TU/e), specializing in ultra-low power designs, variability tolerance, and context-aware computing. He leads research in semiconductor technology, systems power management, and low-power techniques for integrated circuits. His work bridges academia and industry, focusing on applications like IoT, microcontrollers, and healthcare electronics. Pineda holds a PhD from TU/e (1991) and has held roles at Texas A&M University (1991-1999), Philips Research (1999-2006), and NXP Semiconductors (2006 onwards as Principal Scientist and Fellow). He joined TU/e as a professor in 2006, maintaining dual roles in academia and industry innovation. His research emphasizes low-power digital design techniques such as subthreshold design, DVFS, and technology-aware physical design. He has authored over 100 publications, 4 books, and 30+ US patents. His work is funded by institutions like the Dutch Ministry of Science and the US National Science Foundation. As an IEEE Fellow (2009), Pineda contributes to editorial boards of journals like the Journal of Low Power Electronics . His labs focus on embedded systems, energy harvesting, and resilient nanoelectronics, with a strong emphasis on practical industrial applications.

Danny Ruijtersمشاهده پروفایل

استاد

• Data-Driven Healthcare
• Image-Guided Therapy
• Medical Imaging Technologies
• +۳ مورد دیگر

Danny Ruijters is a part-time Full Professor at Eindhoven University of Technology (TU/e), specializing in data-driven value-based healthcare in image-guided therapy. His research focuses on developing intelligent systems that optimize data utilization in minimally invasive treatments, particularly translating population-level medical data to individual patient care. He concurrently serves as a Principal Scientist at Philips' Image Guided Therapy Systems Innovation department since 2011. Education: Bachelor's in Engineering, RWTH Aachen University (2001) Master's Thesis at École Nationale Supérieure des Télécommunications (ParisTech, 2001) PhD from TU/e & KU Leuven (2010) on multi-modal image fusion in minimally invasive treatments Research Focus: Hyper-personalized patient treatment through AI-driven analysis of large medical datasets, clinical workflow optimization, and prototype development for interventional therapies. His work bridges technical innovation with clinical and regulatory requirements. Teaching Activities: Signal Processing Image Analysis for Healthcare Technologies DSP Fundamentals (Signals II) Professional Affiliations: Employed by Philips Healthcare since 2001, leading R&D in image-guided therapy systems. Active in translating research into clinically viable solutions through iterative prototyping and clinical validation.

• Artificial Intelligence in Medicine
• Allergy and Immunology
• Machine Learning Applications
• +۴ مورد دیگر

Merlijn van Breugel is a researcher at the University of Groningen's Faculty of Medical Sciences, Department of Immunology, specializing in artificial intelligence applications for allergy and immunology. His work bridges computational methods with clinical immunology to develop predictive models for allergic diseases. His research focuses on AI-driven allergy prediction using multi-omics data, particularly DNA methylation biomarkers for childhood allergic diseases. Key projects include the exquAIro initiative exploring clinical AI implementation, with emphasis on translating machine learning models into practical healthcare solutions. His fingerprint reveals strong expertise in Artificial Intelligence (100%), Allergy (100%), and Clinical Impact (60%) within immunology. Research trends show progression from foundational multi-omics analysis (2021-2022) toward implementation challenges of AI in clinical settings (2023-2025). His publications consistently address the integration of machine learning with immunological data to improve diagnostic accuracy and personalized treatment strategies, with growing focus on real-world clinical adoption barriers. He actively collaborates with international researchers including Koppelman, Nawijn, and Bønnelykke, with significant contributions to high-impact journals like Nature Communications and Journal of Allergy and Clinical Immunology . His 2025 academic presentation on the exquAIro initiative demonstrates ongoing leadership in AI medicine translation.

• Dynamic Programming
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

E. Emir Demirović is affiliated with the Department of Algorithmics at the Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology. His research focuses on dynamic programming, constraint programming, and optimization techniques applied to artificial intelligence and machine learning systems. He leads the project "Towards a Unification of AI-Based Solving Paradigms for Combinatorial Optimisation" (2022–2026), exploring AI methods for logistics and combinatorial problems. Collaborations include work on decision trees, proof systems, and constrained path optimization. Key research interests include policy synthesis for black-box systems, optimal classification trees for continuous data, and verification frameworks for constraint solvers. His work bridges theoretical algorithm design with practical applications in healthcare analytics and mobility systems, as highlighted in a 2025 media feature on AI in transportation. Demirović has contributed datasets for optimal decision tree methods and variable initialization techniques for lazy clause generation solvers. His research emphasizes interpretable AI solutions and rigorous validation through proof logging frameworks.

M. Saeedniaمشاهده پروفایل

پژوهشگر

• Freight Transport
• Multimodal Transport Systems
• Railway Logistics
• +۳ مورد دیگر

M. Saeednia is a Researcher at Delft University of Technology's Department of Transport, Mobility and Logistics within the Civil Engineering & Geosciences School. His work focuses on optimizing freight transport, multimodal systems, and railway logistics. He holds a Dr.ir. (Dutch engineering doctorate) and actively contributes to transportation engineering research. Research interests include pod scheduling, carrier movement optimization, physical internet-based logistics, and automated railway systems. His recent publications address challenges in multimodal freight networks, intermodal connectivity, and robust logistics design. He collaborates extensively with institutions like TU Delft and international researchers on projects involving heuristic frameworks and railway automation. No scientific awards explicitly mentioned. Advising and grants details unavailable in provided texts. His work contributes to labs focused on sustainable transport systems and multimodal infrastructure integration.

Wieke Egginkمشاهده پروفایل

پژوهشگر ارشد

• Pediatric Movement Disorders
• Dystonia
• Deep Brain Stimulation
• +۳ مورد دیگر

Dr. Wieke Eggink is a Neurology Resident and Postdoctoral Research Fellow at the University of Groningen, affiliated with the Faculty of Medical Sciences and Department of Neurology. Her primary focus is on pediatric movement disorders, particularly dystonia, and the application of deep brain stimulation (DBS) therapies. She holds additional roles as a board member of the Vereniging Arts-assistenten Neurologie (VAAN) and Stichting Wetenschapsfonds Dystonie (SWD). Her research concentrates on dystonia etiologies, neuropsychological impacts of movement disorders, and optimizing clinical care through rating scales and interdisciplinary approaches. Key contributions include studies on myoclonus, genetic dystonia syndromes, and biomarkers for DBS efficacy. She has authored over 45 peer-reviewed articles, with recent work emphasizing pediatric neurology and neurocysticercosis case studies. Eggink’s work spans clinical practice and academic research, addressing both motor and non-motor aspects of movement disorders. She actively collaborates with European networks like ERN-RND to improve care standards for rare neurological conditions. Her multidisciplinary approach integrates genetics, neurophysiology, and neuropsychology to advance diagnosis and treatment strategies.

Toni Sfirtsisمشاهده پروفایل

دانشیار

• Strategic Innovation
• Leadership Development
• Corporate Governance
• +۶ مورد دیگر

Toni Sfirtsis is an Associate Professor at TIAS School for Business and Society, specializing in Strategic Innovation and Future Leadership. His expertise spans Business & Society, Family Business Strategy, Healthcare Innovation, and Organizational Change Management. He advises organizations across industries on strategy development and implementation through his executive consulting practice. Affiliations include membership on the Scientific Board of the European Institute of Sales & Account Management (since 2020) and as an Associate Partner at the Alliance Society Netherlands. His research focuses on innovation ecosystems, leadership dynamics, and digital transformation challenges in healthcare and business. Key research interests include: strategic innovation frameworks, leadership collaboration models, ESG integration, design thinking in volatile markets, and AI-driven business evolution. He frequently speaks at global forums and publishes on innovation fitness, organizational agility, and overcoming innovation barriers. His articles analyze trends like Gen AI's impact on management practices, the cultural shift required for ESG success, and the paradox of algorithm-driven marketing. Despite no listed scientific awards, his work emphasizes practical solutions for real-world business challenges. No advising/grants data is available, but his training programs (e.g., Executive Leadership & Management, Innovation Management Masterclass) demonstrate active engagement in executive education.

Ton de Kokمشاهده پروفایل

استاد

• Supply Chain Management
• Operations Research
• Quantitative Analysis
• +۳ مورد دیگر

Prof. Ton de Kok is a part-time Professor of Operations Management at Eindhoven University of Technology (TUE), holding roles since 2020. He also serves as General Director of CWI (National Research Institute for Mathematics and Computer Science) in Amsterdam since 2020. Previously, he was full-time Professor at TUE (1992–2020) and held academic positions at Boston University Brussels (1990–1992). His career includes leadership roles such as Director of the European Supply Chain Forum (1996–2018) and Scientific Director of research school Beta (2003–2008). Education: PhD in Mathematics (1985), Free University of Amsterdam MSc in Mathematics and Economics (1981), Leiden University Research Interests: De Kok specializes in Supply Chain Management and Quantitative Analysis , focusing on multi-item multi-echelon inventory systems, logistics optimization, and concurrent engineering. His work emphasizes practical applications, such as inventory software (ChainStock) and operational efficiency in high-tech industries. Key Awards & Recognition: Edelman Award Finalist (2004) ISIR Service Award (2006) Fellow of Beta Research School (2008) Leadership & Outreach: Academic Director of TIAS Master of Operations & Supply Chain Management Former Head of the Operations Planning, Accounting, and Control (OPAC) group (2008–2014, 2017–2020) Public speaker at US top universities and events, including a children’s logistics lecture (2013) Labs & Teams: He leads research teams in inventory systems and supply chain optimization, collaborating with industry partners like Philips and Hilti through case studies and software development.

Jeroen De Flanderمشاهده پروفایل

دانشیار

• Strategy Execution
• AI in Business Decision-Making
• Leadership
• +۳ مورد دیگر

Dr. Jeroen De Flander is an Associate Professor at the TIAS School for Business and Society, specializing in strategy execution and the integration of AI into leadership and decision-making. He is a globally recognized expert with over 38,500 engagements across 40+ countries. His work emphasizes bridging strategic planning with practical execution, highlighted by his Amazon-bestselling books *Strategy Execution Heroes* and *The Execution Shortcut*, as well as *The Art of Performance* (2019). He co-founded *The Performance Factory*, a consultancy focused on strategy execution, and chairs *The Institute for Strategy Execution*, a non-profit promoting best practices. His recent research explores AI’s role in leadership, innovation, and strategic decision-making, as seen in his 2025 articles analyzing AI in creativity, judgment, and market prediction. De Flander’s awards include a 2012 nomination for Management Book of the Year (Netherlands). His grants and advisory roles are not explicitly detailed in texts, but his work spans academic publishing, corporate training, and thought leadership in AI-driven business strategy. He collaborates with institutions like TIAS and global corporations, advocating for human-AI synergy in leadership contexts.

G. Gousiosمشاهده پروفایل

استادیار

• Software Engineering
• Empirical Software Engineering
• Software Analytics
• +۷ مورد دیگر

G. Gousios is an Assistant Professor in the Department of Software Technology at the Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology. He previously held an assistant professor position at Radboud Universiteit Nijmegen from January 2015 to July 2016. His research focuses on empirical software engineering, software analytics, and big data applications in software development. His research interests include: Empirical analysis of software development practices Software analytics and data-driven development Agile methodologies and sprint planning automation Code review and pull request dynamics Dependency and build system analysis Predictive modeling for project delays The recent publications of G. Gousios reflect a strong trend in applying data science and machine learning techniques to real-world software engineering problems. His work spans automated sprint planning, call graph generation, delay prediction using Bayesian models, and empirical studies on pull request decisions. These contributions highlight a focus on improving software development efficiency, reliability, and maintainability through empirical and analytical methods. His research bridges the gap between theoretical models and practical tooling in software engineering. Scientific awards received: ASE 2024 ACM SIGSOFT Distinguished Paper Award He has supervised four academic works, indicating active involvement in student mentoring and research guidance. Although specific grant details are not mentioned, his consistent publication record in top venues and dataset creation suggest successful engagement in research funding and collaborative projects. He collaborates extensively with prominent researchers such as Arie van Deursen and Emad Shihab. G. Gousios contributes to the software engineering research community through dataset sharing, such as the 'Catcher' dataset for API misuse detection, promoting reproducibility and open science. His work is integrated into both academic and industrial software development contexts, emphasizing practical impact.

Huijuan Wangمشاهده پروفایل

دانشیار

• Temporal Network Analysis
• Higher-Order Network Modeling
• Information Diffusion
• +۳ مورد دیگر

Huijuan Wang serves as Associate Professor in the Multimedia Computing Group within the Department of Intelligent Systems at Delft University of Technology's Faculty of Electrical Engineering, Mathematics and Computer Science. With over 20 years of affiliation at TU Delft, she completed both her Master's degree and PhD at this institution before rising to her current faculty position. Her research focuses on temporal network analysis, higher-order network modeling, and information diffusion dynamics. Professor Wang investigates epidemic spreading models, network prediction algorithms, and complex system behaviors through both theoretical frameworks and practical applications. Her work bridges computer science, mathematics, and real-world network phenomena including urban systems and social dynamics. Analysis of her recent publications reveals a strong emphasis on temporal network properties, higher-order dependencies, and predictive modeling. Her research demonstrates consistent innovation in extracting diffusion backbones, measuring network dissimilarity, and developing memory-based prediction techniques for evolving networks. Co-founded Dutch Network Science Society (2018) Established Young Talent Prize recognizing emerging researchers Developed free community events fostering industry-academia collaboration Professor Wang actively mentors PhD candidates, emphasizing genuine interest in students' development as she experienced during her own doctoral studies. She champions initiatives promoting social safety, trust-building, and work-life balance within academic environments while leading efforts to establish processes addressing social safety concerns through cultural transformation.