جست‌وجوی استاد و پژوهشگر

Alexios Balatsoukas-Stimmingمشاهده پروفایل

استادیار

• VLSI Circuits for Communications
• Error-Correction Coding Theory
• Non-Linear Signal Processing
• +۳ مورد دیگر

Dr. Alexios Balatsoukas-Stimming is an Assistant Professor in the Electronic Systems group of the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). His research focuses on the intersection of communications, hardware design, and machine learning, with specific interests in VLSI circuits for communications, error-correction coding, non-linear signal processing, and massive MIMO systems. He also explores applications of approximate computing and machine learning in communication signal processing. Education: Dipl.-Ing. (2010) Electronics and Computer Engineering, Technical University of Crete MSc (2012) Electronics and Computer Engineering, Technical University of Crete PhD (2016) Computer and Communications Sciences, École polytechnique fédérale de Lausanne (EPFL) Research Interests: His work emphasizes interdisciplinary approaches to improve communication systems' speed, reliability, and energy efficiency. Key areas include VLSI implementation of coding algorithms, belief propagation decoding, and leveraging machine learning for signal processing tasks. He has contributed to advancements in polar codes, LDPC codes, and full-duplex communication systems. Affiliations: Holds an honorary adjunct assistant professor position at Rice University (USA). Active in conferences and journals as a committee member, founding editor (IEEE journal), and reviewer. Labs/Teams: Associated with the Signal Processing for Communications Lab , Electronic Systems group, Center for Wireless Technology Eindhoven , and the EAISI initiative at TU/e.

Thomas Neeleمشاهده پروفایل

استادیار

• Formal Methods
• Model Checking
• Concurrent Systems
• +۴ مورد دیگر

Thomas Neele is an Assistant Professor at TU/e in the Formal System Analysis group, focusing on concurrency analysis and scalability of formal methods. He earned his PhD at TU/e in 2020 under Prof. Jan Friso Groote and Dr. Tim Willemse, with postdoctoral work at Royal Holloway, University of London. His research addresses partial-order reduction, symbolic techniques, and automata learning. Notable awards include the IPA Dissertation Award (2020) and an NWO Veni Grant (2024) for Explainable Formal Methods with Certificates. Education: BSc and MSc in Computer Science from University of Twente. Teaching includes Automated Reasoning (2022–2024), Automotive Software Engineering, and supervision of over 10 Master’s theses. Research interests span formal verification, fixpoint logic, and parallel programming. Key projects include CLeVer (Postdoc) and MACHINAIDE (2022). Tools developed include contributions to mCRL2 and LTSmin. Awards: IPA Dissertation Award, Best Paper Awards (ETAPS/FACS), NWO Veni Grant Grants: NWO Veni Grant (2024) Labs/Teams: Formal System Analysis group, IPA research school (Managing Director)

• Computer Science
• Big Data
• Distributed Computing
• +۶ مورد دیگر

Eliya Buyukkaya is a researcher active in the fields of Computer Science , Big Data , and Distributed Computing . Their work focuses on scalable systems, environmental informatics, and data compression techniques. 2025: Crop growth simulations using big data 2021: Bit Plane Slicing for clustering 2018: Video streaming optimization for games 2017: Clustering anomaly detection 2015: Cloud resource selection for HPC Research spans Big Data Analytics , Collaborative Algorithms , and Latency Optimization , emphasizing scalability and efficiency. Their work impacts Environmental Informatics , Game Networking , and High-Performance Computing . Collaborations include experts in Earth Observation and Computer Science , with publications in top-tier venues like Computers and Electronics in Agriculture and PLoS ONE .

Flip van Spaendonckمشاهده پروفایل

پژوهشگر

• Model-Driven Engineering
• Model-Based Testing
• Mutual Exclusion
• +۵ مورد دیگر

Flip van Spaendonck is a Researcher at the School of Mathematics and Computer Science at Eindhoven University of Technology. His work focuses on Model-Driven Engineering , Model-Based Testing , and Mutual Exclusion Protocols , with a particular emphasis on improving State Space Generation and Conformance Relations . Recent research outputs include contributions to formal verification techniques, parallel computing frameworks, and term library design. Key subfields of interest span Mutual Exclusion Protocols Model Checking Parallel Systems Context-Free Grammars Boolean Equations Code Transformation . Publications highlight collaborations with J.F. Groote, M. Laveaux, and T.A.C. Willemse, addressing challenges in parallel model checking and state space optimization . No scientific awards or student advising details are explicitly mentioned in the provided data.

Monika Trimoskaمشاهده پروفایل

استادیار

• Cryptanalysis
• Post-Quantum Cryptography
• Multivariate Cryptography
• +۶ مورد دیگر

Monika Trimoska is an Assistant Professor at the Coding Theory and Cryptology group at Eindhoven University of Technology (TU/e) , where she has worked since 2023. Previously, she was a postdoc at Radboud University and obtained her Ph.D. in cryptography at the University of Picardie Jules Verne under the supervision of Gilles Dequen and Sorina Ionica. She has also served as a Teaching and Research Assistant at her alma mater. Current Affiliation: Assistant Professor, TU/e (2023–present) Previous Affiliations: Postdoc at Radboud University (2021–2023), Teaching/Research Assistant at University of Picardie (2017–2021) Research Interests revolve around cryptanalysis of post-quantum cryptosystems , focusing on multivariate , code-based , and isogeny-based systems. Her work bridges theoretical and practical security analysis, particularly through SAT solvers and fault injection attacks . Recent Publications demonstrate expertise in algebraic attacks against digital signature schemes like MQ-Sign and CSIDH , with a focus on code equivalence problems , trilinear forms , and parallel collision search . She has also contributed to homomorphic encryption applications. Teaching Activities include courses on Applied Number Theory and Algebra and guest lectures on isogeny-based cryptography at Radboud University. She has co-supervised multiple Ph.D. , Master’s , and Bachelor’s students. Technical Contributions include open-source tools like WDSat (SAT solver for Weil descent) and MCE (Matrix Code Equivalence implementations).

Kees Vuikمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Computational Fluid Dynamics
• +۴ مورد دیگر

Kees Vuik is a Professor at the Delft Institute of Applied Mathematics within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. His research focuses on developing advanced numerical methods for complex engineering and scientific simulations across multiple domains including energy systems, environmental engineering, and computational physics. His research interests center on Numerical Analysis and Scientific Computing, with specialized expertise in Computational Fluid Dynamics, Reservoir Simulation, and Wave Propagation. He develops innovative preconditioning techniques for large-scale linear systems and creates high-fidelity models for subsurface flow, flood prediction, and energy conversion systems. Recent work integrates graph-based frameworks for electrolyzer modeling and advanced triangulation methods for point cloud processing. Analysis of his 2025 publications reveals a strong interdisciplinary focus on sustainable energy solutions (hydrogen/CO2 storage), environmental resilience (flood modeling), and computational efficiency (parallel preconditioning). His work consistently bridges theoretical numerical methods with practical engineering applications, particularly in energy transition technologies and climate-related simulations. Professor Vuik's scientific contributions have earned significant recognition: Officer of the Order of Orange-Nassau (2021) Professor of Excellence award (Leermeesterprijs) from TU Delft (2022) He has supervised 49 students across bachelor, master, and PhD levels, with notable successes including undergraduate research published in leading medical journals. His collaborative network spans 21 documented activities including industrial partnerships with Fujitsu Laboratories and leadership in academic societies, indicating substantial research funding and mentorship impact. As chair of the Bataafsch Genootschap der Proefondervindelijke Wijsbegeerte (2023-2024) and active member of the Delft Institute of Applied Mathematics, he contributes to both foundational mathematical research and real-world applications through international collaborations and industrial knowledge exchange.

Marc Schröderمشاهده پروفایل

استادیار

• Game Theory
• Mechanism Design
• Algorithms
• +۳ مورد دیگر

Marc Schröder is an Assistant Professor at the School of Business and Economics, Maastricht University, Netherlands. His research focuses on game theory, mechanism design, algorithms, and operations research with applications in scheduling, network economics, and strategic decision-making. He holds a PhD from Maastricht University and has held postdoctoral positions at RWTH Aachen University and Universidad de Chile. His work bridges theoretical foundations and practical applications in algorithmic game theory and optimization. Education and Career: PhD and postdoc at Maastricht University (2011-2016), followed by postdocs at RWTH Aachen (2017-2020) and Universidad de Chile (2016-2017). Current role since 2020. Research Interests: Specializes in strategic interactions in networks, scheduling under constraints, congestion games, and algorithmic mechanisms. His work emphasizes equilibrium analysis, approximation algorithms, and optimization under uncertainty. Recent Publications Trends: Focus on bicriteria network flows, scheduling algorithms with deadlines, congestion game equilibria, and prophet inequalities. Active in venues like Operations Research, Mathematics of Operations Research, and Game Theory conferences. Awards: No awards explicitly listed in provided texts. Teaching: Covers Actuarial Mathematics, Dynamic Modelling, Game Theory applications in economics, and computational methods. Coordinates courses in quantitative economics and game-theoretic optimization.

Jiwon Jungمشاهده پروفایل

استادیار

• Digital Health
• Data-Driven Design
• Machine Learning in Healthcare
• +۳ مورد دیگر

Jiwon Jung is an Assistant Professor at TU Delft's Faculty of Industrial Design Engineering and holds a parallel role as Assistant Professor in Surgery at Erasmus MC. Her research focuses on advancing data-driven design methodologies for digital health, particularly leveraging machine learning to analyze large-scale patient experiences. She developed the Patient Community Journey Mapping tool to transform healthcare design through community-level data insights. Key collaborations include projects with Erasmus MC on cardiology and oncology patient pathways, supported by grants from Convergence Open Mind Call and NWO. Current initiatives include the Consultation Room 2030 project addressing future healthcare systems. Her work bridges design, data science, and clinical medicine to improve patient-centered care. Research Areas: Digital health, machine learning applications, healthcare system design Tools Developed: Patient Community Journey Mapping framework Grants: Convergence Open Mind Call, NWO Take-off Phase 1 Publications emphasize interdisciplinary approaches to healthcare challenges, with recent work exploring clinician-AI interaction needs and colorectal cancer patient experiences. She advises startups like Qaring to translate research into practical tools.

Arnold Meijsterمشاهده پروفایل

مدرس

• Computer Science
• Parallel Computing
• Algorithm Design
• +۲ مورد دیگر

Arnold Meijster is a Lecturer in the Faculty of Science and Engineering at the University of Groningen, specializing in Computer Science with focus areas in Parallel Computing, Programming, Signal Processing, and Algorithm Design. He teaches courses including Imperative Programming, Functional Programming, Signals and Systems, Compiler Construction, and Artificial Intelligence. His research spans parallel computing, image processing algorithms, and artificial intelligence, with particular expertise in developing efficient algorithms for morphological image processing and watershed transforms. His work frequently involves parallel algorithm design for shared-memory systems applied to large-scale image data. Publications from 1996 to 2018 demonstrate consistent focus on image processing and parallel computing, with recent work on hybrid shared-memory algorithms and machine learning applications. Early contributions centered on watershed transformation and connected component algorithms. Best young author paper award (1998)

Loek G.W.A. Cleophasمشاهده پروفایل

استادیار

• Model-Driven Software Engineering
• Algorithm Engineering
• Pattern Matching
• +۵ مورد دیگر

Loek G.W.A. Cleophas is an Assistant Professor in Engineering of Software-Intensive Systems at Eindhoven University of Technology (TU/e), affiliated with the Mathematics and Computer Science department. He holds an Extraordinary Associate Professorship at Stellenbosch University and has held visiting roles at TU Braunschweig (2016-2017) and Umeå University (2014-2016). His academic career includes industry collaborations with ASML and Canon, and leadership as Managing Director of the Dutch research school for Programming and Algorithmics (IPrA). Education: Both his MSc (with honors) and PhD in Computer Science and Engineering were obtained at TU/e. His research focuses on model-driven software engineering (MDSE) and algorithm engineering, with emphasis on pattern matching using finite automata and parallel processing for large datasets. Recent work includes model repository analytics, digital twin systems, and variability analysis in software product lines. Research Trends: Over 129 publications span topics like SAMOS framework for model analytics, VPDSL domain-specific languages, and taxonomy-based algorithm toolkits. His work bridges theoretical foundations with industrial applications in high-tech systems. Advising: Supervised 27 postgraduate students at Stellenbosch and TU/e. Grants/Projects: Led collaborations with ASML on model-driven virtualization, and organized international workshops like AMMoRe (2018-2020). Labs/Tools: Developed SAMOS framework for model analytics and LaMa web application for thematic labeling. Active in open-source tool development for correctness-by-construction methodologies.

Vinh Dangمشاهده پروفایل

استادیار

• Manufacturing Automation
• Autonomous Mobile Robots
• Smart Factory Systems
• +۴ مورد دیگر

Dr. Vinh Dang is an Assistant Professor at the Operations, Planning, Accounting & Control (OPAC) group within the Department of Industrial Engineering and Innovation Sciences at Eindhoven University of Technology (TU/e). His research focuses on automating manufacturing processes, material handling, and the integration of autonomous mobile robots (AMRs) and automated guided vehicles (AGVs) in smart factories, warehouses, and healthcare environments. He has contributed to projects such as Advanced Manufacturing Logistics and Green Transportation Delta, emphasizing sustainable and efficient industrial systems. Education: PhD in Operations Research, Aalborg University (2014) M.Sc. in Industrial Engineering, Pusan National University (South Korea) B.Eng. in Industrial Systems Engineering, Ho Chi Minh City University of Technology (Vietnam) Research Interests: Dr. Dang’s work centers on algorithmic methodologies for planning/scheduling automated systems, particularly in smart industries. He explores robotics integration, energy-efficient logistics, and real-time decision-making frameworks. His research combines operations research techniques with multi-agent systems and reinforcement learning to enhance manufacturing and service sector efficiency. Key Projects: DigiTwop: Digital Twin-based warehouse optimization (2025–2028) Green Transport Delta – Electrification: Sustainable transportation solutions (2021–2024) Decentralized Distribution: Logistics sector disruption analysis (2023–2025) Teaching: Courses include Manufacturing Integration , Modeling and Analysis of Manufacturing Systems , and Engineering Design Expertise , aligning with UN Sustainable Development Goals such as Industry, Innovation & Infrastructure (SDG 9). Labs/Teams: Collaborates on digital twin frameworks for AGV communication and energy management systems, emphasizing practical industry applications like smart hospitals and high-tech logistics.

• Embedded Systems
• Cyber-Physical Systems
• Real-Time Systems
• +۳ مورد دیگر

Jeroen P.M. Voeten is a Full Professor in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e), holding positions in both the High Tech Systems Center and Electronic Systems group. He also serves as a Senior Scientist and Scientific Advisor at TNO-ESI. His research focuses on formal methods, real-time systems, and embedded systems engineering. He earned his MSc (1991) and PhD (1997) in Electrical Engineering from TU/e. His work contributes to UN Sustainable Development Goals, particularly in innovation and industrial applications. Research interests include cyber-physical systems, performance modeling, and model-based design methodologies. Notable projects include the Carm 2G collaboration with ASML and the TRANSACT initiative. He has authored 157+ publications, including 87 conference papers and 11 journal articles. Awards include recognition at FDL 2005 and contributions to formal synthesis methodologies. Education: MSc (1991), PhD (1997) in Electrical Engineering, TU/e Labs: Model-Based Design Lab, Embedded Systems Institute Projects: Carm 2G (ASML partnership), TRANSACT (2021–2025), and others His work emphasizes bridging research and industrial applications, with current focus on cloud-based real-time systems and fault-tolerant scheduling techniques.

Mircea Lazarمشاهده پروفایل

دانشیار

• Constrained Control
• Model Predictive Control
• Hybrid Systems
• +۴ مورد دیگر

Mircea Lazar is an Associate Professor in the Control Systems group at Eindhoven University of Technology. His research spans constrained control, model predictive control (MPC), and stability analysis of hybrid systems, with applications in power systems, smart grids, and precision mechatronics. Research highlights include: Development of data-driven predictive control methods Stability guarantees for nonlinear and hybrid systems Applications in water networks, power converters, and mechatronics He has received the EECI PhD Award and NWO VENI grant. Recent publications focus on real-time MPC implementations, physics-guided neural networks, and optimization for large-scale systems. Collaborations include ASML, DAF, Philips, and Ford. He chairs the IEEE CSS Technical Committee on Hybrid Systems and has supervised 9 PhD students.

Bin Shiمشاهده پروفایل

پژوهشگر ارشد

• Photonics
• Optical Communication
• Neural Networks
• +۳ مورد دیگر

Bin Shi is a Research Fellow at Eindhoven University of Technology's Department of Electrical Engineering, focusing on photonics, optical communication, and neuromorphic computing. He is a core member of the ECO Research Centre for Integrated Nanophotonics (2014–2025), contributing to next-generation optical networks and photonic integrated circuits. His research expertise includes photonic integrated circuits (PICs), semiconductor optical amplifiers (SOAs), and neural network implementations using optical systems. Key projects involve developing low-cost metro-access networks using SOA-based optical add-drop multiplexer (OADM) nodes and exploring photonic computing architectures for deep learning applications. Recent work emphasizes non-invasive characterization techniques for cascaded SOAs, software-defined electro-optical neural networks, and photonic beamforming systems. He has contributed to over 44 research outputs since 2018, with notable publications in Optics Letters and Optics Express . Shi collaborates internationally on topics like photonic neural networks and nanophotonic devices. He teaches the course 'Brain-inspired optical computation' and has been featured in media for his work on light-based neural networks.

Jaron Sandersمشاهده پروفایل

استادیار

• Stochastic Operations Research
• Probability Theory
• Machine Learning
• +۷ مورد دیگر

Jaron Sanders is an Assistant Professor in the Department of Stochastic Operations Research at Eindhoven University of Technology (TU/e). He holds M.Sc. degrees in Mathematics and Physics (2012) and a Ph.D. in Mathematics (2016) from TU/e. Previously, he was a post-doctoral researcher at KTH Royal Institute of Technology (2016–2017) and an Assistant Professor at Delft University of Technology (2017–2019). His research focuses on optimizing large complex systems through interdisciplinary approaches combining probability, statistics, operations research, and machine learning. He has received notable awards, including the ASML Graduation Prize (2012) and TU/e PhD Thesis Award (2017). Education: PhD in Mathematics, TU/e (2016) MSc in Mathematics and Physics, TU/e (2012) Research interests include stochastic optimization, Markov chains, random matrices, and applications in quantum computing, wireless networks, and machine learning. His work bridges theoretical foundations with practical systems, such as analyzing cluster detection in sequential data and developing noise-resilient optical neural networks. Key contributions include advancements in Block Markov Chains, spectral analysis, and error accumulation models in quantum circuits. He leads projects like the Waardevol AI initiative and collaborates on spectral concentration in Markov chains. His teaching spans stochastic simulation, decision theory, and mathematical modeling. Awards: ASML Graduation Prize for Mathematics (2012) TU/e PhD Thesis Award (2017) VvS+OR Master’s Thesis Award (2013) He actively participates in conferences like ACM MobiHoc and organizes seminars to foster interdisciplinary collaboration. His research addresses both theoretical challenges and real-world applications, emphasizing scalable solutions for complex systems.