جستجوی استاد راهنمای دانشگاهی

Geert Deconinck is a full professor at KU Leuven , leading the Electrical Energy Systems and Applications (ELECTA) research group within the Department of Electrical Engineering (ESAT). He also serves as scientific leader of the EnergyVille research center's algorithms domain, focusing on smart electrical networks and thermal systems. M.Sc. and Ph.D. from KU Leuven Head of ELECTA since 2012 (10 professors, 8 postdocs, 70+ PhDs) Over 8 million EUR research budget in last 5 years 44 completed PhDs and 10 current advisees IEEE Transactions editorial board member His research spans smart grid architectures , distributed control , and cyber-physical security , with recent focus on EV-grid integration , renewable energy democratization , and multi-carrier energy systems . Current projects include: Smart Charging - E-Mobility meets Renewable Energy Early Detection and Defense Systems for Smart Grids Open-source P2P energy sharing platforms Microgrid control strategies for PV-battery systems Awarded IET Fellow and IEEE Senior Member status, his work combines machine learning with power systems engineering through both theoretical modeling and experimental validation . He has contributed over 575 publications with 9800+ Google Scholar citations.

• Smart Grids
• Distributed Control Systems
• Renewable Energy Integration
• +۶ مورد دیگر

Tom Dhaene is a Full Professor at Ghent University, affiliated with the Department of Information Technology (INTEC-IDLab) within the Faculty of Engineering and Architecture (FEA). He also holds a position at imec, a research and innovation hub in nanoelectronics and digital technologies. Research Unit: Internet Technology and Data Science Lab (IDLab) Academic Rank: Full Professor Affiliations: Ghent University, imec His research focuses on data-efficient machine learning, surrogate modeling, Gaussian processes, Bayesian optimization, and system identification. He has developed widely used software tools such as the SUMO toolbox and ooDACE, and holds 5 U.S. patents. His work bridges theoretical advancements with practical applications in engineering and biomedical domains. Recent publications highlight his contributions to physics-informed machine learning, antenna design, microwave optimization, and healthcare applications. Notably, he explores Bayesian active learning, multi-objective optimization under uncertainty, and efficient modeling techniques for complex systems. Prof. Dhaene's research has been recognized through over 500 peer-reviewed publications and collaborations across academia, industry, and government sectors globally.

• Data-Efficient Machine Learning
• Surrogate Modeling
• Gaussian Processes
• +۳ مورد دیگر

Charles-Henry Bertrand Van Ouytsel is a Research Assistant and Visiting Lecturer at Université catholique de Louvain , affiliated with the Louvain Polytechnic School (EPL) and the Computer Engineering Center (INGI) . His work focuses on malware analysis , symbolic execution , and machine learning for cybersecurity applications. Research Areas : Packing detection, intrusion detection systems, side-channel security, and adversarial machine learning. Teaching : Involved in courses like Secured systems engineering (LINFO2144) and Software engineering and programming systems seminar (LINFO2359) . His recent publications emphasize malware obfuscation techniques and security evaluation frameworks . Collaborations with Axel Legay and others highlight his contributions to tool development (e.g., Packing-Box , SEMA ). No scientific awards are explicitly mentioned.

• Malware Analysis
• Symbolic Execution
• Machine Learning
• +۳ مورد دیگر

Bruno Tiago da Silva Gomes is a Researcher in the Department of Electronics and Informatics at Vrije Universiteit Brussel (VUB), Belgium. His work focuses on FPGA-based hardware acceleration, biomedical signal processing, and embedded systems. He leads several high-impact projects, including ENACT (environmental health interventions) and Tech4Health (future health technologies). His research spans FPGA design, machine learning acceleration, and real-time signal processing. Education: PhD in Electronics and Informatics (2019, VUB), supervised by Professors Touhafi and Braeken. His thesis addressed streaming application acceleration on FPGAs. Research interests include Field-Programmable Gate Arrays (FPGA), biomedical sensors (e.g., photoplethysmography), beamforming, and high-level synthesis. He has co-authored over 60 publications and holds an h-index of 439. Key projects include OZR4103 (power-efficient AI for biomedical applications) and NSIS3 (decarbonisation technologies). His work integrates hardware-software co-design for edge computing and secure TinyML systems. Advising includes a Master’s thesis on PPG signal analysis. He contributes to datasets like the AMIVU Acoustic Map Imaging Dataset.

• Electronics Engineering
• Signal Processing
• Biomedical Engineering
• +۳ مورد دیگر

Peter Karsmakers serves as Associate Professor at KU Leuven's Department of Computer Science within the Faculty of Engineering Technology, based at the Geel Campus. He coordinates the Declarative Languages and Artificial Intelligence (DTAI) research group and holds leadership roles including coordinator of Research and Education for Computer Science across Geel and Diepenbeek Campuses. Karsmakers earned his PhD in Engineering Science in May 2010, focusing on kernel-based learning algorithms for sparse modeling and efficient predictions from large datasets. His doctoral work established foundations for his current research trajectory in resource-constrained machine learning systems. His research integrates machine learning with signal processing for real-time sensor data interpretation, specializing in anomaly detection from acoustic, radar, and accelerometer signals on embedded devices. Current projects address industrial condition monitoring, elderly care systems, and livestock facility monitoring through three main tracks: acoustic monitoring (e.g., SINS, WATCHDOG), radar-based systems (e.g., FARADAY, NextPerception), and smart electronics for power converters. Recent publications demonstrate strong trends in constraint-guided deep learning architectures for industrial applications, cross-environment robustness in sensor systems, and domain-knowledge integration to reduce data requirements. His work consistently bridges theoretical machine learning with practical implementations in resource-constrained environments. No scientific awards or fellowships were mentioned in the provided materials. Karsmakers supervises over 10 master's theses annually and coordinates a research team of 10 PhD students and a post-doc within DTAI-ADVISE. He has secured approximately 2.3 million euros in funding through VLAIO, EU-ECSEL, and bilateral industry contracts, including 10 active projects such as AutoEdgeML (2024-2028) and Fault Tolerant Neural Networks for Space Applications (2024-2027). He leads the DTAI-ADVISE research group focused on developing software that attaches semantics to sensor data on resource-constrained devices. The team operates across multiple campuses with specialized labs for acoustic monitoring (Geel), radar-based systems (in collaboration with ESAT-TELEMIC), and smart electronics (with Electrical Engineering department), maintaining strong industry partnerships with companies in healthcare, manufacturing, and agriculture sectors.

• Machine Learning
• Deep Learning
• Anomaly Detection
• +۵ مورد دیگر

Marian Verhelst is a Professor at KU Leuven's Faculty of Engineering Science, renowned for her research in hardware-efficient computing and dedication to STEM education. Her work spans hardware acceleration for machine learning, edge AI, and in-memory computing, with a focus on energy optimization and algorithm-hardware co-design. Her research interests include: Designing flexible hardware for ultra-low-power edge AI systems Optimizing sparsity-aware architectures for deep learning workloads Advancing chiplet-based and 3D memory technologies Co-designing algorithms and hardware for probabilistic AI Pioneering STEM outreach through KU Leuven InnovationLab Recent publications (2023–2025) demonstrate strong trends in: Hardware-software co-optimization for edge ML systems Efficient data movement in heterogeneous accelerators Low-precision and sparse computation techniques RISC-V based customizable SoCs Sustainable AI accelerator design Awards & Honors: Young Academy of Europe Award (2021) for science communication and STEM advocacy She leads significant educational initiatives, including the KU Leuven InnovationLab which has engaged 150 schools and 13,000 students since 2014. The program develops hands-on STEM projects (e.g., AI-powered wheelchairs, sustainable energy systems) and provides teacher training to inspire youth in engineering.

• Computer Science
• Electrical Engineering
• Machine Learning
• +۴ مورد دیگر

Frank Piessens is a Full Professor in the Department of Computer Science at KU Leuven's Faculty of Engineering Science, where he leads the Distributed and Secure Software (DistriNet) research group. His research focuses on cutting-edge security challenges at the hardware-software interface. His research interests span: Hardware-software co-design for end-to-end security Confidential computing architectures Microarchitectural side-channel mitigation Secure IoT development Compiler-based security mechanisms Control-flow integrity techniques Recent publications (2024-2025) demonstrate strong focus on: Hardware security cost/performance tradeoffs Processor-level security enhancements (RISC-V, high-end CPUs) IoT device lifecycle security Control-flow leakage prevention Microcontroller IP protection He currently supervises PhD students including M. Bognár and H. Winderix, and leads major research initiatives such as: Hardening confidential computing through vertically integrated system design (2025-2031) Designing secure hardware for software-exploitable attacks (2025-2029) Compiler-based mitigations for microarchitectural side-channels (2023-2027) Security Arms Race at the Hardware-Software Boundary (2020-2025)

• Computer Security
• Hardware-Software Co-design
• Confidential Computing
• +۴ مورد دیگر

Ingrid Moerman is a part-time Professor at Ghent University and a staff member at the Internet Technology and Data Science Lab (IDLab), a core research group of imec embedded within Ghent University and the University of Antwerp. She coordinates mobile and wireless networking research and leads a team of over 30 researchers at Ghent University, with extensive involvement in European and national funding initiatives. She received her Electrical Engineering degree (1987) and Ph.D. (1992) from Ghent University. Her research spans collaborative networks, cognitive radio, software-defined radio, IoT, LPWAN, and high-density wireless access, emphasizing experimentally-supported development of next-generation wireless systems with practical implementations in spectrum management and real-time control. Recent publications (2024-2025) reveal a strong pivot toward AI-integrated wireless networking, featuring OFDMA scheduling innovations, Wi-Fi 6/7 interference mitigation, and time-sensitive networking for industrial applications. Key trends include 5G/6G convergence, vehicular communication enhancements, and digital twin frameworks for network observability, reflecting her focus on mission-critical industrial use cases. Her accolades include: 9 Best Paper Awards 2 FWO Prizes (Research Foundation - Flanders) IMEC Prize of Excellence 2001 MSc Thesis Award (as promoter) Best Demo/Exhibit Award at ICT 2013 DARPA Spectrum Collaboration Challenge Prize ($750,000) She has coordinated major EU projects (FP7/H2020: CREW, WiSHFUL, eWINE, ORCA) with industry partners, securing substantial funding for experimental wireless research. Her grant portfolio emphasizes collaborative innovation in spectrum sharing and neutral-host architectures for multi-operator environments. At IDLab, she directs advanced wireless testbeds supporting real-world validation of technologies like openwifi and White Rabbit, with active experimentation in time-sensitive networking and spectrum collaboration for industrial IoT deployments.

• Wireless Networking
• Cognitive Radio Networks
• Internet of Things
• +۴ مورد دیگر

Dirk Nuyens is a Senior Lecturer in the Department of Computer Science at the Faculty of Engineering Sciences, KU Leuven, Belgium. He is affiliated with the Numerical Analysis and Applied Mathematics (NUMA) research unit and is a member of the iSi Health Institute for Physics-based Modeling for In Silico Health. He also serves on the Faculty Council of Engineering Sciences and the Programme Committees for Engineering Sciences and Computer Science. Education: While specific degrees are not listed, his expertise and research output indicate advanced training in numerical analysis, computational mathematics, and applied mathematics. Research Interests: His primary research focuses on numerical analysis, computational mathematics, and quasi-Monte Carlo (QMC) methods. Specific areas include high-dimensional integration and approximation, lattice rules, low-discrepancy sequences, uncertainty propagation, and financial engineering. He develops algorithms for efficient sampling, integration, and approximation in high-dimensional spaces, with applications in uncertainty quantification, Bayesian inversion, and PDEs with random coefficients. Research Trends: His recent publications emphasize the construction and analysis of embedded lattice-based algorithms for multivariate function approximation and integration. There is a strong focus on achieving higher-order convergence rates, particularly in Korobov and Sobolev spaces, and on developing randomized lattice rules for improved error bounds. His work bridges theoretical developments in numerical analysis with practical implementations in scientific computing and engineering. Scientific Contributions: He has authored or co-authored numerous influential papers in top-tier journals such as Mathematics of Computation , Journal of Complexity , SIAM Journal on Numerical Analysis , and Journal of Computational Physics . His work on lattice rules, QMC methods, and high-dimensional integration has been widely cited and used in fields ranging from computational finance to theoretical physics. Projects and Funding: He leads or co-leads several ongoing and completed research projects funded by national and international agencies. These include projects on turbulence reconstruction from partial observations, uncertainty quantification for climate control in buildings, computational methods for infinite-dimensional Bayesian inversion, circularity improvements in scrap analysis, and ML-based sensitivity analysis. Software Development: He maintains and contributes to open-source software repositories for QMC point generation, including the Magic Point Shop and QMC4PDE projects. These provide efficient implementations of lattice and digital sequence generators in MATLAB, C++, and Python. Institutional Service: Beyond research, he contributes to academic governance through his roles in faculty and departmental councils, influencing curriculum development and strategic planning in engineering and computer science education at KU Leuven.

• Numerical Analysis
• Computational Mathematics
• High-dimensional Integration and Expectations
• +۸ مورد دیگر

Pieter Simoens is an Assistant Professor at Ghent University and affiliated with the imec research institute. He works at the intersection of distributed artificial intelligence, edge computing, and collective intelligence, with a focus on AI applications for resource-constrained environments and robotic systems. His research explores innovative approaches to machine learning deployment in heterogeneous infrastructures, task planning for IoT-integrated robotics, and modeling collective decision-making processes. He has contributed to frameworks like DIANNE for distributed deep learning and developed methods for cognitive modeling in reinforcement learning scenarios. With over 100 publications, his recent work spans adaptive neural networks, privacy-preserving surveillance, UAV hyperspectral data analysis, and computational fairness in AI systems. He leads research initiatives within the Internet Technology and Data Science Lab (IDLab) and contributes to educational programs in software engineering and applied machine learning. Responsible for courses on software engineering, mobile development, system design, and applied machine learning Active in edge computing and neuromorphic algorithms research Develops AI solutions for robotics, surveillance, and industrial IoT applications

• Distributed Artificial Intelligence
• Edge Computing
• Algorithms for Neuromorphic Computing
• +۲ مورد دیگر

Moharram Challenger is a tenure-track Assistant Professor in the Department of Computer Science at the University of Antwerp's Faculty of Sciences. Previously, he served as an assistant professor at Ege University (2017-2018) and as a post-doctoral researcher at the University of Antwerp (2019-2020) working on Flanders Make projects PACo and DTDesign. His academic journey includes R&D leadership roles at UNIT IT Ltd. (2012-2016), post-doctoral research at Wageningen University (2016-2017), and tenure-track faculty positions at IAU-Shabestar University (2005-2009). His research spans Cyber-physical Systems , Multi-agent Systems , and Domain-specific Modeling Languages , with recent publications focusing on quantum machine learning, digital twinning, and IoT optimization. Key projects include ITEA ModelWriter, ITEA Assume, and Flanders Make initiatives. His work demonstrates strong integration of model-driven engineering with emerging technologies like quantum computing and reinforcement learning. Challenger actively contributes to the academic community as a member of IEEE and ACM . His publication record shows consistent output across top venues, with 2025 featuring significant work in quantum-enhanced learning and CPS security. Current research emphasizes practical applications in drone energy modeling, medical diagnostics, and industrial IoT systems. His advising activities focus on cyber-physical systems and agent-based modeling, supported by grants from TUBITAK and Flanders Innovation & Entrepreneurship. Key collaborations include European ITEA projects and partnerships with industrial entities through UNIT IT Ltd. Challenger maintains active development through GitHub repositories related to code refactoring, model-driven engineering, and legacy system modernization, reflecting his commitment to practical software engineering solutions.

• Cyber-physical Systems
• Multi-agent Systems
• Domain-specific Modeling Languages
• +۵ مورد دیگر

Maxime Cordy is a researcher in Computer Science with a focus on Software Engineering and Formal Verification. He holds a PhD in Computer Science from the University of Namur, earned in 2014, and has engaged in visiting research at the University of Luxembourg (2017-2018). He also co-founded SkalUp as an R&D manager from 2015 to 2016. Education: Doctor of Science (University of Namur, 2014), Master in Computer Science (University of Namur, 2011) His research spans Software Product Lines , Model Checking , and Variability-Intensive Systems , emphasizing formal verification and automated analysis. He has contributed to over 53 research outputs with 949 citations and an h-index of 17. Recent publications include advancements in Featured Transition Systems , Mutation-Based Model Checking , and Machine Learning for Software Quality . He co-organized workshops like MaLTeSQuE 2019 and the Machine Learning and Software Engineering in Symbiosis workshop (2018). Scientific Awards: VAMOS 2024 Ten-Year Most Influential Paper Award (co-recipient) Maxime has collaborated extensively with institutions including the University of Luxembourg and co-authored works with leading researchers in formal methods and software engineering.

• Software Engineering
• Formal Verification
• Model Checking
• +۴ مورد دیگر

Herman Bruyninckx is a Professor at the Faculty of Engineering Sciences , KU Leuven , where he also serves as Vice-Chair of the Department of Mechanical Engineering and head of the Robotics, Automation and Mechatronics (RAM) subdivision. His research focuses on integrating formally represented domain knowledge into robotic systems for real-time, self-explanatory, and certifiable control. He advocates for open standards and software engineering practices in robotics, with a career-long emphasis on knowledge-driven robotic systems over data-driven approaches.

• Robotics
• Mechatronics
• Bayesian Sensor Processing
• +۱۰ مورد دیگر

Thibaut Vandervelden is a Researcher at Vrije Universiteit Brussel's Faculty of Engineering, Department of Electronics and Informatics. His work focuses on hardware security, embedded systems, and cryptographic implementations for resource-constrained environments. With an h-index of 4, he has produced significant research output since 2019, primarily in Sensors, IEEE Access, and Future Generation Computer Systems. His research interests center on practical security implementations for IoT devices and embedded systems, with particular expertise in Rust programming for secure embedded development, cryptographic protocol optimization, and network security for low-power wireless networks. His work bridges theoretical security concepts with practical implementation challenges in constrained environments. Analysis of his recent publications reveals a strong trend toward secure embedded systems development, with increasing focus on Rust programming language applications, zero-knowledge proofs for privacy-preserving systems, and optimization of cryptographic operations for IoT devices. His research demonstrates consistent collaboration with colleagues including Ruben De Smet, An Braeken, and Kris Steenhaut. Scientific recognition includes: IACR RWC'21 Cryptohackathon on Functional Encryption: 2nd prize (2021) Vandervelden actively participates in research community building through organizing workshops like RustIEC and presenting at events focused on embedded systems security. His work has been referenced in Wikipedia pages and garnered attention across academic and professional platforms, with media contributions on cybersecurity research in Flanders. He leads and participates in research teams focused on wireless community development and the Internet of Batteryless Things, contributing to both theoretical frameworks and practical implementations in hardware security.

• Hardware Security
• Internet of Things
• Embedded Systems
• +۵ مورد دیگر

Robrecht Blancquaert is a doctoral researcher at the Faculty of Engineering Sciences , Vrije Universiteit Brussel , Belgium. His work focuses on optimizing cryptographic algorithms for ARM processors and enhancing cybersecurity in embedded systems. Doctoral Scholarship, Electronics and Informatics Research Interests: Active in domains spanning cryptography , ARM processor optimization , benchmarking , and embedded systems security . His research intersects hardware-software co-design for secure communication protocols. Recent Publication: In 2024, co-authored an article on accelerating Elliptic Curve Cryptography for ARM architectures, addressing efficiency and security trade-offs in resource-constrained environments. Key Research Areas: Cryptographic algorithms, performance optimization, cybersecurity, and hardware-software integration for mobile platforms.

• Cryptography
• ARM Processors
• Benchmarking
• +۴ مورد دیگر