جستجوی استاد راهنمای دانشگاهی

Barbara J. Grosz serves as Higgins Research Professor of Natural Sciences in Harvard University's School of Engineering and Applied Sciences, renowned for foundational contributions to Artificial Intelligence through pioneering work in dialogue processing and multi-agent collaboration theories applied to human-computer interaction, healthcare coordination, and science education. Her research spans Artificial Intelligence, Natural Language Processing, Multi-agent Systems, and Computational Ethics, with recent focus on improving computer system design for healthcare and education. She co-founded Harvard's Embedded Ethics program, which integrates ethical reasoning into core computer science curricula, reflecting her commitment to responsible AI development. Professor Grosz has earned exceptional recognition including: ACM/AAAI Allen Newell Award (2009) IJCAI Award for Research Excellence (2015) ACL Lifetime Achievement Award (2017) Election to the National Academy of Engineering Membership in the American Philosophical Society Her leadership extends to establishing interdisciplinary research institutions and advancing women in science, demonstrating sustained impact across academia and societal applications of AI.

• Artificial Intelligence
• Natural Language Processing
• Multi-agent Systems
• +۴ مورد دیگر

Said Hamdioui serves as a full Professor in the Department of Computer Engineering within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. His research focuses on cutting-edge hardware architectures for neuromorphic computing and energy-efficient AI acceleration, with particular emphasis on memristor-based systems, emerging memory technologies, and fault-tolerant designs for edge applications. His research interests span Neuromorphic Computing , Memristor-Based Architectures , and Energy-Efficient AI Hardware , addressing critical challenges in hardware security, computation-in-memory, and reliable edge AI deployment. Recent work demonstrates significant advancements in RRAM/FeFET testing methodologies, spiking neural network implementations, and spin wave computing alternatives to traditional CMOS. His publications reveal strong trends toward real-world deployment of brain-inspired hardware with practical constraints like power efficiency, testability, and security. Award highlights include: DATE'20 Best Paper Award DFT'21 Outstanding Student Paper ETS 2021 Best Paper Award LATS 2018 & 2022 Best Paper Awards Professor Hamdioui actively contributes to the research community through editorial roles at IEEE Transactions on VLSI Systems , IEEE Design & Test , and Journal of Electronic Testing from 2017-2018. His leadership in multi-partner projects like CONVOLVE and NEUROKIT2E demonstrates strong industry-academia collaboration for edge AI solutions. Current work shows increasing focus on practical deployment challenges including in-field fault monitoring, security vulnerabilities in neuromorphic systems, and realistic brain simulation frameworks.

• Neuromorphic Computing
• Memristor-Based Architectures
• Energy-Efficient AI Hardware
• +۷ مورد دیگر

Geoffrey Nelissen is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology. He holds additional roles as a Research Scientist and Investigador Auxiliar at INESC TEC (Portugal), contributing to interdisciplinary research in real-time systems. His work focuses on schedulability analysis, parallel task execution, and real-time communication protocols, with applications in embedded systems and multicore architectures. Research Interests: Real-Time Scheduling Response Time Analysis Multi-core and Parallel Systems Time-Sensitive Networking (TSN) Formal Verification Recent work emphasizes schedule abstraction frameworks, memory contention analysis, and deterministic communication protocols. His research has received recognition through multiple best paper awards including ICESS 2021 and RTAS 2022. He actively contributes to conferences like RTSS and ECRTS while teaching courses on Real-Time Systems and Operating Systems. Collaborations span European institutions with focus on embedded systems, autonomous driving, and safety-critical applications. Current projects explore holistic approaches to WCRT analysis and resilient real-time communication architectures.

• Real-Time Systems
• Schedulability Analysis
• Multicore Platforms
• +۳ مورد دیگر

Bart J. Hengeveld is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Industrial Design department within the Faculty of Industrial Design. He leads the Future Everyday research cluster, focusing on the intersection of sound design and IoT systems. His work explores how embedded technologies can communicate through sound and tangible interaction in ways that enhance human experience. Education: BSc/MSc in Industrial Design Engineering, Delft University of Technology (2003) PhD in Industrial Design, TU/e (2011), with concurrent training in double bass at Codarts Conservatory Research Interests: Acoustic communication in IoT systems Tangible interaction design Social implications of ubiquitous computing Sound as a medium for human-technology interaction Recent Research Trends: Focus on multimodal interfaces combining sound/light/haptics Studies on sound design for healthcare and smart living environments Investigations into ethical and inclusive IoT soundscapes Awards: Two Teacher of the Year awards at TU/e, reflecting his commitment to pedagogical excellence. Advising & Projects: Supervised 113 student projects including interactive installations like 'Naktis' and 'Sonostapp' Leading the €1.5M 'Role of Sound in Hybrid Work Environments' project (2023–2026) Organized TEI 2016 conference and serves on its steering committee Teams/Labs: Core member of TU/e's Future Everyday cluster, collaborating with multidisciplinary teams on embodied interaction research.

• Internet of Things (IoT) Interaction Design
• Sound Design
• Tangible Interaction
• +۳ مورد دیگر

Anuj Pathania serves as an Assistant Professor in the Parallel Computing Systems (PCS) group within the Informatics Institute at the University of Amsterdam's Faculty of Science. His research pioneers sustainable computing systems operating under severe power, thermal, and reliability constraints, with significant contributions to energy-efficient hardware design and embedded systems. Education: PhD in Computer Science (2018), Karlsruhe Institute of Technology MSc in Computer Science (2012), National University of Singapore B.Tech in Computer Science (2009), Maharaja Agrasen Institute of Technology Pathania's research centers on low-power design and sustainable systems for constrained environments, with particular expertise in thermal management of 3D-stacked architectures and energy-efficient machine learning inference . His work bridges electronic design automation with real-world reliability challenges, developing novel power budgeting techniques like T-TSP that incorporate transient temperature effects ignored by conventional methods. Current projects include EU-funded initiatives on energy labeling for digital services, addressing ecological impacts through technological, behavioral, and legal frameworks. His publication trajectory reveals a strategic evolution toward zero-waste computing , with recent work (2023-2025) focusing on hardware-software co-design for edge AI, energy modeling across computing continua, and parameter-efficient neural adaptation. Key themes include thermal-aware scheduling for S-NUCA many-cores, cooperative processor utilization in heterogeneous systems, and sustainability metrics for digital services. Scientific Recognition: Best Paper Award Nomination at IEEE Computer Society Annual Symposium on VLSI 2023 for 3D-TTP power budgeting technique Pathania actively mentors 4 PhD students (Ehsan Aghapour, Saeedeh Baneshi, Sudam Wasala, Yixian Shen) and has successfully supervised 5 Master's theses (including Cum Laude defenses by Joris op ten Berg and Jurre Wolff). His research is supported by major grants including Energy Labels for Ecologically Sustainable Digital Services (2023-2024) and Towards Zero-Waste Computing (2021-2025), developing simulation frameworks like HotSniper and CoMeT for thermal analysis. The PCS group maintains strong industry collaborations with ARM and NVIDIA, particularly through tools like ARM-CO-UP for heterogeneous processor utilization.

• Low Power Design
• Sustainable Systems
• Electronic Design Automation
• +۴ مورد دیگر

Prof. Miro Zeman serves as a Professor in the Photovoltaic Materials and Devices research group within the Department of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. With an extensive publication record of 559 research outputs including articles, conference contributions, patents, and datasets, he maintains active leadership in photovoltaic research. His research spans photovoltaic materials science , solar cell engineering , and renewable energy systems . Specializing in crystalline silicon solar cells, thin-film technology, and tandem cell configurations, his work addresses fundamental semiconductor physics alongside practical implementation challenges. Recent research focuses on impedance spectroscopy of PN junctions, defect analysis in transition metal oxides, and integration of power electronics with photovoltaic modules. Zeman's publication portfolio reveals consistent contributions to high-impact journals including Solar Energy Materials and Solar Cells , ACS Applied Energy Materials , and EPJ Photovoltaics . The research demonstrates strong interdisciplinary connections between semiconductor physics, materials engineering, and energy system integration, with particular emphasis on improving efficiency metrics and practical implementation of photovoltaic technologies across diverse environmental conditions. His research group has produced 37 supervised academic works and maintains active collaboration with industry and academic partners globally, as evidenced by the extensive network visualizations in his profile. The Photovoltaic Materials and Devices laboratory at TU Delft serves as the primary research environment for his team's investigations into next-generation solar technologies.

• Photovoltaics
• Solar Cell Engineering
• Thin Film Technology
• +۵ مورد دیگر

Hugh Greatorex serves as a Researcher at the University of Groningen within the Faculty of Science and Engineering, affiliated with the Bio-inspired Circuits & Systems research group. His work centers on neuromorphic hardware design, specializing in spiking neural networks and event-based processing systems that prioritize energy efficiency and real-world applicability in robotics and embedded AI. His research spans critical areas in neuromorphic engineering: Neuromorphic Engineering Spiking Neural Networks CMOS Circuit Design Memristive Computing Event-based Vision and Audio Processing Symbolic Computation in Neural Hardware Greatorex integrates circuit design with neural algorithms to solve power efficiency challenges, frequently exploring beyond-CMOS technologies like memristors and multi-timescale neural dynamics for practical implementations. Analysis of his 14 recent publications (2022-2025) reveals a cohesive trajectory toward hybrid neuromorphic processors combining CMOS with emerging devices. Key contributions include the TEXEL and Fused-MemBrain architectures for beyond-CMOS integration, alongside advancements in event-based sensory processing and symbolic operations within spiking hardware. This work consistently bridges neural computation with symbolic AI paradigms while emphasizing scalability and robustness. No scientific awards were documented in available sources. Information regarding student advisement and research grants was not provided in the source materials. As a core member of Groningen's Bio-inspired Circuits & Systems group, Greatorex contributes to internationally collaborative projects focused on biologically inspired electronic systems for intelligent processing, with applications spanning edge AI and neural computation modeling.

• Neuromorphic Engineering
• Spiking Neural Networks
• CMOS Circuit Design
• +۳ مورد دیگر

Sajid Mohamed is a guest researcher in the Electronic Systems group at the Department of Electrical Engineering, Eindhoven University of Technology. His work focuses on model-based design of image-based control systems tailored for resource-constrained domains like semiconductor manufacturing and automotive industries. PhD, M.Tech, and B.Tech degrees from TU/e, IIT Kharagpur, and NIT Calicut respectively His research combines expertise in Multiprocessor Systems , Deep Neural Networks , and Motion Control to address challenges in real-time performance and optimization. Key contributions include the IMOCO4.E reference framework and methodologies for vision-based DNN deployment on legacy hardware . Recent publications highlight advancements in Motion Control through multi-rate sensor fusion , DNN-driven visual perception , and predictable multi-core implementations . His work aligns with the UN Sustainable Development Goals, particularly in technological innovation for industrial efficiency. Best Paper Award at ECYPS (2019) Professionally, he serves as a Principal Software Engineer at ITEC B.V., integrating academic research with industry applications. Collaborations include projects like FitOptiVis and oCPS , emphasizing cyber-physical systems and edge computing.

• Image-Based Control Systems
• Multiprocessor Systems
• Motion Control
• +۷ مورد دیگر

Prof. A.D. Pimentel holds a full professorship at the Informatics Institute of the University of Amsterdam, leading the Parallel Computing Systems (PCS) group within the Systems and Networking Lab. His research focuses on multi-core and multi-processor systems, emphasizing performance, energy efficiency, dependability, and productivity in system design and runtime management. He earned his PhD and MSc in Computer Science from the University of Amsterdam in 1998 and 1993, respectively. Current roles: Chair of PCS group, Board member of Advanced School for Computing and Imaging (ASCI), and ICT Research Platform Nederland (IPN) Teaching: Courses on Multi-core Processor Systems, Embedded Software, and Architecture Research interests span edge AI, sustainable computing, and system-level modeling. Recent work includes innovations in energy-efficient scheduling, thermal management in 3D-stacked systems, and adaptive CNN inference at the edge. Over 25 years of contributions to embedded systems design space exploration and hardware/software co-design have been recognized through awards like the IEEE CEDA Outstanding Service Award (2025). Awards: IEEE DATE Fellow (2025), NWO Knowledge & Innovation Covenant grant lead Active in conference organization, serving as General Chair for Embedded Systems Week (2026) and Design Automation and Test in Europe (DATE 2024). Engages in cross-disciplinary projects like improved secure semiconductor evaluation (ISSE) and energy labeling for digital services.

• Parallel Computing Systems
• Multi-core Embedded Systems
• System-level Design
• +۵ مورد دیگر

Marcel van Gerven serves as Professor of Artificial Intelligence at Radboud University, leading the Artificial Cognitive Systems laboratory within the Donders Institute for Brain, Cognition and Behaviour. He holds dual Principal Investigator roles at both the Donders Centre for Cognition and the Donders Institute, while directing the ELLIS Unit Nijmegen as an ELLIS Fellow. His research program bridges artificial and natural intelligence through machine learning and neuromorphic computing, with core expertise in neural networks, brain-computer interfaces, and neuroprosthetics for vision restoration. Current work develops brain-inspired AI systems that enhance computational efficiency while modeling biological neural processes, particularly focusing on cortical stimulation safety and real-time adaptive systems. Analysis of his 2025 publications reveals dominant themes in reinforcement learning for neuroprosthetics, anomaly detection frameworks, and spiking neural network applications. His work consistently integrates medical applications including epilepsy regulation, immunotherapy diagnostics, and prosthetic vision enhancement, demonstrating strong translational impact from fundamental AI research to clinical solutions. Scientific recognition includes: Vidi laureate from the Dutch Research Council ELLIS Fellowship for European AI leadership Professor van Gerven directs significant research funding through national and international grants, including the Vidi award. His laboratory develops specialized frameworks like Abmax and Kozax for agent-based modeling while mentoring students in cognitive AI systems, though specific advisees aren't documented in available sources. The Artificial Cognitive Systems lab operates within the Donders Institute ecosystem, collaborating closely with the ELLIS Unit Nijmegen to advance European neuromorphic computing research. Current initiatives focus on biologically plausible learning rules, efficient neural network architectures for embedded systems, and closed-loop neuroprosthetic control systems.

• Machine Learning
• Neural Networks
• Neuromorphic Computing
• +۳ مورد دیگر

Prof. W.P.M.H. (Maurice) Heemels is a Full Professor at the Eindhoven University of Technology in the Mechanical Engineering department, leading the Control Systems Technology group. His work focuses on hybrid systems , networked control , and model predictive control (MPC) with applications in cyber-physical systems , precision agriculture , and cancer treatment optimization . PhD in Mathematics (1999) and MSc in Mathematics (1995) from TU/e Assistant Professor at TU/e Electrical Engineering (2000-2004) Research Fellow at Embedded Systems Institute (2004-2006) His research addresses resource-aware control strategies to replace traditional time-triggered systems with event-triggered schemes , enabling efficient use of computational and communication resources. Applications span automated driving , precision agriculture , thermal control in lithography , and nuclear fusion reactors . He has developed innovative hybrid integrator-gain systems (HIGS) and reset controllers to overcome limitations of linear time-invariant controllers. Model predictive control is a core methodology, applied to hyperthermia treatments, wafer scanner thermal control, and drone-based farming systems. His 2025 publications emphasize networked control for nuclear fusion, safety-critical MPC in autonomous vehicles, and data-driven control barrier functions for nonlinear systems. Key trends include hybrid systems theory , resource-aware control , and scalable optimization for large-scale applications. 2025: DISC PhD Award for Stefan Heijmans 2019: EECI European PhD Award for Victor Dolk 2017: Best Paper Award @ EBCCSP Conference 2014: Automatica Outstanding Service Award 2000: ASML-award for best fundamental PhD thesis He has supervised 88+ students and secured over 7 MEuro in research funding from NWO, EU, and industry. His Control Systems Technology group operates labs including the Automotive Engineering Science Laboratory and Robotics Laboratory , contributing to the UN Sustainable Development Goals for sustainable cities and climate action through cyber-physical systems.

• Hybrid Systems
• Networked Control Systems
• Model Predictive Control (MPC)
• +۵ مورد دیگر

Matthias Volk is an Assistant Professor in the Formal System Analysis group at Eindhoven University of Technology (TU/e). His research focuses on improving the safety and reliability of complex systems through formal methods, particularly probabilistic model checking. He develops automated techniques for system modeling and analysis, with a focus on industrial applications. He is a core developer of the Storm model checker. Academic Background: B.Sc. and M.Sc. in Computer Science from RWTH Aachen University Ph.D. from RWTH Aachen University, supervised by Joost-Pieter Katoen Postdoctoral researcher at the University of Twente Joined TU/e as an Assistant Professor in 2023 Research Interests: Probabilistic model checking Safety-critical systems Automated verification tools Industrial collaborations for formal methods Grants and Funding: NWO grant NWA.1160.18.238 (PrimaVera) ERC Consolidator Grant 864075 (CAESAR) EU Horizon 2020 project MISSION (101008233) Labs/Teams: Part of the Formal System Analysis group and Algorithms and Logics for Verification group at TU/e.

• Formal Methods
• Probabilistic Model Checking
• Safety-Critical Systems
• +۳ مورد دیگر

Martijn L.P.J. Koedam is a researcher at the Electronic Systems department within the Electrical Engineering school at Eindhoven University of Technology . His work focuses on embedded systems, multi-processor architectures, and resource utilization optimization in real-time environments. Research areas: Embedded Systems, Multi-Processor Systems, Real-Time Systems External role: Founder/R&D at Verintec Solutions (since 2018) His research explores predictable and composable platforms for cyber-physical systems, emphasizing dynamic environments, quality-sensitive applications, and model-driven design. Projects like IMOCO4.E , TRANSACT , and I-MECH highlight collaborations with experts such as Kees Goossens and Sander Stuijk. Recent publications address resource utilization in multi-rate control systems, deployment frameworks for dynamic environments, and composable ROS2 architectures for robotics. These works intersect with disciplines like computer science, signal processing, and cyber-physical systems. He contributes to projects like COMP4DRONES and I-MECH , focusing on real-time systems and model-driven design. His external position at Verintec Solutions aligns with his technical expertise in embedded systems and MedTech innovation.

• Embedded Systems
• Multi-Processor Systems
• Real-Time Systems
• +۳ مورد دیگر

Mojtaba Haghi is a post-doctoral researcher at Eindhoven University of Technology (TU Eindhoven), affiliated with the Department of Electrical Engineering . He is actively involved in the Embedded Control Systems Lab , CompSOC Lab - Predictable & Composable Embedded Systems , and the High Tech Systems Center . His research focuses on co-designing control applications for resource-constrained embedded platforms, particularly targeting multi-core hardware architectures. PhD in Electrical Engineering (2025) from TU Eindhoven MSc in Electrical Engineering from AmirKabir University of Technology His work addresses critical challenges in: Resource utilization optimization in embedded control systems Multi-rate controller design for predictable platforms Hardware/software co-design for industrial applications Parallel computing in model predictive control algorithms Recent publications demonstrate expertise in: Water grid model predictive control frameworks GPU-accelerated optimization algorithms Embedded system performance verification Industrial motion control applications He collaborates with research groups including: EAISI (Eindhoven AI Systems Institute) MedTech Innovation Center

• Embedded Control Systems
• Model Predictive Control
• Multi-core Platforms
• +۴ مورد دیگر

Kanishkan Vadivel is a Researcher in the Electronic Systems group at Eindhoven University of Technology (TU/e), specializing in energy-efficient hardware architectures and compiler-based code-generation techniques. He holds a Master’s degree in Embedded Systems from TU/e (2017) and a Bachelor’s from Coimbatore Institute of Technology (India). Prior to academia, he worked in embedded systems at Tata Engineering and Arm Ltd. Research Interests : His work focuses on computation-in-memory architectures using resistive devices, optimal code generation for CGRA (Coarse-Grained Reconfigurable Architecture), and high-performance computing. Key projects include the MNEMOSENE initiative and development of the CIM-SIM simulator for computation-in-memory systems. Awards : HiPEAC collaboration grant (2019) Advising & Grants : His research is supported by grants focused on neuromorphic processors and edge-AI hardware. He collaborates on projects like NEUROKIT2E for embedded deep learning systems. Labs/Teams : Active member of TU/e’s Electronic Systems Center and Efficient Stream Processing Lab, contributing to neuromorphic and energy-efficient computing initiatives.

• Energy-Efficient Computing
• High-Performance Computing
• Compiler Techniques
• +۴ مورد دیگر