Find Academic Supervisors

• System Security
• Fault Injection
• Fuzzing
• +5 more

Erik van der Kouwe is an Assistant Professor at the Faculty of Science, Vrije Universiteit Amsterdam, holding dual appointments in the Computer Systems department and the Network Institute. His research focuses on system security, fault injection, fuzzing, and embedded systems security. He contributes to both theoretical advancements and practical implementations of security mechanisms. Key research areas include mitigating vulnerabilities through hardware-software co-design, exposing compiler optimization flaws that evade sanitizers, and developing efficient fuzzing frameworks for malware analysis. His work often bridges academic research with real-world applications in embedded systems and operating systems. Recent publications highlight innovations like InvisiGuard for microcontroller security, hwdbg for hardware debugging frameworks, and Enviral for environment-aware malware analysis. His research has been published in top-tier venues such as IEEE Transactions, ACM conferences, and workshops like EuroSEC. Teaching responsibilities include Advanced Operating Systems, Secure Programming, and Software Security courses. Collaborations span international teams working on topics like compiler hardening, embedded system resilience, and vulnerability benchmarking.

Venkatasubraman Kalpathy VenkiteswaranView profile

Assistant Professor

• Robotics
• Soft Robotics
• Medical Devices
• +3 more

Venkatasubraman Kalpathy Venkiteswaran is an Assistant Professor in the Biomedical Device Design and Production Technology field. His research focuses on soft robotics , magnetic actuation , and biomedical applications . He explores topics such as variable stiffness mechanisms, medical devices, and biomimetic systems. He has contributed to advancements in magnetic soft robots, including multi-layer printing techniques and adaptive locomotion systems. His work integrates materials science with robotics, emphasizing applications in healthcare and minimally invasive interventions. Collaborations span global institutions, reflecting his interdisciplinary approach. Notable research includes the APOLLO project for endovascular interventions and fin-wave-inspired amphibious robots. His publications highlight innovations in actuation, modeling, and material design for robotic systems.

Marwan HassaniView profile

Assistant Professor

• Process Mining
• Data Mining
• Unsupervised Learning
• +7 more

Marwan Hassani is an Assistant Professor at Eindhoven University of Technology's Department of Mathematics and Computer Science, affiliated with the Process Analytics and EAISI Foundational groups. He leads the streaming process mining research group and the Customer Journey track at the Data Science Center Eindhoven (DSC/e). His academic background includes a PhD from RWTH Aachen University (2015) and postdoctoral research there until 2016. Research focuses on unsupervised learning methods for streaming event data, including clustering, outlier detection, and sequential pattern mining, with applications in customer journey optimization and real-time process analytics. Over 65 publications span data mining, process mining, and related areas. He serves on program committees for ECML/PKDD, SDM, and journals like KAIS and DAMI, co-chairing multiple events. Key technical contributions include PrefixCDD for concept drift detection, BFSPMiner for sequential pattern mining, and autoencoder-based anomaly detection systems. His work addresses GDPR compliance in business processes and traffic flow prediction using contrastive learning. Awards include a 2025 Best Paper Award at ACM SIGAPP. Education: PhD in Computer Science (RWTH Aachen, 2015) Projects: Led GDPR compliance project (2018-2021), developed BPR4GDPR framework Teaching: Advanced Process Mining, Foundations of Data Analytics Editorial roles: Guest editor for Data & Knowledge Engineering , editorial board member for Process Science Research aligns with UN SDGs through contributions to efficient resource management and privacy-preserving analytics.

Vincent GroenhuisView profile

Researcher

• Medical Robotics
• MRI-Guided Surgical Interventions
• Stepper Motor Technology
• +3 more

Vincent Groenhuis is a postdoctoral researcher at the Robotics and Mechatronics (EEMCS-EE-RAM) group , University of Twente. He holds a BSc, MSc, and PhD in Computer Science, Embedded Systems, and Medical Robotics respectively, all cum laude. His work focuses on image-guided medical robotics for bladder cancer diagnosis (Next-gen In-Vivo project) and MRI/ultrasound-guided breast biopsy (Sunram/MURAB projects), alongside innovations in pneumatic and electric stepper motors with two patents spun into companies. Education : BSc Computer Science (cum laude), MSc Embedded Systems (cum laude), PhD Medical Robotics (cum laude) Key Collaborations : Hamlyn Surgical Robot Challenge, European Robotic Forum, ICRA, BioRob, IROS His research integrates 3D-printed actuators with MRI safety protocols, enabling compact, reconfigurable systems for endovascular interventions and biopsy procedures. Article trends emphasize sensorless positioning , pneumatic computation , and multi-axis motor design . Awards include Best Video (Sunram 4, Hamlyn 2017) and Best Design (Sunram 3, Hamlyn 2016) . Supervised student projects span ultrasound needle steering , auto-rewind spool holders , and emergency disengagement mechanisms .

• Multicore Computing
• Program Analysis
• Virtual Machine Optimization
• +4 more

Daniele Bonetta is an Assistant Professor in the Department of Computer Science at Vrije Universiteit Amsterdam and holds an ancillary role as a Medewerker (Employee) at Eindhoven University of Technology since June 2020. His primary affiliation is with the Faculty of Science, where he contributes to the Network Institute as well. His research focuses on optimizing virtual machines, parallel programming models, and dynamic compilation techniques, with a particular emphasis on multicore systems and distributed computing environments. Bonetta has also been involved in teaching advanced courses such as Advanced Network Programming and contributes to the Accelerator-Centric Computing Ecosystems program. His research interests are centered around improving the performance of managed runtimes, including virtual machine optimization, dynamic taint analysis, and efficient data processing in polyglot environments. He has explored topics such as speculative optimizations for JSON data access, columnar array storage transformations, and scalable solutions for virtual memory oversubscription. His work frequently addresses challenges in distributed systems, cloud computing, and cross-language program analysis. Bonetta’s recent publications (2023-2025) highlight advancements in transparent scale-out mechanisms for virtual memory, automated supernode generation in interpreters, and dynamic query engines embedded in polyglot runtimes. His contributions to the field include both theoretical frameworks and practical implementations, often leveraging the GraalVM and Truffle frameworks for polyglot execution. While no formal awards are listed, his extensive publication record (47+ outputs) demonstrates significant scholarly impact. His teaching portfolio includes courses on network programming and systems architecture, reflecting his dual focus on both theoretical research and applied computer science education.

• Energy-Efficient Computing
• High-Performance Computing
• Compiler Techniques
• +4 more

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.

• Robotics
• Machine Learning
• Computer Vision
• +11 more

Dr. Hamidreza Kasaei is an Associate Professor in the Department of Artificial Intelligence at the University of Groningen, Netherlands. He leads the Interactive Robot Learning Lab (IRL-Lab) , focusing on robotics, machine learning, and computer vision. Develops algorithms for lifelong interactive robot learning Specializes in 3D perception and multi-arm manipulation Actively involved in IEEE RAS as Associate Editor His research directions include: Perception systems for dynamic environments Dual-arm manipulation techniques Lifelong learning architectures Multimodal integration for object understanding Dynamic motion planning for reactive systems Recent work trends show emphasis on: Vision-language model integration for open-world grasping Dual-arm reinforcement learning frameworks Neural ODE applications for video generation Ensemble methods in continual learning Imitation learning for agricultural robotics Transformer-based view planning systems Scientific awards include: Google Research Scholar Award (2023) Outstanding Associate Editor - IEEE RAS (2023) His lab actively trains students through PhD and Master's projects in domains like 3D object perception, dual-arm coordination, and multimodal learning. The lab has successfully graduated multiple PhD candidates including Zhenxing Zhang and Hamed Ayoobi .

• Neuromorphic Engineering
• Edge AI
• Computer Architecture
• +4 more

Dr. A. Yousefzadeh is an Assistant Professor in Edge AI at the University of Twente (joined February 2024), affiliated with the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) within the Department of Computer Architecture Design and Test for Embedded Systems. He holds a Ph.D. in Neuromorphic Engineering from IMSE (Instituto de Microelectrónica de Sevilla), where his thesis focused on bio-inspired vision processing. His research specializes in neuromorphic computing systems, with emphasis on: Designing ultra-low-power AI processors and event-based vision systems Developing hardware accelerators for spiking neural networks (SNNs) Edge AI deployment for sensor-based applications Hardware-software co-design for energy-efficient computing His publication trends (2015-2025) reveal core foci on neuromorphic processor architectures (e.g., SENECA, NeuronFlow), event-based vision processing, hardware-aware neural network optimization, and 3D integration techniques. Recent work explores activation sparsification in transformers and hybrid analog-digital neuromorphic systems. Prior to academia, he contributed to industry neuromorphic projects: Architected the NeuronFlow processor at GrAI Matter Labs (acquired by Snap) Led SENECA processor development at imec's Hardware Efficient AI group He currently leads research on next-generation edge AI processors at UT's Embedded Systems lab.

Stefan ManegoldView profile

Professor

• Database Systems
• Column-store Database Architectures
• Data Management for Sciences
• +7 more

Stefan Manegold is a Professor for Data Management (0.2 fte) at Leiden University's Faculty of Science within the Leiden Institute of Advanced Computer Science (LIACS), while also serving as a Senior Researcher (0.8 fte) and former Head (2011-2024) of the Database Architectures Research Group at Centrum Wiskunde & Informatica (CWI) in Amsterdam. His career spans over 27 years at CWI and 11 years as a professor at Leiden University, with prior experience at Humboldt-Universität zu Berlin. Professor Manegold's research focuses on innovative database architectures, particularly column-store systems and hardware-aware database technologies. His expertise spans database query optimization, parallel and distributed information systems, XML storage and processing, and scientific data management. He has pioneered work in adaptive indexing, progressive query processing, and main-memory database systems that leverage modern hardware capabilities. His research bridges theoretical database concepts with practical implementations, as evidenced by his involvement in the MonetDB open-source database system. His work shows a clear evolution from foundational database research toward addressing modern challenges in big data management, scientific data processing, and interactive analytics. Recent publications demonstrate his continued leadership in database indexing techniques, GPU-accelerated database operations, and geospatial data management. Professor Manegold has received significant recognition for his contributions to the database community, including the prestigious 2020 ACM SIGMOD Contributions Award, the VLDB'2011 Challenges & Visions Track Best Paper Award, and the VLDB'2009 10-year Best Paper Award. His work has had substantial impact on both academic research and practical database system design. He has been actively involved in the academic community through conference organization, particularly with SIGMOD and VLDB events, and has contributed to numerous workshops including the Data Management on New Hardware (DaMoN) series. His leadership extends to collaborative research projects such as SciLens, PROMIMOOC, and DAMIOSO, which address data management challenges in scientific domains. Professor Manegold leads the Database Architectures Research Group at CWI, which has been at the forefront of database system research for decades. The group's work on MonetDB has influenced modern column-store database systems and continues to push boundaries in areas like progressive query processing and hardware-aware database design.

Sherif EissaView profile

Researcher

• Machine Learning
• Hardware Design
• Data Encoding
• +4 more

Sherif Eissa is a PhD Candidate in the Electronic Systems group at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e). His research focuses on neuromorphic computing for efficient real-time AI through hardware design, under the supervision of Prof. Henk Corporaal and Prof. Sander Stuijk. He is part of the national research project efficientdeeplearning.nl . Education: Bachelor of Information Engineering (cum laude), German University in Cairo (2016), with thesis at the Institute for Microelectronics Stuttgart (IMS) and University of Stuttgart. Master of Information Technology and Embedded Systems (cum laude), University of Stuttgart (2019), with thesis at Bosch Research Campus, Renningen. Research Interests: Machine Learning, Hardware Design, Data Encoding, Parallel Data Processing, Memory Structures, and Sparsity Utilization for Low-Power Edge AI. Awards: Best Achieving Student in overall grades (Bachelor's Degree). Best Achieving Student in overall grades (Master's Degree). Advising and Projects: Supervised 3 research works. Principal project: Efficient Deep Learning Platforms (eDLP) (2018–2023), focusing on Deep Learning Method, Energy Efficiency, and Hardware Platforms. Labs and Teams: Member of the Efficient Stream Processing Lab and the Electronic Systems group at TU/e.

• Distributed Systems
• Cloud Computing
• High-Performance Computing
• +3 more

Thilo Kielmann is an Associate Professor at the Vrije Universiteit Amsterdam, holding positions in the Faculty of Science (Computer Systems department) and the Network Institute. His research focuses on distributed systems, cloud computing, and high-performance computing, with emphasis on resource management, data locality, and scalable infrastructure. He has authored 95+ research outputs and supervised 8 PhD theses. Education details are not explicitly listed, but his academic role implies advanced qualifications in computer science. His work contributes to UN Sustainable Development Goals through innovative computing solutions. Research interests include distributed file systems (e.g., MemEFS), resource disaggregation, energy-efficient scheduling (e-BaTS), and scalable VM management. His publications span conferences like HPDC and journals like Future Generation Computer Systems. No scientific awards are explicitly mentioned. He teaches four courses, including Computer Programming and Large Research Project in Computer Science. Labs/teams involvement is not detailed, but his research collaborations and conference roles (e.g., HPDC15 Chair) suggest active participation in academic networks.

Sander StuijkView profile

Associate Professor

• Embedded Systems
• Signal Processing
• Multi-Core Architectures
• +3 more

Sander Stuijk is an associate professor at the Department of Electrical Engineering of Eindhoven University of Technology, chairing the Electronic Systems (ES) group. His research focuses on design methodologies for embedded signal processing applications in high-tech systems like industrial manufacturing, automotive, and healthcare. He develops high-level compilation strategies for heterogeneous multi-core platforms, with interests in efficient code generation and resource allocation. Prof. Stuijk holds leadership roles including chairing the ES group, serving on the board of 4TU.NIRICT (a Dutch ICT research consortium), and coordinating the Embedded Systems master program. He contributes to national ICT initiatives through ICT Next Generation, a network for mid-career academics. He also chaired the Department Council (2016-2018) and participates in TPC activities. His research spans streaming applications, real-time systems, and multi-processor architectures. Notable projects include the PROMES initiative (programming embedded multi-media systems) and the MNEMEE project (automated MPSoC design). His work emphasizes predictable and energy-efficient computing, with applications in healthcare monitoring (e.g., remote PPG, thermal imaging) and reconfigurable systems. Prof. Stuijk has advised numerous PhD/Master students and led projects like FORSEE, VSM, and SenSafety. His publications include over 70 journal/conference papers, covering topics like neuromorphic computing, GPU optimization, and medical signal processing. He actively engages in academic service, including organizing SCOPES workshops and serving on editorial boards.

Gabriele KellerView profile

Professor

• Functional Programming
• Type Systems
• High-Performance Computing
• +3 more

Prof. Gabriele Keller is a Professor of Software Technology at Utrecht University's Faculty of Science. She previously held roles at the University of New South Wales, including Associate Professor (2014–2018) and Senior Lecturer (2001–2013). Her research focuses on functional programming, type systems, high-performance computing, and verification methodologies. Current projects include Accelerate (a parallel computing DSL embedded in Haskell) and Cogent (a systems programming language with formal verification features). Education: PhD in Natural Sciences (Technische Universität Berlin, 1999): 'Efficient Compilation of Nested Data-Parallelism for Distributed Memory Machines' MSc Computer Science (Technische Universität Berlin, 1995) Research Interests: Type systems and correctness guarantees Parallel computing and GPU programming Formal verification of systems software Domain-specific languages for high-performance domains Professional Activities: Co-Chair, IPN Working Group on Equity, Diversity & Inclusion Editor, Journal of Functional Programming Member, IFIP Working Group 2.8 Selected Projects: Accelerate: Optimized parallel computing for Haskell Cogent: Verified systems programming with uniqueness types EmoSTL: Formal verification of game emotion logic

Odysseas PapapetrouView profile

Assistant Professor

• Streaming Data Processing
• Big Data Management
• Approximation Algorithms
• +5 more

Odysseas Papapetrou serves as Assistant Professor in the Information Systems group within the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). His academic journey spans multiple prestigious institutions across Europe, establishing him as a significant contributor to the field of data management and stream processing. Academic Credentials: BSc in Computer Science from University of Cyprus MSc in Advanced Information Technologies from University of Cyprus MSc in Computer Science from University of Saarland and Max Planck Institute (2005) PhD in Computer Science from University of Hannover and L3S Research Center (2011) Dr. Papapetrou's research focuses on cutting-edge challenges in data-intensive systems, with particular emphasis on real-time processing of streaming data. His work develops approximation algorithms for distributed Big Data management, distributed data streams processing, and sketching/summarization techniques. He has made significant contributions to cloud computing aspects including cloud federations, cloud-based stream processing, and privacy-preserving query execution. His research bridges theoretical foundations with practical implementations, addressing the growing need for efficient data processing in high-velocity environments. Analysis of his recent publications reveals a consistent focus on multivariate data analysis, with increasing specialization in spatial data streams and advanced similarity search techniques. His work demonstrates a progression from foundational stream processing to increasingly complex multivariate scenarios, reflecting the evolving challenges in Big Data analytics. The publications show strong collaboration patterns with researchers across Europe, particularly in the Netherlands and Switzerland. Dr. Papapetrou actively contributes to the academic community through service roles including frequent participation as PC member and reviewer for top-tier data management conferences and journals such as TKDE and VLDB Journal. He serves as a grants evaluator for the Hong Kong Research Grants Council (RCG) and other funding schemes. His current educational activities at TU/e include teaching Capita Selecta Databases, Seminar Datamanagement, Data-intensive Systems and Applications, Big Data Management, and Datamodeling and Databases. He currently leads research within the Database Group at TU/e, focusing on developing novel techniques for stream processing and Big Data management. His work has received funding from the European Union's Horizon Europe research and innovation program STELAR under grant agreement No. 101070122, supporting his research on advanced data stream processing techniques.

Georgi GaydadjievView profile

Professor

• Reconfigurable Computing
• Advanced Computer Architectures
• Parallel Memory Systems
• +7 more

Georgi Gaydadjiev is a Professor in Innovative Computer Architecture at the University of Groningen's Faculty of Science and Engineering. Previously, he held roles including Chair Professor at TU Delft and Chalmers University of Technology, and served as VP of Dataflow Software Engineering at Maxeler Technologies. He holds a PhD from TU Delft and has over 35 years of industry and academic experience, focusing on reconfigurable computing, high-performance systems, and energy-efficient architectures. His research spans embedded systems, fault tolerance, and scalable architectures. Education: MSc Electrical Engineering (TU Delft), PhD (TU Delft), studies at Voenmeh (Baltic State Technical University). Research interests include reconfigurable computing, advanced architectures, parallel systems, and HPC. He leads projects funded by EU, Google, and Swedish Research Councils, addressing exascale computing and customized hardware. His work has been recognized with awards like the CES Design Showcase (1999) and best paper awards at ICS'10 and WiSTP'07. He advises PhD students and oversees labs like Maxeler IoT-Labs BV, focusing on deploying dataflow technology beyond data centers.