Gábor Závodszky is an academic researcher at the University of Amsterdam's Faculty of Science, Mathematics and Computer Science within the Informatics Institute. His work primarily focuses on computational modeling of biological systems, particularly in the realm of hemodynamics and thrombosis. He specializes in developing advanced numerical methods like the lattice Boltzmann approach for simulating complex fluid-structure interactions in vascular systems. His research integrates multiscale modeling techniques to study blood flow dynamics, platelet behavior, and clot formation under various physiological conditions. Key contributions include the development of the HemoCell framework, a high-performance computational tool for simulating cellular-level blood flow phenomena. He has collaborated extensively on projects involving cerebral aneurysm hemodynamics, flow diverter stent efficacy, and the impact of diabetes on blood rheology. Závodszky's work bridges computational science with clinical applications, aiming to improve understanding of vascular pathologies and support medical device optimization. His research has been published in top-tier journals and conference proceedings, reflecting his expertise in both theoretical and applied computational biology.
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.
Prof. dr. H.B. (Ria) Braam is a Professor in Theoretical Chemistry at the University of Groningen, affiliated with the Zernike Institute for Advanced Materials within the Faculty of Science and Engineering. Her research focuses on computational and theoretical methods to study molecular electronic structure, with a particular emphasis on non-orthogonal configuration interaction (NOCI) for large molecular systems. She leads projects on singlet fission, exciton dynamics, and electronic coupling in organic materials. Her expertise includes quantum chemistry, molecular modeling, and high-performance computing. Key contributions include the development of the GronOR software for NOCI calculations and studies on dielectric properties of organic semiconductors. Braam holds additional roles such as Member of the FWO Chemistry Committee and Editorial Board of the International Journal of Quantum Chemistry. Recent research highlights include advancements in NOCI methodologies for singlet fission mechanisms, triplet diffusion in molecular materials, and computational strategies for energy transport. Her work bridges fundamental theory with applications in materials science and photovoltaics.
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.
Lars van den Haak is a Lecturer in the Department of Mathematics and Computer Science at Eindhoven University of Technology, affiliated with the Algorithms and Logics for Verification research group. His academic role involves teaching courses including Discrete Mathematics and Discrete Structures. His research spans parallel computing and formal verification , with core interests in: Design and analysis of parallel algorithms Data parallelism and GPU programming (OpenCL) Software verification methodologies (deductive verification, soundness) Software development tools and annotation systems Recent publications focus on integrating verification frameworks (e.g., HaliVer) with scheduling languages, developing linear parallel algorithms for bisimulation, and applying formal methods to scientific pipelines like radio telescope data processing. His work consistently addresses challenges in concurrency, GPU optimization, and quantifier reasoning.
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 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.
Alexios Balatsoukas Stimming is Assistant Professor in the Signal Processing for Communications Lab at Eindhoven University of Technology, with an adjunct appointment at Rice University. His research bridges communications theory, hardware implementation, and machine learning. Research Focus Develops algorithms and hardware for error-correction coding, wireless communications, and RF sensing systems. Current work focuses on low-complexity decoders for emerging standards, machine learning for signal processing, and full-duplex communication systems. Publication Trends Recent work demonstrates advances in polar/LDPC decoder architectures, Wi-Fi sensing techniques, and machine learning applications for demodulation. Strong emphasis on hardware-efficient implementations for 5G/6G systems. Projects & Service BIT-FREE: Hybrid coded modulation for free-space optics Editor for IEEE Journal on Selected Areas in Communications Program committee member for multiple IEEE conferences
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.
Mark Wijtvliet is a University Researcher at Eindhoven University of Technology (TU/e), specializing in the Department of Electronic Systems within the field of Electrical Engineering. His work focuses on reconfigurable architectures, energy efficiency, and their applications in electronic systems. He contributed to the BrainSense project (2018–2020), developing an autonomous EEG headset for brain-controlled applications. Education: Mark holds a Master’s degree in Electronic Systems from TU/e (2011) and completed his Ph.D. in 2020 with a thesis titled *Blocks, a reconfigurable architecture combining energy efficiency and flexibility*. His research explores energy-efficient computing and integrated circuit design. Research Interests: His work emphasizes reconfigurable architectures, energy conservation, and hardware-software co-design. Recent projects include the development of R-Blocks, a programmable coarse-grained reconfigurable architecture (CGRA), and energy-area estimation models for CGRAs. Publications: Key contributions include peer-reviewed articles on reconfigurable computing, energy-efficient design, and CGRA optimization. His work bridges theoretical computer architecture with practical applications in low-power systems. Grants/Projects: As a project member in BrainSense, he advanced brain-computer interface technologies. His research aligns with UN Sustainable Development Goals related to affordable and clean energy.
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.
Jan Broenink is an Associate Professor at the University of Twente, affiliated with the Robotics and Mechatronics group within the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS). He serves as Programme Director of the MSc Robotics programme since July 2021 and previously chaired the Robotics and Mechatronics group (2017–2021). He holds a PhD and MSc in Electrical Engineering (EE) and biomedical degrees from the University of Twente. His research focuses on Cyber-Physical Systems, Systems Engineering, Robot Software Architectures, and Tools Development. Key interests include model-driven design, meta-modelling, simulation, co-simulation, and concurrent engineering. He develops software tools for robotics, emphasizing real-time computing and embedded control systems. Teaching encompasses Robot Software Design using Model-Driven approaches, Systems Engineering, and project supervision for MSc/BSc students. His work contributes to UN SDGs related to Industry and Future of Work, and Social and Daily Life. Broenink has supervised 9 student works and is active in conferences, presenting on topics like co-design methodologies and bond graph frameworks. His 162+ research outputs span embedded systems, robotics, and simulation tools.
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.
Simon Portegies Zwart is a Professor of Computational Astrophysics at Leiden University's Leiden Observatory (Sterrewacht Leiden). His research focuses on computational gravitational dynamics, stellar evolution, and supercomputing applications in astrophysics. He leads the AMUSE software framework project, enabling multi-domain astrophysical simulations. He holds editorial roles at the Journal of Computational Astrophysics and is a member of the Royal Netherlands Academy of Arts and Sciences (KHMW). Education: PhD in Astrophysics (Utrecht University) Research Interests: His work spans numerical stellar dynamics, galaxy evolution, and high-performance computing. Notable contributions include simulating star cluster dynamics, supermassive black hole interactions, and planetary system formation. He emphasizes sustainable computational practices and interdisciplinary software development. Key Contributions: Developed the Starlab, AMUSE, and OMUSE software packages Recipient of the HPC Innovation Excellence (2020) and SURF Innovation Challenge (2015) awards Advising & Grants: Supervises multiple PhD candidates and postdocs, focusing on computational astrophysics and oceanography. Collaborates with institutions like RIKEN (Japan) and the Massachusetts Institute of Technology (MIT). Labs & Teams: Leads the Computational Astrophysics group at Leiden, specializing in gravitational dynamics and multi-physics simulations.
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
