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Dragan BosnackiView profile

Assistant Professor

• Machine Learning
• Bioinformatics
• Computational Biology
• +6 more

Dragan Bosnacki is an Assistant Professor at Eindhoven University of Technology (TU/e), holding joint appointments in the departments of Biomedical Engineering and Mathematics and Computer Science. He specializes in interdisciplinary research at the intersection of computer science and biomedicine, with a focus on machine learning, bioinformatics, and computational modeling of medical therapies. His work combines formal verification techniques, GPU-accelerated algorithms, and biomedical applications such as High Intensity Focused Ultrasound (HIFU) therapy modeling. Dr. Bosnacki earned his BSc in Electrical Engineering and MSc in Computer Science from Sts. Cyril and Methodius University (Macedonia), followed by a PhD in Mathematics and Computer Science at TU/e under Professors Jos Baeten and Dennis Dams. He has pioneered advancements in parallel model checking and contributed significantly to the SPIN model checking tool. His research also spans separation logic, biomedical data analytics, and the application of mathematical methods to biological systems. Education: BSc in Electrical Engineering, Sts. Cyril and Methodius University (Macedonia) MSc in Computer Science, Sts. Cyril and Methodius University (Macedonia) PhD in Mathematics and Computer Science, TU/e (2001) Research Interests: Mechanisms of machine learning for biomedical data Formal verification of hardware/software systems GPU-optimized algorithms for computational biology Mathematical modeling of HIFU cancer therapies Reconstruction of biological networks and drug response modeling Teaching: Advanced programming and biomedical data analysis Simulation of biochemical systems Programming for data analytics Capita selecta in bioinformatics Thermodynamics & chemical kinetics Key Contributions: Co-developed GPU-based model checking tools (e.g., GPUexplore) Improved partial-order and symmetry reduction techniques Contributions to the SPIN model checker and VeriFast tool Advising: Supervised 37 academic works, focusing on interdisciplinary projects in computational biology and formal methods.

Luuk VisscherView profile

Professor

• Density Functional Theory
• Quantum Chemistry
• Computational Chemistry
• +4 more

Prof. Luuk Visscher holds dual professorships at the Faculty of Science and AIMMS at Vrije Universiteit Amsterdam. He specializes in computational quantum chemistry, focusing on subsystem methods, relativistic calculations, and algorithm optimization for supercomputing/GPU architectures. His work bridges quantum mechanics and classical methods to model complex systems in catalysis, drug design, and actinide chemistry. Education: PhD in Relativity and Electron Correlation (University of Groningen, 1993 cum laude), Master's in Chemistry (Groningen, 1989). Research Themes: (1) Density Functional Theory subsystem approaches; (2) High-performance computing for quantum chemistry; (3) Relativistic methods for heavy elements. Notable achievements include the DIRAC computational chemistry code and collaborations with Oak Ridge National Laboratory. Awards: Dirac Medal (2006), Vici Grant (2005), Clemens Roothaan Award (1996). Teaching: Courses in quantum chemistry, scientific computing, and thermodynamics at BSc/MSc levels. Supervised 13 PhD theses.

Vincent LostanlenView profile

Researcher

• Machine Learning
• Audio Content Analysis
• Computer Music
• +11 more

Vincent Lostanlen is a Researcher (chargé de recherche) at CNRS, affiliated with the LS2N laboratory located at Centrale Nantes engineering school. He also serves as a visiting scholar at New York University. His research focuses on developing artificial intelligence for audio analysis, emphasizing machine listening systems, bioacoustic monitoring, and computer music. Key projects include the BirdVox platform for avian migration monitoring and the Kymatio Python library for wavelet scattering transforms. Education: Ph.D. in Mathematics from École Normale Supérieure (2017), supervised by Stéphane Mallat; MSc in Acoustics & Music Informatics from Université Pierre-et-Marie-Curie (2013); Dipl. Ing. in Signal Processing from Télécom ParisTech (2013). Research interests span audio representation learning, environmental sensor networks, and generative models for sound. Notable contributions include the SONYC-UST urban sound dataset and the PCEN signal frontend for robust acoustic sensing. Scientific awards include the 2019 AFIM Young Researcher Award, 2016 SFA Best Poster Prize, and 2015 IEEE MLSP Best Paper Award. He advises graduate students across computer science and music technology domains, with former advisees working in academia and industry. Current activities include developing green AI for audio processing and collaborating on bioacoustic sensor networks for biodiversity monitoring. His work bridges signal processing theory with practical applications in music, ecology, and biomedical engineering.

Anton J. WijsView profile

Associate Professor

• Parallel Software Development
• Formal Verification
• Model Checking
• +4 more

Anton J. Wijs is an Associate Professor at the Eindhoven University of Technology (TU/e) , affiliated with the Model-Driven Software Engineering group within the Department of Software Engineering and Technology . His research focuses on parallel software development and leveraging parallelism to enhance formal verification techniques. He holds leadership roles as a Board Member of the European Association for Programming Languages and Systems (EAPLS) and a member of the Steering Committee of ETAPS. Academic Background: Wijs earned his PhD in Computer Science from VU Amsterdam (2007), followed by postdoc positions at INRIA (France), TU/e, and RWTH Aachen University (Germany). He transitioned to an Assistant Professor role at TU/e in 2015 and became an Associate Professor in 2023. Research Interests: His work emphasizes scalable verification methodologies using GPUs, formal analysis of concurrent systems, and tools like mCRL2 . He collaborates with industry partners such as ASML and Codeplay Software to bridge academic research with practical applications. Awards & Grants: Notable achievements include the Best Student Paper Award (FACS 2017) , the Amazon Research Award (2022) , and an NWO grant for GPU-accelerated state space analysis. He co-led the GEARS project (2017–2022) , focusing on efficient parallel model checking. Teaching: Wijs teaches courses on Programming , Program Verification Techniques , and Digital Twins of Medical Devices . He actively supervises students and contributes to international conferences as a program chair and reviewer.

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.

Lejla BatinaView profile

Professor

• Cryptography
• Embedded Systems Security
• Side-Channel Analysis
• +3 more

Lejla Batina is a full professor of security of embedded devices in the Digital Security (DiS) group at Radboud University's Computing Science Department within the Faculty of Science. She also holds an adjunct professorship at the Selmer Center of the University of Bergen, Norway. Her research focuses on applied cryptography, embedded systems security, and leveraging machine learning for side-channel analysis. Batina has contributed significantly to cryptographic implementations, including ECC and post-quantum cryptography. She has organized major conferences such as CHES 2014, EUROCRYPT 2020/2021, and ACNS 2024, and currently serves on editorial boards like ACM TECS. Notable achievements include a VIDI grant (2014) and IEEE Senior Membership (since 2018). Her work spans over 240 publications, covering topics like SCA-resistant ECC, neural network security, and hardware countermeasures. Batina has mentored over 20 PhD students and postdocs, many of whom now hold academic or industry leadership roles. Her lab, CESCA, emphasizes collaborative research in cryptographic hardware and embedded systems security. Education: Professional Doctorate in Engineering (PDEng) in Mathematics for Industry, TU Eindhoven (2001) PhD in Mathematics, KU Leuven (2005) M.Sc. in Mathematics, University of Zagreb (1995) Research Interests: Her work bridges theoretical cryptography and practical implementations, with emphasis on Side-channel and fault attacks on embedded devices Machine learning applications in security Low-cost countermeasures for constrained hardware Post-quantum cryptography implementations Security of neural networks and AI models Grants & Awards: VIDI grant from Dutch Research Council (2014) Senior Member, IEEE (2018–present) Editorial Board Member, ACM Transactions on Embedded Computing Systems (since 2018) Advising & Mentorship: Supervised 15+ PhD students and numerous postdocs, including prominent alumni like Dr. Stjepan Picek (Associate Professor at Radboud) and Dr. Lukasz Chmielewski (Assistant Professor at Masaryk University). Labs & Teams: Leads the Cryptographic Embedded Systems and Security Applications (CESCA) lab, collaborating internationally on hardware-software co-design for secure implementations.

Mojtaba HaghiView profile

Research Fellow

• Embedded Control Systems
• Model Predictive Control
• Multi-core Platforms
• +4 more

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

• Theoretical Chemistry
• Computational Science
• Physical Chemistry
• +3 more

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.

Danny RuijtersView profile

Professor

• Data-Driven Healthcare
• Image-Guided Therapy
• Medical Imaging Technologies
• +3 more

Danny Ruijters is a part-time Full Professor at Eindhoven University of Technology (TU/e), specializing in data-driven value-based healthcare in image-guided therapy. His research focuses on developing intelligent systems that optimize data utilization in minimally invasive treatments, particularly translating population-level medical data to individual patient care. He concurrently serves as a Principal Scientist at Philips' Image Guided Therapy Systems Innovation department since 2011. Education: Bachelor's in Engineering, RWTH Aachen University (2001) Master's Thesis at École Nationale Supérieure des Télécommunications (ParisTech, 2001) PhD from TU/e & KU Leuven (2010) on multi-modal image fusion in minimally invasive treatments Research Focus: Hyper-personalized patient treatment through AI-driven analysis of large medical datasets, clinical workflow optimization, and prototype development for interventional therapies. His work bridges technical innovation with clinical and regulatory requirements. Teaching Activities: Signal Processing Image Analysis for Healthcare Technologies DSP Fundamentals (Signals II) Professional Affiliations: Employed by Philips Healthcare since 2001, leading R&D in image-guided therapy systems. Active in translating research into clinically viable solutions through iterative prototyping and clinical validation.

• Bioinformatics
• Plant Genomics
• Renewable Energy Systems
• +5 more

Jan-Peter Nap serves as an External employee in the BIOS Applied Bioinformatics group at Wageningen University & Research, holding a doctoral engineering title (dr.ir.). His interdisciplinary work bridges computational biology and sustainable energy systems through 123 research outputs including 71 articles, 17 reports, and 12 book chapters. Research Focus: His primary investigations span plant genomics (gene regulation in root nodule development, Solanaceous genome analysis), bioinformatics tool development (pyPaSWAS for GPU-accelerated sequence alignment), and renewable energy systems (economic/environmental analysis of bio-power-to-methane conversion). This tripartite approach integrates molecular biology with environmental engineering and high-performance computing. Publication Trends: Recent work (2017-2020) demonstrates dual specialization: computational biology publications focus on sequence alignment algorithms and plant phenotyping metadata standards, while energy research examines methane production feasibility. Both strands emphasize practical implementation with measurable environmental benefits and Levelised Cost of Energy metrics. Supervision & Projects: He co-supervised two major PhD projects: Low-cost high performance biocomputing (2013-2019) developing GPU-accelerated genomic analysis tools Solanaceous genome bioinformatics (2010-2014) examining gene family relationships in crop plants Research Infrastructure: As core member of BIOS Applied Bioinformatics, he contributes to Wageningen's computational biology ecosystem focusing on agricultural applications, with demonstrated impact through 17+ Scopus citations for his renewable energy work and adoption of phenotyping data standards.

Lars van den HaakView profile

Lecturer

• Parallel Algorithms
• Data Parallelism
• Parallelism
• +5 more

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.

• Probability Theory
• Operations Research
• Stochastic Processes
• +3 more

Michel Mandjes is a full professor of Probability and Operations Research at Leiden University's Mathematical Institute . He studied Econometrics and Mathematics at Vrije Universiteit Amsterdam, earned a PhD in Operations Research, and held positions at Twente University, CWI (Centrum Wiskunde & Informatica), and the University of Amsterdam before joining Leiden in 2023. Research Focus: Intersects probability theory, stochastic operations research, and sustainable mobility solutions. His work includes modeling the impact of new mobility services (MaaS, mobility hubs) and parking policies on urban transportation sustainability using activity-based demand models (ABM). Publications: Co-authored a 2023 study analyzing multimodal travel behavior in the Rotterdam-The Hague metropolitan area, highlighting the potential of mobility hubs and MaaS to reduce private car use through e-bike sharing and public transport improvements. Technical Expertise: Specializes in computational methods for large-scale ABMs, including GPU acceleration and common random numbers to optimize model calibration. His current research emphasizes policy-driven transformations in metropolitan mobility systems through data-driven modeling and scenario analysis.

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.

Gerard BarkemaView profile

Professor

• Statistical Mechanics
• Condensed Matter Physics
• Computational Physics
• +4 more

Prof. Gerard Barkema is a faculty member at Utrecht University, holding the position of Professor in the Simulation of Complex Systems within the Department of Information and Computing Sciences at the Faculty of Science . His research focuses on computational physics, statistical mechanics, and materials science, with a particular emphasis on polymer dynamics, phase transitions, and Monte Carlo methods. He leads the Simulation of Complex Systems chair and contributes to interdisciplinary initiatives such as the university's AI/Data Science Taskforce and the Centre for Complex Systems Studies. Key research areas include applied data science, complex systems foundations, and computational modeling of materials like graphene and amorphous silicon. His work spans topics such as critical phenomena in Ising models, structural dynamics of polycrystalline systems, and cluster algorithms for bond-diluted models. He actively collaborates on projects involving nucleation mechanisms, skyrmion dynamics, and high-performance computing techniques like GPU acceleration. Prof. Barkema is deeply engaged in academic service, including roles on steering committees for initiatives like I-partnership and Co-teach Informatics, as well as executive board memberships in national research platforms (e.g., ICT-research Platform Netherlands). His extensive publication record reflects a commitment to advancing computational methods and their applications in understanding complex physical systems.

Arris S. TijsselingView profile

Researcher

• Transients
• Water Hammer
• Fluid-Structure Interaction
• +3 more

Arris S. Tijsseling is a researcher active in engineering fields, particularly focusing on transient pipe flow , water hammer , and fluid-structure interaction . His work spans both theoretical and applied research, with a strong emphasis on computational fluid mechanics and dynamics. Field of Interest : Transients, One-Dimensional Modeling, Hydrodynamics Course Taught : Analysis 3 (2013–2025) His recent research includes GPU-accelerated simulations of two-phase flows and the development of Lagrangian particle models for moving boundary problems. Publications highlight collaborations with international researchers and institutions, addressing complex challenges in pipeline systems and pressure wavefronts. Tijsseling has supervised 19 formal advisees and contributed to datasets in transient flow analysis. His work has been cited over 3,700 times, reflecting significant impact in fluid dynamics and pipeline engineering.