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Tiziano De MatteisView profile

Assistant Professor

• Parallel and Distributed Computing
• High-Performance Computing
• Energy Efficiency in Computing
• +4 more

Tiziano De Matteis is an Assistant Professor in the @Large Research group at Vrije Universiteit Amsterdam's Faculty of Science, Department of Computer Systems. He also holds an affiliation with the Network Institute. His research focuses on overcoming post-Moore architecture challenges through parallel and distributed computing, high-performance systems, energy efficiency, and FPGA applications. Previously, he was a PostDoc at ETH Zurich's SPCL Group and earned his MSc/PhD from the University of Pisa. Education PhD in Computer Science, University of Pisa MSc in Computer Science, University of Pisa Research Interests Post-Moore architectures for distributed ecosystems Energy-aware parallel computing High-level abstractions for parallel software development FPGA-based hardware acceleration Data stream processing and distributed systems Recent Research Trends Recent work emphasizes: Data center risk analysis and sustainability Optimizing microservices and distributed scheduling LLM model offloading to NVMe storage Python-based data-centric programming productivity GPU interconnect performance in supercomputing Grants & Projects Participates in the EU-funded 'Extreme and Sustainable Graph Processing' project (2023-2025), exploring scalable graph algorithms and energy-efficient computing systems. Teaching Accelerator-Centric Computing Ecosystems Computer Organization Distributed Systems Systems Seminar

Renata SotirovView profile

Professor

• Optimization
• Operations Research
• Mathematical Programming
• +3 more

Renata Sotirov is a Full Professor at Tilburg University's Department of Econometrics & Operations Research within the Tilburg School of Economics and Management. She holds a PhD in Mathematics and has held academic positions including Associate Professor (2009–2014) and Assistant Professor (2007–2009). Her research focuses on optimization, operations research, and semidefinite programming with applications in combinatorial optimization and discrete mathematics. She has supervised PhD students such as Dr. Ning Ma and Dr. Uwe Truetsch and collaborates internationally, including with institutions like the University of Melbourne and McMaster University. Her work includes pioneering contributions to semidefinite relaxation techniques, integer SDPs, and algorithmic optimization. Recent projects involve the Chvátal-Gomory procedure for integer SDPs and complex cut polytopes. She has served as a research coordinator, director of graduate studies, and treasurer for her department. Her expertise spans teaching operations research methods, combinatorial optimization, and quantitative business analytics.

• Computational Science
• Scientific Visualisation
• GPU Computing
• +5 more

Dr. Robert (Rob) G. Belleman is a Lecturer and researcher in the Computational Science Lab at the Informatics Institute, Faculty of Science, University of Amsterdam. He concurrently serves as Director of the university-wide College of Informatics (CoI), manager of the Visualisation Lab at Science Park, and head of the Education Group, consolidating teaching, research, and infrastructure leadership roles. Education details are not disclosed in the supplied text. His research integrates large-scale scientific computing with immersive visualisation. Core themes encompass GPU-accelerated simulations—from gravitational N-body dynamics to flood modelling—interactive visual analytics for criminal and biological networks, 3-D morphological analysis of marine organisms, sign-language lexicon technologies, and in-silico oncology platforms that fuse multiscale cancer models with clinical IT workflows. Recent articles (2022-2024) emphasise interdisciplinary visual analytics: uncovering cocaine-trafficking scenarios, discriminating coral species via 3-D quantification, and constructing 3-D sign-language lexicons. Earlier work advanced GPU computing for astrophysics and MD simulations, hybrid socio-genetic network modelling of HIV spread, and grid-enabled oncosimulation for nephroblastoma therapy planning. No scientific awards are listed in the provided source. Belleman leads educational innovation through the College of Informatics and the Education Group, while infrastructural oversight of the Visualisation Lab supports numerous UvA research teams. No explicit grant amounts or named PhD advisees appear in the text. He heads the Visualisation Lab facility at Science Park and coordinates the Education Group within the Computational Science Lab, fostering cross-disciplinary collaboration among computer scientists, life scientists, and physicists.

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.

Eric VerbeekView profile

Researcher

• Process Mining
• Process Models
• Process Discovery
• +3 more

H.M.W. (Eric) Verbeek is an ICT Developer in the Department of Mathematics and Computer Science at Eindhoven University of Technology, specializing in Process Analytics. His work focuses on advancing process mining techniques, standardization of event data (e.g., IEEE XES), and the development of tools like ProM and CPN IDE. He has contributed to over 160 publications since 1986, with recent research emphasizing process discovery algorithms, workflow net synthesis, and context-rich process modeling. Research Interests include Process Mining, Process Models, Process Discovery, Petri Net Analysis, and Workflow Management. He has actively participated in projects like the GDPR compliance toolkit and has been involved in defining standards for event data interoperability. Notable contributions include the IEEE XES standard for process mining, tools for event log generation and analysis, and methods for decomposed conformance checking. His work bridges theoretical advancements in process modeling with practical tool development, supporting both academia and industry applications. Collaborations span international academia, focusing on process mining workflows and compliance monitoring. His research trends highlight a balance between algorithmic innovation and real-world applicability in areas like healthcare, logistics, and service management.

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.

• Data-Intensive Systems
• Parallel Graph Analytics
• Algorithms
• +6 more

H.J. Sips is a Professor at Delft University of Technology within the Faculty of Electrical Engineering, Mathematics and Computer Science. His research focuses on data-intensive systems, parallel graph analytics, and distributed systems, with a strong emphasis on algorithm design and performance optimization. Academic Rank: Professor Institution: Delft University of Technology School: Faculty of Electrical Engineering, Mathematics and Computer Science His research interests include: Algorithms Distributed Systems Graph Analytics OpenCL and GPU Programming Social Network-based Sybil Defense His publications highlight trends in high-performance computing, with a focus on portability, parallelization, and hardware acceleration. Notable topics span Intel Xeon Phi architecture, acoustic ray tracing, and robust Sybil defense mechanisms under dynamic network churn. H.J. Sips has contributed to editorial activities for the Lecture Notes in Computer Science journal since 2009. His work demonstrates a sustained commitment to advancing computational efficiency and distributed system security.

• Machine Learning
• Parallel and Distributed Computing
• Graph Theory
• +6 more

Giuseppe Agapito is an Associate Professor and senior research scientist at Magna Graecia University of Catanzaro, working within the Department of Law, Economics and Sociology. His academic profile spans both teaching responsibilities and active research in computational fields. His research focuses on computational intelligence techniques applied to complex real-world problems, particularly in machine learning, parallel and distributed computing, and graph theory. His work demonstrates strong interdisciplinary applications, especially in biology and omics sciences where he develops methods to leverage the vast amounts of data being produced. Agapito has published over 100 papers in international journals and conference proceedings. His recent publications show a strong focus on graph-based machine learning approaches, sentiment analysis techniques, and bioinformatics applications. His research trajectory indicates a progression from bioinformatics applications toward more general machine learning frameworks with applications across multiple domains. As an active member of the academic community, he serves as a reviewer for scientific journals and participates as chair and program committee member for national and international conferences. His editorial work for PeerJ Computer Science demonstrates his standing in the academic community. His teaching responsibilities include courses on Computer Science and Data Analysis within the Department of Law, Economics and Sociology, suggesting an interdisciplinary approach that applies computational methods to social sciences domains.

Peter BonczView profile

Professor

• Database Systems
• Column-Store Databases
• Vectorized Query Processing
• +5 more

Peter Boncz is a Professor in the special chair of Large Scale Analytical Database Systems at Vrije Universiteit Amsterdam and leads the Database Architectures (DA) research group at CWI (Centrum Wiskunde & Informatica), the Netherlands' national research institute for mathematics and computer science. He serves on the CWI management team and is actively involved in multiple research initiatives and industry collaborations. Professor Boncz is internationally recognized as a pioneer of column-store databases, introduced through his PhD project MonetDB. His research spans database architecture, query processing optimization, and analytical database systems. His work on vectorized query processing with his first PhD student Marcin Zukowski has become foundational in modern analytical databases including BigQuery, Databricks, Snowflake, and DuckDB, which has millions of monthly downloads. Current research focuses include GPU data processing, vector search optimization, confidential computing, and graph data management. Boncz's recent publications reveal strong trends toward optimizing database systems for modern hardware architectures, particularly GPUs and cloud CPUs. His work bridges theoretical database concepts with practical implementation, focusing on performance optimization through innovative data layouts, compression techniques, and hardware-aware processing. The research shows a clear trajectory from foundational database concepts toward specialized optimization for emerging hardware and application requirements. VLDB Test of Time Award 2025 (second time, previously won in 2009) CIDR Test of Time Award 2024 ACM Fellow (2022) Humboldt Research Award (2013) ICTRegie Award (2006) Boncz has co-founded six spin-off companies in data systems, including MonetDB BV, and serves as an advisor to ventures like Databricks Corp. His research is supported by multiple external funding projects including Actian Research Grants, Databricks research agreements, and Motherduck Service Agreements. He has advised numerous students, with Marcin Zukowski being notably mentioned as his first PhD student who co-developed vectorized query processing. As leader of the Database Architectures research group at CWI, Boncz oversees a team focused on pushing the boundaries of database technology. The group maintains close ties with industry through projects with Databricks, Motherduck, and RelationalAI, while continuing to develop open-source technologies like DuckDB. The team is particularly active in GPU acceleration, confidential computing, and graph data management through the Linked Data Benchmark Council (LDBC), which Boncz founded.

Syed Hassan ShahView profile

Adjunct Professor

• Wireless Networks
• Cyber-Physical Systems
• Smart Cities
• +7 more

Dr. Syed Hassan Shah is a distinguished adjunct faculty member at California State University, Fullerton's Department of Computer Science, where he teaches graduate-level courses. Concurrently, he serves as a Wi-Fi connectivity subject matter expert and Product Director for Short Range Technologies at Quectel Inc., and previously held roles as Product Manager for Mobile & Compute Connectivity at Qualcomm Inc., focusing on Mi-Fi, CPE, and UWB technologies. Ph.D. in Computer Science & Engineering, Kyungpook National University, South Korea (2013-2017) BS in Computer Science, Kohat University of Science & Technology, Pakistan (2007-2012) Dr. Shah's research spans interdisciplinary domains in Wireless Communications , Cyber-Physical Systems , and Smart Cities , with significant contributions to Vehicular Networks , Internet of Things , and Future Internet Architectures . His recent publications demonstrate expertise in Neural Networks , Optimization Theory , and Security & Privacy applications. Scientific recognition includes: Qualcomm Innovation Award (2016) IEEE Senior Member (2018) ACM Distinguished Speaker (2018) Best Research Contribution Award (Brain Korea, 2016) Multiple travel grants from ACM and IEEE KNU Honors Scholarship (2013) Dr. Shah actively contributes to academic governance as: Editorial Board Member for 60+ special issues in top-ranked journals TPC Member for 100+ international conferences including IEEE Globecom and ACM MobiHoc IEEE Vehicular Technology Society Board appointee (2018-2019)

Hadi HajibeygiView profile

Professor

• Multiphase Flow in Porous Media
• Multiscale Modeling and Simulation
• Subsurface Hydrogen Storage
• +8 more

Hadi Hajibeygi is a Professor of Geo-Energy Solid and Fluid Mechanics at Delft University of Technology, leading subsurface storage research and multiscale modeling initiatives. He serves as the Subsurface Storage Theme Lead (2016–present) and Energi Simulation Chair holder (2022–present), with a focus on underground hydrogen storage, CO2 sequestration, and geothermal energy systems. His research integrates multiphase flow in porous media , multiscale simulation , and geomechanical stability , supported by NWO-Vidi and Interpore awards. He leads the DARSim and ADMIRE projects, developing frameworks like Adaptive Dynamic Multiscale Integration and pEDFM-U for fractured reservoirs. Key awards include: Interpore Award for Porous Media Research (2021) NWO-Vidi Laureate (2019) Interpore Rosette (2017) ETH Zurich PhD Medal (2012) TU Delft Innovative Teaching Talent (2018) His teaching portfolio spans Dynamics of Solids and Fluids , Numerical Methods for Subsurface Simulation , and Multiscale Modeling , while advising a team of postdocs and PhD candidates on projects spanning induced seismicity, microbial interactions, and fractured reservoir mechanics.