Dr. Haneen Farah is an Associate Professor in the Department of Transport & Planning at Delft University of Technology and co-director of the Traffic and Transportation Safety Lab. She also serves as head of the Traffic Systems Engineering section. Her research focuses on road infrastructure design, road user behavior, and traffic safety, integrating transportation engineering, human factors, and econometrics. Prior to TU Delft, she was a postdoc at KTH Royal Institute of Technology and earned her M.Sc. and Ph.D. in Transportation Engineering from the Technion-Israel Institute of Technology. Her work includes national/international projects like SAMEN (mixed automated/human traffic implications), AfroSAFE (road safety in Africa), and XCARCITY (sustainable city mobility). She teaches undergraduate and graduate courses on road design and traffic safety, including online programs for low/middle-income countries. Farah supervises multiple PhD and Master students in her research areas, contributing to over 50 peer-reviewed publications. Key research themes include infrastructure design for automated vehicles, driver behavior modeling, cyclist safety, and policy implementation of the Safe System approach. Her interdisciplinary approach bridges engineering and psychology to enhance traffic safety and efficiency through advanced analytics and simulation models.
Dr. Yan Feng is an Assistant Professor at Delft University of Technology's Department of Transport and Planning and Director of the Mobility in eXtended Reality (XR) Lab. Her research focuses on using XR technologies (Virtual, Augmented, and Mixed Reality) to study human mobility behavior in urban environments, including walking, cycling, automated vehicles, and public transportation. She leads interdisciplinary research at the intersection of transportation engineering, design, architecture, and human-computer interaction, aiming to advance human-centered and inclusive mobility systems. Her research interests include mobility behavior analysis, XR-based methodologies for studying wayfinding, evacuation scenarios, human-vehicle interaction, safety training, and accessibility. Key contributions involve developing XR tools for urban mobility research and addressing diversity and inclusion in transportation systems. Recent work emphasizes validating VR simulations against real-world environments and analyzing pedestrian behavior in high-risk scenarios. Articles highlight methodologies for crowd management strategies, pedestrian choice modeling, and evacuation dynamics. No scientific awards are explicitly mentioned in the provided texts. Dr. Feng’s activities include organizing conferences (e.g., XRIA 2024), delivering keynote talks on automated driving and cycling simulator studies, and serving on editorial boards like the Delft Young Academy. The Mobility in eXtended Reality Lab is central to her work, fostering collaborations across disciplines to innovate mobility solutions.
Maarten Hornikx is a Full Professor in Building Acoustics at the Department of the Built Environment , Eindhoven University of Technology. He serves as Vice-Dean of the department, leads the Building Acoustics Chair of Unit Building Physics and Services (BPS), and coordinates the Science of Sound and Music course series since 2013. His research focuses on computational modeling of sound propagation in built environments, with applications in mixed reality platforms and numerical analysis of outdoor/indoor propagation effects like vegetation and meteorological influences. Education : PhD in Applied Acoustics (Chalmers University of Technology, 2009); MSc in Architecture, Building and Planning (2004) Hornikx promotes open research software in acoustics and has received multiple grants, including Marie Curie Individual and Career Integration Grants. His recent publications explore AI-driven diffusion equation modeling, acoustic absorber optimization, and advanced numerical methods like the discontinuous Galerkin technique. He has held international leadership roles, including chairing the Computational Acoustics Technical Committee of the European Acoustics Association and serving as Associate Editor for Acta Acustica . Scientific Awards : Marie Curie Fellowship (2009-2011); Marie Curie Career Integration Grant (2012); 4TU.Built Environment Center Scientific Director (2020-2021); eScience Center Fellow (2022) As a research leader, Hornikx guided the H2020-ITN Acoutect project and conducted sabbaticals at Aalto University, Stockholm University (2018), and Politecnico Torino (2022). His group emphasizes computational acoustics and open-source tools to enhance reproducibility and collaboration.
Edwin van der Heide is a part-time Lecturer and researcher at Leiden University, affiliated with the Leiden Institute of Advanced Computer Science (LIACS) and the Academy for Creative and Performing Arts (ACPA). His work bridges sound, space, and interaction through installations, performances, and environments. Key roles include: Co-head of ArtScience Interfaculty (Royal Conservatoire & Royal Academy of Art, The Hague) until 2016 Edgard Varèse Guest Professor at TU Berlin (2009) Invited artist at Le Fresnoy (France) and HKB Bern University of the Arts (2019) Research Interests: Spatial sound composition, audience interaction, interdisciplinary art, and the intersection of technology with auditory perception. Notable projects include Whispering Wind (permanent installation at Leiden University) and Spiral of Time (MACBA, Barcelona). Recent Articles & Exhibitions (2023–2024): Focus on large-scale installations like Pneumatic Sound Field and Spiral of Time , emphasizing public engagement and spatial acoustics. Awards: Witteveen+Bos Art+Technology Award (2009), Best Paper Award for BAI (摆) (2018). Grants & Labs: Collaborations with institutions like NCCA (Russia), MAXXI (Italy), and involvement in projects like Evolving Spark Network (global sound art initiatives).
Pascal Mettes is a tenured Assistant Professor at the University of Amsterdam within the Informatics Institute, specializing in Artificial Intelligence. He leads groundbreaking research in hyperbolic deep learning, a field he has significantly advanced through theoretical developments and practical applications in computer vision and multimodal learning. His research focuses on three primary domains: hyperbolic vision-language models that address the hierarchical nature of language-vision relationships; hierarchical deep learning using hyperbolic embeddings that naturally accommodate exponential growth patterns; and robust deep learning in hyperbolic space that improves out-of-distribution detection and network resilience. Mettes has established himself as a leading figure in this emerging field through numerous publications at top-tier conferences including CVPR, ICCV, ICML, NeurIPS, and ICLR. His recent work demonstrates how hyperbolic geometry provides natural solutions to fundamental limitations in modern deep learning, particularly regarding hierarchical data structures that cannot be adequately represented in Euclidean space. The publication trends show increasing impact and recognition in the computer vision and machine learning communities, with multiple papers receiving oral presentations and best paper nominations. Best paper nomination ESWC25 for 'Designing Hierarchies for Optimal Hyperbolic Embedding' Finalist MM 2023 Best Open-Source Software Competition (for HypLL) Multiple reviewer awards across major conferences including CVPR, ICLR, ECCV, ICML, and NeurIPS MM 2016 Best Doctoral Student Award TRECVID 2015 Winner Multimedia Event Detection Benchmark Mettes actively mentors eight PhD students working on hyperbolic learning and related topics, while also securing significant research funding including ELLIs PhD Award, NWO ClickNL, Google Perception Academic Funding, and Data Science Centre PhD Grants. He serves in prominent academic roles as Program Chair for International Conference on Multimedia Retrieval 2026 and has organized multiple workshops on hyperbolic learning at major conferences. His leadership in establishing hyperbolic deep learning as a recognized research direction is evident through his survey paper in IJCV 2024 and the development of the HypLL library for hyperbolic learning.
Dr. Prasanth Venugopal is an Associate Professor specializing in Power Electronics with a focus on advanced energy transfer systems and battery technology. His research spans wireless power transfer, electric vehicle charging, and electrochemical impedance spectroscopy for battery diagnostics. Primary research areas: Wireless Power Transfer (100%), Harmonics (88%), Inductive Power Transfer (87%), Battery Engineering (48%) Recent publications demonstrate expertise in transformerless converter designs, multi-level architectures, and AI-driven battery capacity estimation. He has pioneered meander coil topologies for harmonic mitigation and developed computation-light models for battery aging analysis. His work includes collaborations on Li-ion battery degradation, onboard chargers for electric vehicles, and hybrid power systems for electric aircraft. Despite significant output in IEEE Transactions, no explicit awards or student mentoring data appears in the provided texts.
Achilleas Psyllidis is an Assistant Professor of Urban Mobility and Director of the Urban Analytics Lab at TU Delft. He also leads the Social Urban Data Lab at Amsterdam Institute for Advanced Metropolitan Solutions and is affiliated with the LDE Centre for BOLD Cities. His roles include membership in TU Delft's Transport & Mobility Institute, the Mobility Futures Vision Team, and serving on the Executive Board of CUPUM. Education: PhD in Spatial Data Science (TU Delft, Faculty of Architecture and the Built Environment) Master of Science in Spatial Planning (National Technical University of Athens) Engineering Diploma in Architectural Engineering (National Technical University of Athens) Research Interests: Focuses on accessibility, walkability, land-use dynamics, and travel behavior. Develops computational methods for analyzing access equity, spatial segregation, and human mobility. Leads projects on sustainable urban mobility, environmental exposures, and the 15-minute city concept. Awards: CTwalk Map: Best Demo Award (ICT.Open 2024) ROUTE Ontology of Urban Transportation Entities (2015) Grants & Projects: Involved in initiatives like PERISCOPE (Social Resilience Design), Horizon2020 'Equal-Life' (Environmental Health), and SocialGlass (Urban Analytics Dashboard). Active in research collaborations across Europe and Asia. Labs & Teams: Directs Urban Analytics Lab and Social Urban Data Lab, focusing on data-driven urban solutions. Engages in interdisciplinary teams addressing mobility futures, urban health, and sustainable design.
Francesca Grisoni serves as an Assistant Professor in the Department of Biomedical Engineering at Eindhoven University of Technology (TU/e), where she currently leads the Molecular Machine Learning team. She additionally holds appointments as an ICMS Core member and Associate Professor at EAISI (Eindhoven Artificial Intelligence Systems Institute), reflecting her cross-disciplinary role at the intersection of computational science and biomedical applications. Academic Background : Grisoni completed her Environmental Sciences degree and earned a Ph.D. in 2016 from the University of Milano-Bicocca, where her dissertation focused on interpretable machine learning for molecular property prediction. During doctoral studies, she conducted research at ETH Zurich's Department of Chemistry and Applied Biosciences and the U.S. EPA's National Center for Computational Toxicology. Ph.D., University of Milano-Bicocca, 2016 (Dissertation: Interpretable machine learning for molecular property prediction) Environmental Sciences, University of Milano-Bicocca Her research integrates artificial intelligence, chemistry, and biology to develop computational methods for drug discovery, emphasizing wet-lab experimental validation alongside algorithmic innovation. Key focus areas include overcoming activity cliffs in molecular machine learning, generative modeling for scaffold hopping, and AI-augmented decision-making in therapeutic development, with the ultimate goal of achieving 'better decisions faster' in drug discovery pipelines. Analysis of her recent 2025 publications reveals a concentrated trend toward chemical language models and generative deep learning frameworks, specifically addressing low-data drug discovery challenges through active learning and neural network architectures. These works bridge computer science with pharmacology, targeting bioactivity prediction, molecular representation, and enzyme design while maintaining strong ties to experimental validation. Scientific Awards : Lush Young Researcher Prize Early Career Award 2022 from the Dutch Royal Netherlands Academy of Arts and Sciences (KNAW) ERC Starting Grant (2022) Grants and Supervision : Dr. Grisoni secured the prestigious ERC Starting Grant in 2022 to advance her molecular machine learning research. Institutional records indicate she has supervised 7 students (as shown in TU/e's 'Supervised Work (7)' repository section), though specific names aren't provided in the source material. Her group maintains active industry collaborations, including past engagement with Bracco Pharmaceuticals. Laboratory and Team : The Molecular Machine Learning team operates under the ICMS and EAISI frameworks, merging computational AI development with experimental wet-lab validation. This collaborative unit focuses on fragment-based molecular design, chirality representation (evidenced by fragSMILES work), and high-throughput nanoparticle identification using machine learning, as highlighted in recent press coverage and datasets.
Professor Roland J. Pieters is a distinguished academic at Utrecht University's Faculty of Science, where he serves as a full Professor in the Department of Chemical Biology and Drug Discovery. With over two decades of experience at the institution, he has progressed from Assistant Professor (1998) to Associate Professor (2005) and ultimately to Full Professor (2010-present). His research group is internationally recognized for groundbreaking work at the intersection of carbohydrate chemistry, chemical biology, and drug discovery, with particular emphasis on developing novel therapeutic approaches against bacterial infections and pathogenic mechanisms. Full Professor, Utrecht University (2010-present) Associate Professor, Utrecht University (2005-2010) Assistant Professor, Utrecht University (1998-2005) NWO Talent Post-doctoral Fellow, ETH-Zürich (1995-1996) Postdoctoral Researcher, University of Groningen (1996-1998) Professor Pieters earned his M.Sc. in Organic Chemistry from the University of Groningen in 1990, where he worked with Professor Ben Feringa, and completed his Ph.D. at MIT in 1995 under the supervision of Professor Julius Rebek Jr. His doctoral research focused on molecular recognition and template effects in bisubstrate systems, establishing the foundation for his lifelong interest in molecular interactions. Professor Pieters' research primarily centers on glycodrugs and the strategic interference with protein-carbohydrate interactions using multivalent systems of varying architectures. His laboratory has made significant contributions to understanding how rigid spacers in multivalent ligands can dramatically enhance binding affinity to target proteins, with applications against viral and bacterial adhesion proteins, toxins, galectins, and glycosidases. A particular focus has been on developing inhibitors for Pseudomonas aeruginosa lectin LecA, cholera toxin, influenza virus hemagglutinin, and more recently, SARS-CoV-2 spike protein interactions with host cell receptors. His group also pioneered the use of glyco- and peptide-microarrays for high-throughput screening of carbohydrate-protein interactions and drug discovery, particularly in the area of O-GlcNAcylation research. The publication record of Professor Pieters demonstrates consistent innovation in the field of multivalent carbohydrate-based therapeutics. His recent work (2020-2024) shows a strategic expansion into viral pathogenesis (particularly influenza and SARS-CoV-2), immune modulation through glycan recognition, and novel approaches to vaccine development. A notable trend is the increasing sophistication of multivalent architectures, moving from simple divalent systems to tetra- and hexavalent ligands with precisely engineered spatial arrangements. His research bridges fundamental chemical principles with practical therapeutic applications, maintaining strong connections to pharmaceutical development while advancing basic science understanding of carbohydrate-mediated biological processes. Professor Pieters' scientific achievements have been recognized with prestigious awards including a Fellowship from the Royal Netherlands Academy of Arts and Sciences (KNAW) in 1999 and a VICI personal grant from the Netherlands Organisation for Scientific Research (NWO) in 2008. These competitive awards reflect the significance and innovation of his research program. He has also served on editorial advisory boards, notably as Section Editor-in-Chief for Chemical Biology in the journal Molecules (2018-2022), contributing to the scholarly community through peer review and academic leadership. Fellowship of Royal Netherlands Academy of Sciences (KNAW), 1999 VICI, personal grant, NWO, 2008 Section Editor-in-Chief Chemical Biology for Molecules (2018-2022) Throughout his career, Professor Pieters has coordinated significant research projects including the EU project POLYCARB and secured competitive funding that has sustained his innovative research program. His laboratory has fostered numerous collaborations across Europe and internationally, creating a vibrant research environment that has trained many scientists now working in academia and industry. His research on multivalent carbohydrate systems represents a sustained intellectual contribution to chemical biology with direct relevance to developing new anti-infective strategies and therapeutic approaches. Professor Pieters leads an active research group within Utrecht University's Department of Chemical Biology and Drug Discovery, situated in the David de Wied Building. His laboratory maintains strong connections with other research groups both within Utrecht University and internationally, particularly in the fields of glycobiology, infectious diseases, and drug discovery. The research environment he has cultivated emphasizes interdisciplinary approaches, combining synthetic chemistry, biophysical analysis, and biological testing to address fundamental questions in carbohydrate-mediated biological processes with therapeutic applications.
Professor Vedran Dunjko is a faculty member at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, with affiliations to the Leiden Institute of Physics (LION). He leads the Applied Quantum Algorithms group and co-founded the Quantum@LIACS initiative, focusing on the intersection of quantum computing, machine learning, and artificial intelligence. His research interests include quantum machine learning, quantum-enhanced reinforcement learning, quantum heuristics, and the application of AI to quantum computing challenges. Dunjko's work bridges theoretical foundations with experimental implementations on near-term quantum devices, exploring both quantum advantages in learning and the use of classical AI for quantum system design. The recent publications show a strong trend toward proving quantum advantages in learning tasks, optimization, and topological data analysis, with publications in Nature , Nature Communications , and NeurIPS . Key themes include quantum policy gradients, quantum TDA, and reinforcement learning for quantum circuit optimization. ERC Consolidator Grant (2024) PNAS Cozzarelli Prize (2018) Editor’s Suggestion in Physical Review Letters (2014, 2018) Featured in Physics (American Physical Society) (2014, 2018) Dunjko advises several PhD candidates and postdocs, including Rahul Bandyopadhyay, Sofiene Jerbi, and Lea Trenkwalder. He has received competitive grants, most notably the ERC Consolidator Grant in 2024. His group fosters international collaborations with institutions across Europe and industry partners. The Applied Quantum Algorithms group and the Quantum@LIACS team combine theoretical investigations with practical implementations on quantum hardware, focusing on scalable quantum algorithms and AI-driven quantum discovery.
Leonardus Cornelis Nicolaas de Vreede is a Professor at Delft University of Technology in the Faculty of Electrical Engineering, Mathematics and Computer Science. With over 237 research publications and extensive conference activities, he is a leading researcher in RF and microwave engineering with specialization in power amplifiers, digital transmitters, and mm-wave circuits for wireless communications applications. Dr. de Vreede's research focuses on the intersection of circuit design and signal processing for next-generation wireless systems: Advanced Power Amplifier Architectures including Doherty and Out-phasing techniques Energy-Efficient Digital Transmitters with high linearity and power efficiency mm-Wave Circuit Design for 5G/6G applications Machine Learning Applications for Digital Predistortion CMOS RF Integrated Circuit Implementation Wideband Signal Processing Techniques His recent publications demonstrate a clear research trajectory toward integrating machine learning with traditional RF circuit design to solve the efficiency-linearity tradeoff in wireless transmitters. This work is particularly relevant for current and future wireless infrastructure requiring high spectral efficiency across wide bandwidths while maintaining energy efficiency. Dr. de Vreede has received significant recognition for his contributions to the field: EuMC Microwave Prize (2024) for groundbreaking work on wideband Doherty amplifiers Recognition for innovative characterization techniques for high-power RF transistors (2015) As an active researcher and educator, Dr. de Vreede has supervised 16 students and regularly participates in major international conferences including serving on program committees for the IEEE MTT-S International Microwave Symposium. His work bridges theoretical advances with practical implementations for wireless infrastructure applications, with numerous patents and industry collaborations evident from his research portfolio.
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.
Paris Avgeriou is a Professor of Software Engineering at the University of Groningen, affiliated with the Faculty of Science and Engineering and the Bernoulli Institute. He leads the Software Engineering and Architecture research group and serves as Editor-in-Chief of the Journal of Systems and Software . His expertise spans technical debt management, software architecture, self-adaptive systems, and embedded systems design. Avgeriou holds an office at Nijenborgh 9, Groningen, and actively advises academic institutions and funding bodies globally. Research Interests: Avgeriou's work focuses on advancing software architecture principles, technical debt lifecycle management, and the integration of AI in software engineering. His research emphasizes practical solutions for improving software quality, maintainability, and system dependability, particularly in embedded and self-adaptive contexts. Recent Contributions: Recent studies include frameworks for benefit-cost-risk decision-making in self-adaptive systems, automated technical debt management using ML, and tools for tracing architecture-related debt. He collaborates internationally, contributing to standards like the Copenhagen Manifesto for human-centered AI in software engineering. Grants & Awards: While no specific awards are listed, his editorial role and frequent conference contributions reflect recognition in the field. He chairs conference tracks and oversees workshops, fostering early-career researchers and artifact evaluation. Labs & Teams: His group is part of the Bernoulli Institute, working on platforms like SDK4ED for embedded systems and tools such as DebtViz for technical debt monitoring. The team explores intersections between systems engineering and software architecture in complex systems-of-systems.
Constant Hak is an Assistant Professor with the Building Acoustics research group at the Department of Built Environment, Eindhoven University of Technology (TU/e). His work focuses on reducing adverse health effects caused by human-induced noise while promoting positively perceived sound environments through computational and experimental acoustic methods. Dr. Hak specializes in room and hall acoustics, developing advanced acoustic measurement techniques based on impulse responses. His research includes analyzing how orchestra members influence stage acoustic parameters across multiple concert hall stages and orchestra pits, as well as creating methods for accurate impulse response measurements in large open-air theaters. Software and measuring equipment developed by Hak is used worldwide for research and engineering applications. His recent publications demonstrate expertise spanning building acoustics, plasma physics, and indoor environmental quality measurement. The research shows consistent focus on measurement techniques, with applications in architectural acoustics, concert hall design, and educational tools for environmental quality assessment. Dr. Hak teaches multiple courses including Architectural Acoustics, The Science of Sound, Techniques in Architectural Acoustics, and various Master's projects related to Building Physics and Services. His educational work includes developing the SvS (Senses versus Sensors) device, which helps students understand indoor environmental quality through hands-on measurement of temperature, humidity, light, and sound.
George Vosselman is a Full Professor at the University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), specializing in Geo-Information Extraction with Sensor Systems. Educated with honours at Delft University of Technology (1986) and PhD in Photogrammetry from Rheinische Friedrich Wilhelms University of Bonn (1991), he has held academic roles at the University of Stuttgart, University of Washington, and Delft University of Technology (1993–2004). Since 2004, he has been a key figure at ITC, serving as department head (2012–2018, 2023–). Education: Delft University of Technology (BSc with honours, 1986), Rheinische Friedrich Wilhelms University of Bonn (PhD with honours, 1991) His research focuses on leveraging sensor technology advancements for large-scale geo-information production. Key expertise includes quality analysis of laser altimetry data, point cloud segmentation/classification, 3D building/road modeling, and model-driven imagery analysis. He has published over 220 papers and co-edited the textbook Airborne and Terrestrial Laser Scanning (2010). Recent work integrates deep learning with geospatial data, addressing semantic segmentation, visual question answering, and drone-based mapping. Recent publications (2025–2023) highlight trends in deep learning for remote sensing , including multimodal question answering benchmarks (HRVQA), vectorized building extraction (RoIPoly), latent diffusion for road modeling (LDPoly), and drone obstacle avoidance systems. His work bridges photogrammetry , computer vision , and robotic mapping , with applications in urban planning, disaster management, and informal settlement monitoring. Scientific Awards : Hansa Luftbild (1993), ISPRS Otto von Gruber (2000), Schwidefsky Medal (2012), Karl Kraus Medal (2012), ASPRS Fairchild Award (2015), ISPRS Fellow (2020) As an educator, Vosselman has taught photogrammetry, remote sensing, and laser scanning at Delft University of Technology and globally. He chaired the ITC Examination Board (2015–2023) and modernized geo-information education in Asia/Africa. His software for point cloud processing is commercialized in Europe, and he currently leads ISPRS working groups on point cloud methodologies. Labs/teams include the Earth Observation Science Chair Group at ITC, collaborating on UAV-based datasets (UAVid, UAVPal) and indoor laser scanning systems. Recent activities (2025) involve invited talks on pulse matching limitations in laser scanning and deep learning for point cloud classification.
