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Jakob SchoefferView profile

Assistant Professor

• Responsible AI
• Explainable AI
• Human-Centered AI
• +6 more

Jakob Schoeffer is a tenure-track Assistant Professor in the Artificial Intelligence department at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen (Netherlands). His work focuses on the intersection of human decision-making and artificial intelligence, particularly in high-stakes contexts where fairness, transparency, and appropriate human-AI collaboration are critical. Dr. Schoeffer's research interests center on responsible and explainable AI, with specific focus areas including: Human-AI collaboration dynamics in decision-making processes Fairness perceptions and interventions in AI systems Appropriate reliance on AI recommendations Explainable AI techniques for high-stakes domains Transparency mechanisms that improve human-AI team performance Label indeterminacy issues in medical AI applications His recent publications (2023-2025) reveal a strong trend toward applying AI research in critical domains like healthcare (particularly neurological recovery prediction), while maintaining a rigorous focus on the human aspects of AI deployment. His work spans both theoretical foundations of human-AI interaction and practical implementations, often employing mixed-methods approaches that combine technical AI development with behavioral studies. Dr. Schoeffer actively collaborates with researchers across institutions including the University of Texas at Austin and has made significant contributions to top conferences in AI ethics, fairness, and human-computer interaction. His research has been featured in multiple news outlets and policy discussions, indicating real-world impact of his work on responsible AI development. Prior to his current appointment, Dr. Schoeffer was a Postdoctoral Research Fellow at the University of Texas at Austin. He received his PhD from the Karlsruhe Institute of Technology (KIT) in Germany with a dissertation titled "On the Interplay of Transparency and Fairness in AI-Informed Decision-Making." He also holds a master's degree in Operations Research from Georgia Tech and industry experience as a Senior Data Scientist at IBM.

Roel C.G.M. LoonenView profile

Associate Professor

• Building Performance Simulation
• Energy Engineering
• Adaptive Facades
• +7 more

Roel C.G.M. Loonen is an Associate Professor at the Unit Building Physics and Services within the Department of the Built Environment at Eindhoven University of Technology (TU/e), Netherlands. He holds joint appointments with EAISI High Tech Systems and EIRES Research groups, focusing on building performance simulation and energy systems. His work bridges academic research with practical applications through collaborations with SMEs in the building industry. Loonen received his BSc and MSc (cum laude) in Building Services from Eindhoven University of Technology, followed by a PhD in 2018 with a dissertation on 'Approaches for computational performance optimization of innovative adaptive facade concepts.' His educational background has positioned him as a leading expert in building performance simulation and sustainable building technologies. His research interests center on developing and applying modeling and simulation strategies to support decision-making for designing buildings that combine high indoor quality with minimal environmental impact. Key areas include adaptive facades, building-integrated renewable energy systems, and energy-efficient building envelopes. He specializes in creating and validating new building performance simulation models to advance innovative building technologies. His recent publications demonstrate a strong focus on practical applications of building performance simulation, with emphasis on residential energy efficiency, photovoltaic systems, and occupant-centered approaches to building design. The work shows increasing integration of machine learning techniques with traditional building simulation methods, particularly for sensitivity analysis and optimization of building performance. REHVA Young Scientist Award (2021) Best PhD supervisor award from Department of the Built Environment, TU/e (2018) First prize - REHVA International student competition (2011) Smart daylight control for optimal building performance (NWO Take-off award, 2018) Best paper award (2021) Loonen actively supervises PhD and Master's students, evidenced by his Best PhD Supervisor Award in 2018. He manages multiple research projects including Sustainable Summer Comfort (2024-2027), Modeling Innovative Use Scenarios for Future Domestic Comfort (2023-2026), and Just Prepare (2022-2026), with funding from sources including the Dutch Research Council (NWO). His professional service includes being a board member of the Dutch-Flemish IBPSA affiliate and co-chair of IBPSA World's website committee, plus reviewing for 35 academic journals. He leads research within the Building Performance group, focusing on creating practical tools and methodologies that bridge the gap between theoretical building performance models and real-world implementation in the construction industry. His work particularly emphasizes the integration of occupant behavior and practices into building performance models, recognizing that human factors are critical to achieving sustainable building performance in practice.

Nathan van de WouwView profile

Professor

• Dynamics and Control
• Mechatronics and Robotics
• Smart Manufacturing
• +6 more

Nathan van de Wouw is a Full Professor at the Mechanical Engineering Department of Eindhoven University of Technology (TU/e), affiliated with ICMS, EAISI Mobility, EAISI High Tech Systems, EAISI Foundational, and EIRES. He also holds an adjunct Full Professor position at the University of Minnesota and a part-time Full Professorship at Delft University of Technology. His research focuses on dynamics and control of mechanical systems, including mechatronics, robotics, smart manufacturing, energy systems, and networked control. He has supervised over 150 students and led numerous projects funded by industry partners like ASML, Philips, and Shell. Education: M.Sc. (with Honors) in Mechanical Engineering, TU/e (1994) Ph.D. in Mechanical Engineering, TU/e (1999) Research Interests: Nonlinear systems and control Model reduction and complexity analysis Data-driven and networked control strategies Applications in high-tech systems, autonomous vehicles, and energy systems Awards: IEEE Control Systems Technology Award (2015) for variable-gain control in motion systems Grants & Projects: Lead projects on mechatronic design, lithography systems, and thermodynamic optimization Collaborations with TNO, ASML, and industrial partners Labs & Teams: Member of TU/e’s Dynamics and Control group Affiliated with EAISI (Eindhoven AI Systems Institute)

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

• Stochastic Processes
• Operations Research
• Data-Driven Decision Making
• +4 more

Stella Kapodistria is an Associate Professor at Eindhoven University of Technology's Department of Mathematics and Computer Science, specializing in Stochastic Operations Research. She holds roles as EAISI High Tech Systems Associate Professor and editorial board member of journals like MCAP and PEIS. Her research focuses on data-driven decision-making, stochastic systems optimization, and maintenance policies, with applications in renewable energy, critical infrastructure, and cryptocurrency networks. She has secured grants including NWA-ORC, NWO Big Data, and TKI WoZ, and collaborates with industry partners in the Brainport region. Education: BSc (2003), MSc (2006, Hons.), and PhD (2009, summa cum laude) in Mathematics from the University of Athens. Postdoc at TU/e, followed by roles at Groningen University and TU/e's Stochastic Operations Research group. Teaching includes courses on Optimal Decision Making, Stochastic Performance Modeling, and Financial Mathematics. Research interests emphasize real-time learning, system resilience, and scalable algorithms for complex networks. Recent work addresses maintenance logistics, blockchain confirmation times, and wind energy prediction. She has published over 40 peer-reviewed articles and contributed to the 4TU Resilience Engineering Center. Awards include editorial leadership roles and grant funding. Advised 32 academic works and oversees industrial projects bridging theory and practice. Her labs and collaborations focus on adaptive systems, predictive analytics, and sustainable engineering solutions.

Mitra NasriView profile

Assistant Professor

• Real-Time Systems
• Embedded Systems
• Scheduling Algorithms
• +4 more

Mitra Nasri is an Assistant Professor at the Eindhoven University of Technology, affiliated with the College of Engineering's Department of Electrical Engineering. She contributes to the High Tech Systems Center and EAISI Foundational, focusing on interconnected resource-aware intelligent systems. Research Focus: Real-Time Systems, Scheduling Algorithms, Embedded Systems, Fault-Tolerant Computing, and Cyber-Physical Systems. Key Contributions: Development of scheduling frameworks for multi-rate task chains, response-time analysis techniques, and containerization strategies for real-time distributed applications. Her recent work includes advancements in weakly-hard timing constraints, parallel global scheduling, and cloud integration for embedded systems. She actively collaborates on projects like SAM-FMS and COMP4DRONES. Scientific Awards: Best Paper Award - RTAS 2022 Best Paper Award - RTNS 2016 Outstanding Paper Awards at RTAS 2017, 2022 and RTSS 2020 She teaches courses in Real-Time Systems, Operating Systems, and Automotive Software, and participates in organizing conferences like Embedded Systems Week and CompSys.

Rob BastenView profile

Associate Professor

• Operations Management
• Behavioral Operations Management
• Maintenance Optimization
• +7 more

Rob Basten is an Associate Professor in the Department of Industrial Engineering and Innovation Sciences at Eindhoven University of Technology (TU/e). He has been with TU/e since October 2014, initially as an Assistant Professor before being promoted. His work focuses on operations management and behavioral operations management, with particular expertise in maintenance, spare parts supply, and after-sales services for high-tech equipment. Basten's research is highly applied, often conducted in collaboration with industry partners such as ASML, NXP, Canon Production Printing, Marel Poultry, and the Ministry of Defence. Dr. Basten's educational background includes: Master's in Industrial Engineering and Management (2004) from University of Twente Master's in Computer Science (2005) from University of Twente PhD in Operations Management (2010) from University of Twente Rob Basten's research centers on improving after-sales services for high-tech equipment, with a focus on incorporating new technologies such as 3D printing and IoT. His work spans both analytical and empirical approaches, with increasing emphasis on behavioral operations management as human decision-makers interact with AI-based decision support systems. Basten investigates how to design and control after-sales service supply chains when spare parts can be 3D printed, and how to optimize maintenance policies for complex systems. His interdisciplinary research bridges operations management, maintenance engineering, and human behavior. Analysis of Basten's recent publications reveals a strong focus on the application of new technologies in after-sales services. Key trends include the integration of additive manufacturing in spare parts supply chains, condition-based and predictive maintenance driven by Industry 4.0 technologies, and the behavioral aspects of human-AI collaboration in maintenance decision-making. His work frequently addresses challenges in high-tech manufacturing contexts, particularly semiconductor equipment, with a growing emphasis on Industry 5.0 concepts that integrate human-centered approaches with advanced technologies. Dr. Basten has received recognition for his work, including: Finalist for the 2020 Daniel H. Wagner Prize for Excellence in Operations Research Practice ISIR Best Student Paper Award 2018 Rob Basten actively supervises numerous PhD students and has led several major research projects. He currently supervises nine PhD students including Maryam Azani, Ragnar Eggertsson, Bibi de Jong, Zhao Kang, Niccolò Maccarini, Aran Nasiri, Bas van Oudenhoven, İpek Tanıl, and Alireza Yazdani. He has successfully guided six PhD students to completion. Basten has been project leader and work package leader in significant research initiatives including ProSeLoNext (funded by NWO with industry co-funding), PrimaVera, SINTAS, and OCPROM projects. His research is consistently supported by both public funding agencies and industry partnerships, reflecting the practical relevance of his work. Basten is an active member of the Operations, Planning, Accounting & Control group at TU/e and contributes to the EAISI High Tech Systems initiative. He has organized key academic events including the first two editions of the Maintenance Research Day and the Behavioral Operations Conference 2019. His work connects academic research with industry practice through ongoing collaborations with leading high-tech companies, creating a dynamic research environment focused on solving real-world challenges in maintenance and service logistics.

V. MenkovskiView profile

Associate Professor

• Graph Neural Networks
• Machine Learning
• Deep Learning
• +7 more

Dr. V. Menkovski serves as an Associate Professor in Data Mining at Eindhoven University of Technology's Department of Mathematics and Computer Science. He also holds associate professor positions with EAISI Health and EAISI High Tech Systems, and is an ICMS Affiliated member. His work spans multiple domains of artificial intelligence and computational physics, with significant contributions to fusion energy research. Mathematics and Computer Science, Data Mining (Primary Appointment) EAISI Health (Associate Professor) EAISI High Tech Systems (Associate Professor) ICMS (Affiliated Member) Menkovski's research focuses on Graph Neural Networks, Machine Learning, Deep Learning, and their applications in diverse fields from plasma physics to metamaterials. His work demonstrates strong interdisciplinary connections, particularly between computer science and fusion energy research. He has developed novel approaches for crowd simulation, tokamak plasma monitoring, and metamaterials homogenization using advanced neural architectures. His fingerprint reveals expertise in Quality-of-Experience, Autoencoders, Neural Networks, Annotation, Graph Neural Networks, Video Streaming, Adversarial Machine Learning, and Anomaly Detection. Analysis of his recent publications (2023-2025) shows a clear trend toward applying Graph Neural Networks to complex physical systems, particularly in fusion energy research and materials science. His work increasingly integrates symmetry principles with neural architectures, as seen in his research on equivariant networks for metamaterials and symmetry-informed networks for zeolite analysis. There's also significant focus on practical applications in fake news detection, anomaly detection, and plasma state monitoring. Best Paper Award ICPM 2021 (with Sommers and Fahland) Best Paper Award of LoG 2022 (with multiple co-authors including Huang, Chen, Fang, Zhao, Yin, Pei, Mocanu, Wang, Pechenizkiy, and Liu) Menkovski teaches several advanced courses including Deep Learning, Advanced Topics in Artificial Intelligence, and Sociophysics 2, which runs through August 2025. His supervised work portfolio includes 79 projects, indicating substantial mentorship activity. He has received significant media attention for his research, including coverage by 11 news outlets, blog posts, and mentions on social media platforms. His work on 'Supervised Learning of Process Discovery Techniques Using Graph Neural Networks' was particularly noted in media coverage. His research involves collaboration with multiple institutions and teams, particularly in fusion energy research (Eurofusion Tokamak Exploitation Team, ASDEX-Upgrade team, EUROfusion MST1 Team). He works closely with researchers across disciplines, including physicists working on tokamak plasma and materials scientists studying metamaterials and zeolites.

Moez LouatiView profile

Adjunct Associate Professor

• Fluid Mechanics of Waves
• Pipeline Hydraulics
• Water Loss Management
• +5 more

Moez Louati is an Adjunct Associate Professor in the Department of Civil and Environmental Engineering at The Hong Kong University of Science and Technology (HKUST). His expertise spans fluid mechanics of waves, pipeline hydraulics, and smart water infrastructure. He holds a PhD from HKUST, supported by scholarships and the SENG PhD Fellowship Award. His research focuses on pipeline defect detection, hydraulic transients, and smart urban water systems, with notable contributions including the discovery of Bragg resonance connections in pressurized pipes. He has led projects funded by RGC, ITF, and international collaborations, and co-founded Hyele Limited, a startup developing pipeline condition assessment technologies. Education: B.Eng. (Electro-Mechanical Engineering) and MPhil (Renewable Energy) from Tunisia’s National School of Engineering of Sfax (ENIS). Professional credentials include HKIE chartered engineer status and roles as Chair of IAHR’s Africa Division and Assistant Editor of the Journal of Hydraulic Research. Research Interests: Fluid mechanics of waves, computational fluid dynamics (CFD), machine learning for infrastructure optimization, and sustainable water management. Key projects include Smart Urban Water Supply Systems and high-frequency wave-based defect detection methods. Awards: Geneva Innovations Expo 2023/24 awards, Straub Award nomination, and HKUST’s Tech-Ship startup funding. Active in international organizations and advisory roles for Hong Kong’s WSD and DSD. Labs & Facilities: Designed three water lab facilities at HKUST and collaborated globally on projects like the FREJLEV Facility’s air-water stratified flow studies. Holds five patents in pipeline diagnostics and HPC applications.

Ravindra ShindeView profile

Researcher

• Computational Materials Science
• Quantum Chemistry
• Exascale Scientific Computing
• +5 more

Ravindra Shinde is a Research Scientist at the University of Twente , Netherlands, affiliated with the Computational Chemical Physics group under the Faculty of Science and Technology . He is also the founder of The Science Dev , a tech and research blog. Education : Ph.D. in Physics (2014) and M.Sc. in Physics (2009) from IIT Bombay, India. His research spans Exascale Scientific Computing , Quantum Chemistry , Time-Dependent DFT , and Topological Materials . He specializes in method development for quantum mechanical simulations and code modernization for high-performance computing . Recent publications focus on Exascale Computing challenges , Quantum Monte Carlo accuracy, and machine learning integration in force field modeling. Notable awards include SERB Postdoctoral Fellowship and multiple Best Presentation Awards . Scientific Awards : Best Poster Award, UC Riverside (2019) Best Presentation Awards, SERB 2018 & IISc 2018 National Postdoctoral Fellowship, India (2016-2018) All India GATE Physics Rank 73 (2009)

Koen TielsView profile

Assistant Professor

• System Identification
• Machine Learning
• Nonlinear Dynamical Systems
• +3 more

Koen Tiels is an Assistant Professor in the Control Systems Technology (CST) group at the Department of Mechanical Engineering of Eindhoven University of Technology (TU/e). His research focuses on the identification of nonlinear dynamical systems, combining techniques from system identification and machine learning. Education Master's in Electromechanical Engineering, Vrije Universiteit Brussel (2010) PhD in Electromechanical Engineering, Vrije Universiteit Brussel (2015) His work contributes to areas such as magnetic hysteresis modeling using physics-aware neural networks, optimal control design with low computational cost, and kernel-based identification methods for sampled data. He has collaborated with institutions like Uppsala University and TU/e's EAISI (Eindhoven Artificial Intelligence Systems Institute) on high-tech systems and mechatronics. Recent publications emphasize applications in recurrent neural networks for magnetic hysteresis, sparse modeling of piezoelectric systems, and robust experiment design for MIMO systems. His research aligns with UN Sustainable Development Goals related to clean energy and advanced technologies.

Debargha DeyView profile

Assistant Professor

• Human Factors in Transportation
• Intelligent Transportation Systems
• Ubiquitous Computing
• +4 more

Debargha (Dave) Dey is an Assistant Professor in the Department of Industrial Design at Eindhoven University of Technology. He holds dual roles as an EAISI Foundational Assistant Professor and EAISI High Tech Systems researcher. His work focuses on human factors in Intelligent Transportation Systems and Future Mobility, with expertise in ubiquitous computing, multimodal interaction, and automotive user interfaces. He earned a PhD cum laude from TU Eindhoven in 2020, followed by postdoctoral research at TU Eindhoven and Cornell Tech. Education background includes a Master's in Computer Science from Vanderbilt University (2012), PDEng in User-System Interaction from TU Eindhoven (2017), and a PhD in Human-Machine Interaction (2020). Research interests emphasize traffic safety, autonomous vehicle-pedestrian interaction, and cross-cultural behavior modeling for AV development. His work addresses UN Sustainable Development Goals related to safe cities and communities (SDG 11). Recent research explores external human-machine interfaces (eHMIs) for autonomous vehicles, using VR simulators and participatory design methods. He investigates multimodal communication (light/sound) effectiveness and collaborative in-car activities' fairness. Awards: AutomotiveUI '22 Best Paper Award (2022) CHI '20 Best Paper Award (2020) AutomotiveUI '20 Honorable Mention (2020) Best Demo Award (2017) Active in ACM SIGCHI and AutomotiveUI communities, he serves on steering committees and organizes conferences like AutomotiveUI 2023. His work bridges academia and industry through collaborative projects on smart mobility solutions.

Maria VlasiouView profile

Professor

• Stochastic processing networks
• Queueing theory
• Energy networks and EV charging
• +6 more

Maria Vlasiou is a Full Professor of Mathematics of Operations Research at the University of Twente, an Associate Professor at Eindhoven University of Technology, and a Research Fellow of EURANDOM. Her work focuses on stochastic processing networks, particularly layered architectures arising in energy systems, high-tech manufacturing supply chains, and bandwidth-sharing networks. Education & Qualifications UTQ (University Teaching Qualification) diploma obtained in 2010; continuous professional-education training thereafter. Research Interests Vlasiou’s research spans the performance analysis of stochastic networks, with special emphasis on: Electric-vehicle charging networks and congestion control. Extreme-value theory for fork-join queues inspired by ASML/Philips/Boeing supply chains. Lévy-driven queues, heavy-tailed risk models, and large-deviations asymptotics. Resource-allocation problems involving non-convex optimisation and heavy-traffic limits. She combines rigorous probabilistic methods (fluid & diffusion approximations, perturbation analysis) with practical applications in energy, logistics and manufacturing. Scientific Awards & Recognition AMSI-ANZIAM Lecturer 2017 Marcel Neuts Student Paper Award (MAM8) 3rd Prize, 8th Conference in Actuarial Science & Finance 2014 Best Paper Award, ICORES 2013 Excellent Course Prize, University of Twente 2021 Grants & Projects She has attracted over €4.5 million in external funding from NWO, Australian Research Council, 4TU.CEE, and industry, including current NWO LTP ROBUST programme on “AI for Energy Grids” (2023-2032). Teaching & Supervision Vlasiou teaches Probability, Stochastic Processes, Performance Modelling and Infectious-Disease Epidemiology. She has supervised nine PhD dissertations and numerous BSc/MSc theses, internships and PDEng projects, and leads educational-innovation grants on mini-lectures and student engagement. Editorial & Leadership Roles Associate editor for OR Spectrum, IISE Transactions, Math. Methods of OR, and INFOR. She represents the Netherlands at the IMU Committee for Women in Mathematics and serves on advisory boards for national women-in-math organisations in Cyprus and Greece.

O.R. WilliamsView profile

Researcher

• Astronomy
• Astrophysics
• High Energy Physics
• +4 more

Dr. O.R. (Rees) Williams serves as the Target Scientific Project Manager at the Center for Information Technology, University of Groningen. His position involves managing the €32 million Target project and the Euclid Netherlands Science Data Centre. His research focuses on applying expertise in large data volume processing to astronomical projects including LOFAR Long Term Archive, OmegaCAM, and the Euclid mission. Williams began his career as a High Energy Physicist but transitioned to High Energy Astronomy after completing his PhD. With over 25 years of experience processing astronomical data from space projects, he directed the high-tech company Praesepe from 1996 to 2009 before returning to academic research. His current work centers on the Euclid mission which aims to map the sky in optical and near-infrared wavelengths to study dark matter and dark energy. His research interests span X-ray and gamma-ray emissions from quasars, Gamma-ray Bursts, and developing information systems for large data volumes. Williams' recent publications (2020-2025) demonstrate a strong focus on Euclid mission data analysis, examining galaxy clusters, globular clusters, stellar structures, and developing methods for processing astronomical data. His work combines expertise in astronomy with advanced data science techniques. Williams actively collaborates with numerous international research groups, as evidenced by his co-authorship on multiple Euclid Consortium papers. His work contributes to UN Sustainable Development Goals through astronomical research that advances scientific knowledge and technological development. As manager of the Euclid Netherlands Science Data Centre, Williams oversees the processing infrastructure for Euclid mission data. His leadership in the Target project connects scientific research groups with innovative businesses to develop complex data systems for astronomy and bioinformatics applications including GLIMPS and Lifelines Research Workspace Environment.

Gábor MolnárView profile

Associate Professor

• High-Tech Innovations
• Big Data
• Digital Transformation and Strategy
• +1 more

Gábor Molnár, PhD, is an Associate Professor of Applied Sciences at Wittenborg University and holds adjunct roles as Associate Professor at the University of Colorado Boulder's ATLAS Institute and Fellow at Silicon Flatirons. He also serves as an Evangelist of Video Technology and Science at Divitel and advises e-Conomics, a telecom policy consultancy. Dr. Molnár's expertise spans high-tech innovations, AI-driven digital transformation, and data policy, with over 20 years in the high-tech industry. His research emphasizes bridging human and machine collaboration, AI ethics, and the socio-economic impacts of broadband infrastructure. He holds degrees from Corvinus University of Budapest (BSc Economics), Budapest University of Technology and Economics (MSc Electrical Engineering), and the University of Colorado Boulder (MSc/PhD Telecommunications). Education: BSc Economics (Corvinus), MSc Electrical Engineering (Budapest University of Technology), MSc/PhD Telecommunications (CU Boulder) His research focuses on human-centric AI, digital transformation strategies, and telecom policy. Recent work explores HRM's role in AI adoption, algorithm factories, and broadband accessibility's socio-economic effects. He has contributed to journals like Journal of Industrial Economics and Discover Artificial Intelligence . As a consultant, he advises Fortune 500 firms, SMEs, and governments on digital transformation and high-tech strategies. His articles highlight trends in algorithmic video delivery, 5G network sharing, and spectrum policy, reflecting his dual academic and industry perspectives.