جست‌وجوی استاد و پژوهشگر

Antonio Gonzálezمشاهده پروفایل

استاد

• Computer Architecture
• Microarchitecture
• Compilers
• +۷ مورد دیگر

Antonio González is a full Professor at the Barcelona School of Informatics (FIB), Universitat Politècnica de Catalunya (UPC), where he is affiliated with the Department of Computer Architecture. He leads the Microarchitecture and Compilers (ARCO) research group, focusing on robust and energy-efficient computing systems, including general-purpose processors, GPUs, and cognitive computing architectures. His research spans computer architecture, microarchitecture, compilers, GPU design, and deep learning acceleration, with a strong emphasis on energy efficiency and memory optimization. His work integrates hardware and compiler techniques to enhance performance in modern computing systems. The recent publications highlight a strong trend in energy-efficient DNN acceleration, GPU microarchitecture, memory systems, and near-data processing. His team explores novel quantization methods, adaptive caching, and architectural extensions for vision and AI workloads, published in top-tier venues such as MICRO, ISCA, and IEEE/ACM journals. ACM Fellow (2020) ICREA Academia Award (2014, 2019, 2024) UPC Duran Farell Award (2008) HiPEAC 2024 Paper Award (twice) González has supervised several PhD students and leads competitive R&D+i projects, including those funded by ICREA and the European Research Council. His research group collaborates extensively within UPC and with international institutions. He is actively involved in advancing computer architecture through innovation in simulation, hardware design, and AI acceleration. He is a member of IEEE and ACM, and his work continues to influence both academic and industrial developments in high-performance and energy-efficient computing.

• Robotics
• Artificial Intelligence
• Robotic Manipulation of Deformable Objects
• +۷ مورد دیگر

Carme Torras Genís is a Research Professor at the Spanish National Research Council (CSIC), affiliated with the Institute of Robotics and Industrial Informatics (IRI) in Barcelona and the Technical University of Catalonia (UPC). Her career spans over three decades, focusing on robotics, neurocomputing, and artificial intelligence with applications in healthcare and deformable object manipulation. M.Sc. in Mathematics (University of Barcelona, 1978) M.Sc. in Computer Science (University of Massachusetts, 11981) Ph.D. in Computer Science (UPC, 1984) Research Interests : Robotic manipulation of deformable objects (especially textiles) Neurocomputing and machine learning for robotic control Human-robot interaction and assistive robotics Computational topology for cloth state representation Ethics in social robotics and AI Medical applications of robotics for neuromuscular disease assessment Scientific Leadership : ERC Advanced Grant recipient (2016) IEEE and EurAI Fellow Coordinator of Horizon Europe project SoftEnable and former ERC project CLOTHILDE Editorial leadership in IEEE Transactions on Robotics and multiple journals Active in ethics committees and AI policy advisory boards Advisory Committee of Ethics in AI (Catalan Government) Vice-President of CSIC Ethics Committee Member of Royal Academy of Engineering (Spain)

Nicolò Cesa-Bianchiمشاهده پروفایل

استاد

• Machine Learning
• Online Learning
• Multi-armed Bandit Problems
• +۷ مورد دیگر

Nicolò Cesa-Bianchi is a Professor of Computer Science at the University of Milan, where he serves as head of the Computer Science programs. He is also associated with the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano. Cesa-Bianchi holds significant leadership roles including Board member, Fellow and co-director of the Milan unit of the European Laboratory for Learning and Intelligent Systems (ELLIS), and membership in the prestigious Accademia Nazionale dei Lincei. He is also involved with The European Lighthouse on Secure and Safe AI (ELSA), The European Lighthouse of AI for Sustainability (ELIAS), and The FAIR foundation. Professor Cesa-Bianchi's research focuses on the theoretical foundations of machine learning, with special emphasis on sequential decision making and online learning algorithms. His work spans multiple areas including multi-armed bandit problems, regret analysis, prediction with expert advice, and learning on graphs. He has made significant contributions to understanding the theoretical limits of learning algorithms and developing efficient methods for various learning scenarios. His research has important applications in online markets, social networks, and bioinformatics. His monographs 'Prediction, Learning, and Games' and 'Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems' are considered seminal works in the field. His recent publications demonstrate continued leadership in advancing the theoretical understanding of machine learning, with 2024-2025 papers covering cooperative online learning, multitask learning, fair trade mechanisms, and refined analyses of bandit algorithms. The research shows increasing focus on practical economic applications while maintaining strong theoretical foundations. Google Research Award Xerox Foundation UAC Award Member of the Accademia Nazionale dei Lincei ELLIS Fellow Cesa-Bianchi has been deeply involved in academic service, having served as action editor for the Machine Learning Journal, IEEE Transactions on Information Theory, and the Journal of Machine Learning Research. He currently serves as associate editor for the Journal of Information and Inference and TheoretiCS. He has held leadership positions including President of the Association for Computational Learning and member of the steering committee for the EC-funded Network of Excellence PASCAL2. He was program chair of the 13th Annual Conference on Computational Learning Theory and the 13th International Conference on Algorithmic Learning Theory. He leads the Laboratory for AI and Learning Algorithms (ALGA) at the University of Milan, which focuses on theoretical and applied research in machine learning. His international collaborations are extensive, with visiting positions at UC Santa Cruz, Graz Technical University, Ecole Normale Supérieure in Paris, Google, and Microsoft Research. As an educator, he teaches advanced courses including Reinforcement Learning and Statistical Methods for Machine Learning, and has supervised numerous students through the years.

Bryon Aragamمشاهده پروفایل

دانشیار

• Causality
• Statistical Machine Learning
• Probabilistic Modeling
• +۶ مورد دیگر

Bryon Aragam is an Associate Professor of Econometrics and Statistics and Robert H. Topel Faculty Scholar at the University of Chicago Booth School of Business. His research focuses on the intersection of causality, statistical machine learning, and probabilistic modeling, with particular emphasis on applications to artificial intelligence systems including large language models like ChatGPT and generative models like DALL-E. Dr. Aragam completed his PhD in Statistics and a Masters in Applied Mathematics at UCLA, where he was an NSF graduate research fellow. Prior to joining the University of Chicago, he was a project scientist and postdoctoral researcher in the Machine Learning Department at Carnegie Mellon University. Research Focus: Causal structure learning in probabilistic generative models Key Areas: Causal machine learning, deep generative models, latent variable models, statistical learning theory Applications: AI interpretability, ethics, and fairness in artificial intelligence systems Teaching: Business Statistics, Econometrics and Statistics Colloquium His recent publications demonstrate a strong theoretical foundation combined with practical applications, particularly in understanding and improving AI systems. His work spans causal discovery, graphical models, deep learning, and latent variable modeling, with particular attention to the theoretical properties of these methods and their applications to real-world AI challenges. The research shows a progression toward increasingly complex problems in causal representation learning and AI interpretability. Scientific Awards: Robert H. Topel Faculty Scholar NSF Graduate Research Fellow Dr. Aragam's work has been published in top statistics and machine learning venues including the Annals of Statistics, Neural Information Processing Systems (NeurIPS), the International Conference on Machine Learning (ICML), and the Journal of Machine Learning Research (JMLR). His research group publishes broadly across both statistical and machine learning communities, demonstrating the interdisciplinary nature of his work at the intersection of statistics, machine learning, and causal inference. As a data science consultant for technology and marketing firms, Dr. Aragam has applied his expertise to problems in survey design, customer retention, logistics, and ranking, bridging the gap between theoretical research and practical applications.

Jordi Perello Muntanمشاهده پروفایل

دانشیار

• Telecommunications Networks
• Optical Fiber Networks
• Optical Networks
• +۱۳ مورد دیگر

Jordi Perelló Muntan is an Associate Professor in the Department of Computer Architecture at the Universitat Politècnica de Catalunya (UPC), Barcelona, Spain, where he is also affiliated with the Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona (ETSETB). He is a member of the Broadband Communications Systems and Architectures (CBA) and IDEAI-UPC research groups, focusing on advanced optical and future internet networking technologies. Research Interests: His research spans telecommunications networks, optical fiber and optical networking, resource optimization, network architectures, and the Future Internet. He investigates performance optimization in 5G transport networks, Spatial Division Multiplexing (SDM), Recursive Inter-Network Architecture (RINA), elastic optical networks, and cognitive networking. His work integrates SDN, network virtualization, and green networking principles for scalable and efficient infrastructures. Publication Trends: His recent publications focus on probabilistic constellation shaping in multicore fiber networks, cognitive strategies for optical margin reduction, RINA-based QoS assurance, and migration planning toward spectrally-spatially flexible optical networks. These reflect a strong trend toward intelligent, adaptive, and energy-efficient network design for future communication systems. Scientific Awards: Co-recipient of the 2020 Fabio Neri Best Paper Award Runner-up (Elsevier Journal of Optical Switching and Networking) Co-recipient of the ONDM 2021 Best Paper Award Co-recipient of the 2019 IEEE Communications Society Charles Kao Award Co-recipient of the ONDM 2012 Best Student Paper Award Advising and Grants: He has advised multiple PhD students on topics including RINA, optical network planning, and virtual provisioning. He has led or participated in major European (H2020, FP7) and national (PID, TEC) research projects such as SLICENET, PRISTINE, TRAINER, and ALLIANCE, focusing on 5G, RINA, and sustainable network infrastructures. Labs and Teams: He is an active member of the CBA research group at UPC, contributing to experimental and theoretical advancements in optical and programmable networks. His team collaborates internationally on testbed development and standardization efforts in next-generation networking.

• Biomedical Signal Processing
• Respiratory Monitoring
• Sleep Disorders
• +۴ مورد دیگر

Prof. Raimon Jané Campos is a leading figure in biomedical signal processing at the Universitat Politècnica de Catalunya (UPC) and Universitat de Barcelona (UB). As co-director of UPC's Biomedical Signal and System Group (CREB) and coordinator of the Biomedical Engineering PhD Programme, he bridges engineering and clinical applications. His work focuses on respiratory and sleep disorder diagnostics, with significant contributions to COPD and sleep apnea monitoring through wearable devices and machine learning. PhD in Biomedical Engineering (UPC, 1989) Visiting researcher at Université de Nice-Sophia Antipolis Vice-president of Spanish Society of Biomedical Engineering Research spans respiratory mechanics , sleep-disordered breathing , acoustic biomarkers , bioimpedance , and machine learning in biomedical contexts . His 2025 work on microcalorimetric pathogen classification and 2024 spiking neural networks for apnea detection demonstrate cutting-edge integration of computational methods with physiological monitoring. Articles from 2017-2024 reveal consistent focus on non-invasive diagnostics , cardiorespiratory synchronization , and smartphone-based health solutions . Awarded the Barcelona City Technology Research Award (2005) and serving on the International Advisory Board for Physiological Measurement since 2010, his career combines academic leadership with real-world clinical translation through IBEC's technology transfer initiatives.

Claudia Patricia Ayala Martinezمشاهده پروفایل

مدرس

• Empirical Software Engineering
• Off-The-Shelf Adoption
• Requirements Engineering
• +۴ مورد دیگر

Claudia Patricia Ayala Martinez serves as a Lecturer in the Department of Service and Information Systems Engineering at the Barcelona School of Informatics (FIB), Polytechnic University of Catalonia (UPC). She is actively involved in research through the GESSI - Group of Software and Service Engineering and the UPC inSSIDE - integrated Software, Services, Information and Data Engineering research groups. Her career spans over two decades of academic contributions in software engineering with consistent publication output. Dr. Ayala Martinez's research focuses on Empirical Software Engineering, Off-The-Shelf Adoption, Requirements Engineering, and Software and Architectural Quality. Her work demonstrates an evolution from traditional software engineering topics toward increasing integration with machine learning and AI systems. Recent publications show particular emphasis on software quality indicators, ML pipeline design principles, trustworthiness of ML models, and green computing in software systems. Analyzing her publication trends reveals a consistent research trajectory with growing focus on AI/ML integration in software engineering. Her work spans empirical studies, systematic literature reviews, and practical industrial applications. The research shows strong connections between software quality metrics, architectural decisions, and emerging technologies, with increasing attention to ethical considerations in ML systems and sustainability in software development. Most-Influential Paper Award at the 30th IEEE International Requirements Engineering Conference Dr. Ayala Martinez has participated in numerous competitive R&D projects including those funded by the Spanish National Research Plan, Horizon 2020, and the Catalan Innovation Strategy. Her collaborative network includes extensive work with Professor Javier Franch Gutierrez (69 joint publications), Silverio Juan Martinez Fernandez (26 joint publications), and Cristina Gomez Seoane (20 joint publications). Her research has been supported by various national and European funding programs focusing on software engineering, quality assessment, and open source adoption. She is actively involved with the GESSI and inSSIDE research groups at UPC, which focus on integrated software, services, information, and data engineering. These groups maintain strong industry connections and have produced significant research in empirical software engineering, reference architectures, and quality assessment methodologies. Her recent work shows increasing collaboration with researchers working at the intersection of software engineering and artificial intelligence.

Pere-Pau Vázquezمشاهده پروفایل

استادیار

• Information Visualization
• Scientific Visualization
• Medical Data Visualization
• +۶ مورد دیگر

Pere-Pau Vázquez is an Assistant Professor in AI for Visual Computing at the Computer Vision Lab, TU Wien, Austria . Previously, he held academic positions at the ViRVIG Group and Facultat d'Informàtica de Barcelona (UPC) , where he taught courses in Programming, Computer Graphics, and Visualization for over 20 years. His research focuses on Information Visualization, Scientific Visualization, Medical Data Visualization, Molecular Visualization, and AI applications to Visual Computing . Current Teaching : Data Visualization, Fast Realistic Rendering, Information Visualization, Medical Images, Scientific Visualization, Virtual Reality, and 3D Medical Visualization. Former PhD Students : Elena Molina, Alexandra Cortez, Jesús Díaz, Pedro Hermosilla, Eva Monclús. His scientific awards include the Best PhD Thesis Award (UPC, 2003), Best Student Paper Award (SPIE, 2012), and Best Paper Award (International Conference on Computer Graphics Theory and Applications, 2013). Recent publications explore AI integration in biomedical visualization, molecular data analysis, and interactive techniques for volume rendering. He serves on the EuroGraphics Executive Board as Secretary and is active in steering committees for EuroVis and Visual Computing for Biology and Medicine . His work bridges Computer Graphics, Artificial Intelligence, and Human-Computer Interaction , with applications in medical and molecular data analysis.

• Statistical Modeling
• Biostatistics
• Biomedicine
• +۷ مورد دیگر

Dae-Jin Lee is an Assistant Professor at IE University’s School of Science and Technology, specializing in statistical modeling and data science. Previously, he served as a Research Line Leader at the Basque Centre for Applied Mathematics (BCAM) and coordinated the Knowledge Transfer Unit in Data Science/AI. His academic background includes a Ph.D. in Mathematical Engineering (2010) from Universidad Carlos III de Madrid and postdoctoral research at CSIRO (Australia). His research focuses on statistical methods for complex data, including penalized splines, tensor product smooths, and applications in biomedicine, epidemiology, environmental science, and sports analytics. He has led multidisciplinary projects funded by public and industry grants, collaborating globally with experts across fields like engineering, medicine, and biology. Key research themes include predictive modeling for health outcomes (e.g., SARS-CoV-2 pneumonia severity), sports injury prevention, and AI in healthcare. His work integrates machine learning with traditional statistical techniques, addressing real-world challenges like pedestrian dynamics simulations and automated medical diagnostics. He is actively involved in scientific organizations, including the Spanish Biostatistics Society and the Statistical Modelling Society. His recent publications highlight innovations in growth curve modeling, AI ethics, and spatiotemporal data analysis, reflecting his commitment to advancing both theoretical and applied statistics.

• Fluid Dynamics
• Turbulence
• Heat Transfer
• +۶ مورد دیگر

Andrea Ianiro is a Full Professor in the Aerospace Engineering Department at Universidad Carlos III de Madrid (UC3M), where he leads research in fluid dynamics, turbulence, and heat transfer. His work bridges experimental techniques and machine learning applications for flow analysis and control. He serves as Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) and directs the EFM Lab (Experimental Fluid Mechanics Laboratory) at UC3M. Professor Ianiro's research focuses on turbulence characterization, boundary layer flows, and the application of machine learning to fluid mechanics problems. His work spans experimental techniques including Particle Image Velocimetry (PIV), infrared thermography, and advanced data processing methods. Recent research emphasizes data-driven approaches for flow field reconstruction, turbulence control, and heat transfer optimization in wall-bounded flows. His projects often combine theoretical, experimental, and computational approaches to address complex fluid mechanics challenges. The analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional fluid mechanics. His work increasingly focuses on using deep learning techniques (particularly CNNs and GANs) for flow field prediction from limited measurements, developing meshless computational methods for flow analysis, and applying optimization techniques (including genetic algorithms) to heat transfer enhancement. His research maintains a strong experimental foundation while embracing data-driven approaches to tackle turbulence modeling challenges. Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) Professor Ianiro leads multiple significant research projects including SPANDRELS (SParse AND paRsimonious Event-based fLow Sensing, 2025-2030), HumanIC (Human-Centric Indoor Climate for Healthcare Facilities, 2024-2027), and EXCALIBUR (Extraction of machine learning strategies for turbulent flow control, 2023-2026). His work has attracted funding from the European Commission, Spanish National Research Agency, and industry partners including Airbus. He has supervised numerous theses on topics including AI-based sensing of turbulent flows, convective heat transfer control, and turbulent boundary layers. At UC3M, Professor Ianiro directs the Experimental Fluid Mechanics Laboratory (EFM Lab), which focuses on advanced measurement techniques for fluid flow and heat transfer characterization. The lab specializes in PIV/PTV techniques, infrared thermography, and the development of novel experimental approaches for turbulence research. Current research directions include machine learning applications for flow field reconstruction, plasma-based flow control, and heat transfer optimization in complex flow configurations.

Agustín Zaballos Diegoمشاهده پروفایل

استادیار

• Smart Grids
• Internet of Things (IoT)
• Sensor Networks
• +۳ مورد دیگر

Agustín Zaballos Diego is an Assistant Professor in the Department of Computer Engineering at University Ramon Llull (URL), Barcelona, Spain, since 1999. He serves as Research Coordinator in the Department of Engineering at La Salle Campus Barcelona and leads the R&D Networking and Security Area since 2002. His academic background includes a PhD in Data Networks and Internet Technologies (2012), an International MBA (2014), and an M.S. in Electronic Engineering (2000). University: University Ramon Llull (URL) Department: Department of Computer Engineering Research Group: GRITS Research Focus: Real-time QoS-aware routing protocols in Smart Grids, Ubiquitous Sensor Networks, and IoT communications. His work bridges telecommunications, computer science, and energy systems through projects like OPERA (FP6), INTEGRIS (FP7), and FINESCE (FP7). Publication Trends: Recent articles highlight advancements in HF communications for Antarctic research, hybrid genetic algorithms for traffic engineering, IPv6 testing, and Industry 4.0-related networking solutions. Keywords span Smart Grids, IoT, Sensor Networks, and QoS optimization. Collaborative Projects: Key initiatives include the Antarctica Project , ATHIKA (ICT in healthcare), ENVISERA (environmental sensor networks), HOTSUP (online teaching innovation), PLANET4 (AI/ML in industry), and XIoT (IoT scalability challenges).

Alejandro Villaverdeمشاهده پروفایل

استاد پژوهشی

• Systems Biology
• Control Theory
• Mathematical Modeling
• +۵ مورد دیگر

Alejandro F. Villaverde is a Ramón y Cajal research fellow in the Department of Systems & Control Engineering at the School of Industrial Engineering, University of Vigo, Spain. He also serves as a Research fellow at CITMAga since 2022. Previously, he worked as a postdoctoral researcher at IIM-CSIC from 2016-2020. His research focuses on the modeling of dynamical systems with particular emphasis on biological applications. Villaverde earned his PhD in Systems and Control Engineering from University of Vigo between 2005 and 2009. His academic career has centered at Spanish institutions with a strong interdisciplinary approach bridging engineering, mathematics, and biology. His primary research interests include systems biology, control theory, and mathematical modeling, with specialized expertise in structural identifiability, observability analysis, and computational tools for dynamic modeling of biological systems. Villaverde's work addresses fundamental challenges in building reliable mathematical models of complex biological processes, with applications spanning immunology to microbial communities. His theoretical contributions have practical implications for improving model reliability and predictive power in biological research. Villaverde has published extensively in top journals including PLOS Computational Biology, Bioinformatics, and IEEE/ACM Transactions on Computational Biology. His recent publications (2023-2025) reveal a consistent research trajectory focused on developing theoretical frameworks for biological model analysis, creating practical software tools, and applying these methods to cutting-edge problems. His work shows particular strength in identifying and addressing fundamental limitations in modeling approaches, especially regarding parameter identifiability and model observability constraints. Among the top 2% Scientists Worldwide 2024 (Stanford University list) Recognition as one of the EEI's top valued instructors at University of Vigo's School of Industrial Engineering Villaverde leads multiple significant research projects including DYNAMO-bio (funded by Ministry of Science, Innovation and Universities), SICOMORO (focusing on symmetries in biological communities), and PREDYCTBIO. His group actively develops open-source software tools such as STRIKE-GOLDD for structural identifiability and observability analysis. The laboratory, part of the BICO research group, includes several researchers and students working on various aspects of dynamic modeling in biology, with recent additions including Mahmoud Shams Falavarjani, Adriana González Vázquez, and multiple interns working on specialized projects.

Felix Gomez Marmolمشاهده پروفایل

دانشیار

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Felix Gomez Marmol is an Associate Professor at the University of Murcia's Faculty of Informatics, Department of Information and Communication Engineering. His research focuses on cybersecurity, artificial intelligence, network security, and IoT security. He holds a PhD in Computer Science from the University of Murcia (2010), supervised by Dr. Gregorio Martínez Pérez. Key research interests include adaptive intrusion detection systems, dark web analysis, and AI-driven cybersecurity frameworks. He leads the Intelligent Systems and Telematics research group and previously contributed to the Sistemas Inteligentes group. His work emphasizes practical applications such as the SCORPION Cyber Range platform for cybersecurity training and gamification. Recent projects involve detecting hate networks on social media, optimizing malware defense using transfer learning, and developing SIEM systems for IoT environments. His contributions span technical papers on cybersecurity education, ethical hacking fundamentals, and blockchain-based security solutions. Prof. Gomez Marmol has collaborated on initiatives like the COBRA framework for simulating advanced persistent threats (APTs) and the COnVIDa dashboard for pandemic-related data analysis. His research bridges theoretical advancements with real-world cybersecurity challenges.

• Pharmaceutical Technology
• Drug Delivery Systems
• Nanomedicine
• +۳ مورد دیگر

Helena Roco Taboada is an Interim Lecturer in the Department of Pharmacology, Pharmacy and Pharmaceutical Technology at the University of Santiago de Compostela. She holds a PhD from the same institution for her thesis on 'Quality-by-design approach for the development of lipid-based nanosystems for anti-mycobacterial therapy' (2021), supervised by Dr. Mariana Landín Pérez and Dr. Carmen Remuñán López. She is affiliated with the Faculty of Sciences and is part of the Strategic Grouping in Materials (AEMAT) and the ID-FARMA research group focusing on dosage forms and drug release systems. Her research interests center on pharmaceutical technology, drug delivery systems, and nanomedicine, with a focus on applications in anti-mycobacterial therapy, osteoporosis treatment, and bone regeneration. Her work integrates lipid-based nanosystems, targeted drug delivery, and AI-driven formulation strategies. Recent projects include developing lipid nanoparticles for clofazimine delivery and thermosensitive hydrogels for osteoarthritis management. Her publications span topics like nanoparticle functionalization, cryoprotectant optimization in lyophilization, and AI tools for nanostructured lipid carrier design. She is active in interdisciplinary collaborations within materials science and biomedical engineering.

• Autonomous Vehicles
• Intelligent Transportation Systems
• Robotics
• +۷ مورد دیگر

Miguel Ángel Sotelo Vázquez is a full Professor at the University of Alcalá, leading the INVETT Research Group (Intelligent Vehicles and Traffic Technologies). He holds the Department of Automatic Control and specializes in autonomous systems, particularly in path planning, sensor fusion, and human-vehicle interaction. His research integrates machine learning, robotics, and control theory to address challenges in intelligent transportation systems. He earned his Ph.D. in 2001 with a thesis on autonomous vehicle navigation in partially known environments. His work emphasizes real-world deployment, explainable AI, and safety-critical systems. Recent projects focus on lane change prediction, pedestrian behavior modeling, and cybersecurity for autonomous systems. Key contributions include neuro-symbolic frameworks for decision-making, real-time multi-physics field reconstruction, and cross-cultural studies of pedestrian interactions. He collaborates internationally on urban mobility resilience and hydrogen refueling infrastructure. Research Highlights : Development of knowledge graph-based prediction architectures Experimental validation of human-vehicle interaction in VR environments Creation of the SCOUT trajectory prediction framework