Find Academic Supervisors

Andreas HegyiView profile

Assistant Professor

• Traffic Flow Modelling
• Traffic Control Systems
• Connected and Automated Vehicles
• +4 more

Dr. Andreas Hegyi is an Assistant Professor at TU Delft's Department of Transport & Planning, specializing in traffic flow modeling, control systems, and connected/automated vehicles. He holds a PhD from TU Delft (2004) and was a postdoctoral researcher at Ghent University. His research focuses on improving traffic efficiency through speed control and managing mixed environments of automated and manual vehicles. He teaches courses like Road Traffic Management and advises MSc/PhD students. He has authored over 100 publications and serves as an editor for IEEE Transactions on Intelligent Transportation Systems and Transportation Research Part C. His recent work explores cyclist priority systems using connected vehicles and reinforcement learning in traffic control. Education: PhD in Traffic Engineering (TU Delft, 2004), Postdoc at Ghent University. Research Interests: Traffic flow modeling, cooperative vehicle-infrastructure systems, data-driven traffic analysis. Awards & Roles: Program Chair of IEEE-ITSC 2013, General Chair of TRISTAN 2016, Editorial Board memberships.

• Traffic Systems Engineering
• Transportation Modeling
• Intelligent Transportation Systems
• +3 more

Dr. V.L. Knoop is a Professor in Traffic Systems Engineering at Delft University of Technology's Civil Engineering & Geosciences school. His research focuses on advanced transportation modeling, autonomous vehicle systems, and data-driven mobility solutions. He has contributed over 250 publications and supervised multiple research projects. Notable awards include the Greenshields Prize (2012, 2015, 2016) and D. Grant Mickle Award (2016). Knoop has led editorial roles for Collective Dynamics and co-edited conference proceedings on Traffic and Granular Flow. His work bridges theoretical models with practical applications, addressing challenges in traffic efficiency, safety, and sustainable infrastructure. Recent media engagements highlight his expertise in traffic management solutions and autonomous vehicle technologies. Education: PhD in Civil Engineering (assumed) Research Interests: Development of macroscopic traffic flow models Integration of autonomous systems into existing transport networks Data analytics for traffic prediction and optimization Key Contributions: Large-scale car-following dataset analysis (Lyft Level-5 collaboration) Ramp metering optimization studies using empirical trajectory data Publications on adaptive cruise control and uncertainty-based decision-making in autonomous vehicles Labs/Teams: Traffic Systems Group at TU Delft Collaborations with Rijkswaterstaat (Dutch Ministry of Infrastructure) and Flitsmeister

Xiaoyu XiaView profile

Lecturer

• System Privacy and Security
• AI Privacy
• Parallel and Distributed Computing
• +4 more

Dr. Xiaoyu Xia is a Lecturer (equivalent to Assistant Professor in North America) in Cybersecurity & Software Systems at RMIT University's School of Computing Technologies. He received his PhD with the prestigious Alfred Deakin Medal from Deakin University, Australia, and has established himself as a leading researcher in distributed systems and cybersecurity with over 50 peer-reviewed publications in top-tier venues including IEEE S&P, ACM WWW, and IEEE Transactions. Dr. Xia's research spans critical areas at the intersection of computing and security: System Privacy and Security Distributed Systems and Edge Computing AI Privacy and Machine Learning Systems Sustainable Computing Cybersecurity and Privacy-Preserving Technologies His recent work demonstrates a clear trajectory toward developing practical privacy-preserving frameworks for emerging technologies, particularly in edge computing environments and large language models. Dr. Xia has made significant contributions to machine unlearning, secure data management in distributed systems, and energy-efficient edge computing solutions that balance performance with sustainability concerns. Dr. Xia has received notable recognition for his scholarly impact: World's Top 2% Scientists by Stanford University (2022-2024) Alfred Deakin Medal for PhD research excellence (2021) Teaching Excellence Award from Swinburne University of Technology (2021) As an active researcher, Dr. Xia currently leads multiple funded projects including an ARC Discovery Project grant worth over $500,000 for developing privacy-aware intelligent digital twins for secure critical infrastructures. He is open to supervising motivated PhD students with interests in system security and privacy, and distributed ML systems. Dr. Xia serves the academic community through editorial roles as Associate Editor for IEEE Transactions on Dependable and Secure Computing and as a Review Board Member for IEEE Transactions on Parallel and Distributed Systems, and regularly participates in program committees for major conferences including ACM WWW and IEEE ICDCS.

• Cybersecurity
• Healthcare IT Security
• Machine Learning
• +4 more

Carlos Cilleruelo Rodríguez is an Associate Professor in the Department of Computer Science at Universidad de Alcalá. He is affiliated with the TIFyC research group ("Information Technologies for Training and Knowledge") and specializes in cybersecurity, healthcare IT security, and authentication systems. His research focuses on dark web analysis, malware detection, and adaptive security frameworks. He earned his Doctorate from Universidad de Alcalá in 2023 with the thesis "A cybersecurity review of healthcare industry 2023" , supervised by Dr. José Javier Martínez Herráiz and Dr. Luis de Marcos Ortega. His work spans ransomware mitigation in manufacturing, continuous authentication architectures (JBCA), and privacy issues in IoT healthcare devices. Recent projects include the ProTego initiative for mobile authentication in eHealth environments. Publications highlight trends in machine learning for behavioral authentication, darknet interconnections, and security in Bring-Your-Own-Device (BYOD) healthcare settings. No scientific awards explicitly mentioned. Labs/teams: Active contributor to the TIFyC group, focusing on cybersecurity education and applied research in critical infrastructure protection.

• Computer Vision
• Machine Learning
• Video Analysis
• +5 more

Prof. Laura Leal-Taixé is an Associate Professor at the Technical University of Munich (TUM) leading the Dynamic Vision and Learning group. She holds the Rudolf Mößbauer Tenure Track Chair, promoted from a 2017 Tenure Track Assistant Professorship. Her work focuses on advancing computer vision and machine learning, particularly in video analysis, multi-object tracking, and autonomous systems. She received a Sofja Kovalevskaja Award (2017) for her project socialMaps, which integrates dynamic social data into traffic modeling. Education: B.Sc./M.Sc. in Telecommunications Engineering, Technical University of Catalonia (UPC), Barcelona Ph.D. in Information Processing, Leibniz University Hannover (2014) Postdoc at ETH Zurich (2014–2016), and Senior Researcher at TUM’s Computer Vision Group (2016–2019) Research Interests: Multi-object tracking and segmentation in videos Motion analysis and semantic segmentation for autonomous driving Deep learning for video understanding Social dynamics modeling in urban environments Awards & Grants: €1.65M Sofja Kovalevskaja Award (Humboldt Foundation, 2017) DAAD Australia-German Joint Research Scheme (2017) Multiple travel grants from CVPR and Women in Computer Vision Labs & Collaborations: Dynamic Vision and Learning Group at TUM Collaborations with ETH Zurich, Northeastern University, and NVIDIA

Pau Batlle FranchView profile

Research Fellow

• Statistics
• Applied Mathematics
• Uncertainty Quantification
• +4 more

Pau Batlle Franch is a Research Fellow in the Computing and Mathematical Sciences Department at California Institute of Technology (Caltech), working with Professor Houman Owhadi. He holds a PhD from Caltech (June 2025) and was a research affiliate at NASA Jet Propulsion Laboratory (JPL). His research focuses on the intersection of statistics and applied mathematics, including frequentist confidence intervals in inverse problems, game-theoretical uncertainty quantification, and Gaussian processes. He has applied his work to domains like remote sensing, biology, earthquake prediction, and telecommunications engineering. Education : PhD in Computing and Mathematical Sciences (Caltech, 2025); Double undergraduate degree in Mathematics and Engineering Physics from Universitat Politècnica de Catalunya (CFIS program); Research visitor at NYU's Center for Data Science. His research interests include optimization-based statistical methods, Gaussian process frameworks for scientific computing, and uncertainty quantification in physical systems. Notable contributions include resolving the Burrus conjecture and developing computational hypergraph discovery techniques applied to NASA JPL projects. His work bridges theory and application, addressing challenges in ill-posed inverse problems and robust statistical inference. Recent activities include presenting at SIAM conferences and workshops on inverse problems in Earth science. His Gaussian process methods have been published in journals like PNAS and SIMODS, with applications ranging from PDE solving to RNA classification. Collaborations include JPL and the Groningen seismic study. Grants & Collaborations : Ongoing work with NASA JPL on lunar rover control and computational graph discovery; Seismic modeling in the Groningen gas field with epistemic/aleatoric uncertainty frameworks. He maintains an active GitHub profile showcasing projects in machine learning and scientific computing, including repositories like DarwinProjectAnalytics and emb4class .

• Resilience in 5G
• Distributed Artificial Intelligence (Edge AI)
• Information assessment
• +3 more

Tobias Meuser is a Researcher at the Multimedia Communications Lab of Technische Universität Darmstadt, leading the "Adaptive Communication Systems" group since 2020. He holds a PhD (2019) focused on vehicular network data management and has been a central figure in the third phase of the Collaborative Research Center (CRC) MAKI as a principal investigator in subproject B1. His work emphasizes resilient 5G networks, edge AI, and distributed systems. Education: B.Sc. Business Informatics (Fernuniversität Hagen) M.Sc. Informatics (TU Darmstadt) Research Interests: Resilience in 5G and beyond Edge AI and distributed machine learning Information assessment in vehicular networks Collaborative perception systems Hardware acceleration for network functions Key Projects: Principal Investigator in CRC MAKI's B1 (Monitoring and Analysis) Collaborations with Opel (cooperative maneuvering) and Deutsche Bahn (5G resilience) Labs/Teams: Head of Adaptive Communication Systems group at Multimedia Communications Lab Member of Distributed Sensing Systems group (2016–2020)

• Next Generation Internet
• Human-centric Networking
• Ontology and Knowledge-based Management
• +13 more

David Palma is an Associate Professor at the Department of Information Security and Communication Technology , Faculty of Information Technology and Electrical Engineering , Norwegian University of Science and Technology (NTNU). His research focuses on next-generation networking paradigms, including intent-based networking , knowledge-driven management , and IoT device automation . Key research interests include: Human-centric Internet of Things (IoT) Ontology-based network management Cloud/Edge/Fog computing for IoT Arctic and satellite communication 5G integration in smart grids His recent publications (2024-2019) highlight trends in knowledge graphs for network compliance, XR applications in critical sectors, UAV-based emergency networks , and 5G-enabled smart grid protection . Articles also explore Arctic connectivity via satellite swarms and energy-efficient IoT management.

Gaurav ChoudharyView profile

Associate Professor

• Cybersecurity
• Machine Learning
• Internet of Things
• +8 more

Gaurav Choudhary is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the College of Engineering and the Cybersecurity Engineering department. His research focuses on emerging threats in digital systems, with a particular emphasis on Malware Detection, Internet of Things (IoT) security, and Machine Learning applications in cybersecurity. His recent work explores intrusion detection in IoT ecosystems, drone identification using RF signals, and the analysis of virtual private networks and proxies. These studies span subfields like Tiny Machine Learning, Scalable Deployment, and Tor Browser forensics, reflecting his interest in both theoretical and applied security challenges. The majority of his publications from 2025 revolve around enhancing cybersecurity frameworks through innovative machine learning techniques, user-centric authentication methods, and forensic investigation tools. Despite his active research contributions, no specific scientific awards or students he has advised are mentioned in the provided data.

Lukas IffländerView profile

Professor

• Cybersecurity
• Cryptography
• Network Security
• +4 more

Prof. Dr. Lukas Iffländer serves as a Professor within the Faculty of Informatics / Mathematics at Westsächsische Hochschule Zwickau, Germany, maintaining an active office (Room U 452) with contactable phone number +49 351 462 3516. His academic profile demonstrates continuous engagement through recent publications extending into 2025, with research appointments scheduled by prior arrangement reflecting his operational availability. His research program centers on cybersecurity with exceptional depth in cryptographic systems and infrastructure protection. Key specialties include homomorphic encryption optimization (notably CKKS scheme implementations), IoT security protocols for resource-constrained environments, and railway system vulnerability analysis. His methodology consistently integrates performance benchmarking with security validation, particularly examining computational overhead in privacy-preserving technologies and physical attack vectors against critical transportation infrastructure. This dual focus on theoretical cryptography and real-world system security establishes him as a bridge between academic research and industrial implementation challenges. Analysis of his 15 most recent publications (2023-2025) reveals three dominant research trajectories: (1) Cryptographic performance evaluation, where he pioneers benchmarking frameworks for homomorphic and attribute-based encryption in practical scenarios like linear regression; (2) Railway security innovation, addressing digital interlocking systems and physical attack mitigation through technology forecasting; (3) IoT security optimization, developing multi-objective recommendation systems for group communication protocols. His work consistently emphasizes measurable performance impacts, with 60% of recent publications containing empirical benchmarking data, reflecting an engineering-driven approach to security research. No scientific awards were documented in the source materials. Information regarding student supervision, research grants, or laboratory affiliations remains unavailable in the provided documentation, though his publication volume suggests active research group leadership. His technical focus on virtual machine introspection and hypercall handling indicates potential involvement in low-level systems security teams, while railway security publications imply collaboration with transportation infrastructure entities.

• Adversarial Machine Learning
• Cybersecurity
• Data Mining
• +5 more

Bowei Xi is an Associate Professor at the Department of Statistics at Purdue University. He holds a Ph.D. in Statistics from the University of Michigan (2004) and has made significant contributions to adversarial machine learning, cybersecurity, and metabolomics. Education: Ph.D. in Statistics, University of Michigan (2004) His research focuses on: Adversarial Machine Learning – Developing defenses against attacks on AI systems Cybersecurity – Integrating statistical methods with network security Metabolomics – Applying statistical analysis to biological data Big Data – Creating scalable analytical frameworks Differential Privacy – Balancing data utility with privacy preservation Recent publications highlight: Advancements in causal inference under privacy constraints Novel cyber deception strategies for battlefield IoT Applications of graph neural networks with dropout techniques Statistical defense mechanisms against adversarial examples Awards include: Faculty of 1000 Evaluation (2011) US Patents #7272707 and #7490234 for application server optimization He teaches STAT 514: Design of Experiments with a focus on hands-on learning and statistical software (SAS) integration. His office hours and contact details are publicly listed for student engagement.

Tor-Morten GrønliView profile

Professor

• Internet of Things (IoT)
• Software Architecture
• Machine Learning
• +4 more

Tor-Morten Grønli serves as Professor at the Department of Technology, School of Economics, Innovation and Technology, Kristiania University College (Norway). He is also a Visiting Research Scholar at Copenhagen Business School's Department of Information Technology Management and an affiliate of the Center of Business Data Analytics (cbsDBA). Education PhD in Computer Science from Brunel University, London (2011) Master of Technology (with distinction) from Brunel University, London (2007) Research Focus Grønli leads research in context-aware systems, mobile/pervasive computing, and Internet of Things (IoT). He founded/directs the Mobile Technology Lab at Kristiania and has co-authored 70+ publications. Core expertise includes: IoT architecture and applications Machine learning for transport systems Mobile computing frameworks Blockchain-security integration Edge-cloud computing paradigms Publication Trends Recent works (2023-2025) demonstrate strong focus on converging IoT, blockchain, and AI technologies, particularly for intelligent transport and healthcare systems. Dominant themes include federated learning implementations, privacy-preserving architectures, and sustainable edge computing solutions, with increasing emphasis on real-world applications in medical diagnostics and public infrastructure. Professional Activities Founder/Director of Mobile Technology Lab General Chair: Norwegian Conference on ICT Co-organizer: International Conference on Mobile Web Editorial Board: International Journal of Pervasive Computing, Journal of Online Information Review, Computers & Electrical Engineering TPC Member for IEEE BigData, Percom, HICSS, COMPSAC Guest Editor for special issues in Future Generation Computer Systems

• Information and Communication Technology
• Electrical and Electronic Engineering
• Artificial Intelligence
• +3 more

Paweł Tarnowski is a researcher at the Institute of the Theory of Electrical Engineering, Measurement and Information Systems , Faculty of Electrical Engineering, Warsaw University of Technology. His work focuses on biomedical signal processing, emotion recognition, and machine learning applications in affective computing and human-computer interaction. Research areas include Information and Communication Technology (ICT) Electrical and Electronic Engineering Artificial Intelligence Neuroscience Human Factors His recent publications highlight trends in emotion recognition using multimodal physiological signals (EEG, EOG, GSR), driver fatigue detection, and deep learning techniques like 1D-CNN for medical diagnostics. Articles also address urban traffic monitoring and neuromarketing research via EEG analysis. At Warsaw University of Technology, he supervises thesis projects and contributes to interdisciplinary research in biomedical engineering and signal processing. Labs/Teams: Institute of the Theory of Electrical Engineering, Measurement and Information Systems .

Tamás TettamantiView profile

Professor

• Road Traffic Control
• Intelligent Transportation Systems
• Traffic Simulation
• +3 more

Tamás Tettamanti is a Professor at Budapest University of Technology and Economics, serving as Deputy Head of the Department of Control for Transportation and Vehicle Systems. He holds a PhD (2013), Habilitation (2023), and DSc (2023) in Transportation Engineering, with expertise in road traffic modeling and control. 2007–2010: PhD Student 2010–2013: Assistant Lecturer 2014–2018: Senior Lecturer 2019–2024: Associate Professor 2025–present: Full Professor Education: High School Graduation (2001), DEUG (2004), MSc in Transportation Engineering (2007), Jazz Trumpet Graduate (2008) PhD (2013), Habilitation (2023), DSc (2023) Research focuses on road traffic modeling, intelligent transportation systems (ITS), autonomous vehicle integration, and emission-aware traffic control. His work bridges theoretical developments with real-world applications, including wireless traffic light systems and deep learning for urban mobility peaks. Scientific Awards: BME PhD Research Prize (2012) Literary Awards from Hungarian Scientific Association for Transport (2013, 2017, 2021, 2023) Bolyai János Research Scholarship (2017–2020) Michelberger Master Prize (2022) BME Jubilee Medal (2023) He has led major projects such as Dynamic Adaptive Traffic Control Services (2020–2024) and Deep Learning Anticipated Urban Mobility Peaks (DARUMA) (2021–2024), with industry collaborations including Google-BME Traffic Lab.

Balázs VargaView profile

Research Fellow

• Road Traffic Modeling and Control
• AI-based Traffic Flow Prediction
• Urban Traffic Simulation
• +3 more

Dr. Balázs Varga is a Research Fellow at the Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics (BME). He holds a PhD in Transportation and Vehicle Sciences (2021) and an MSc in Vehicle Engineering (2015) from BME. His industry experience includes roles at AVL Hungary as a Software and Function Developer (2016–2018) and academic positions at Chalmers University of Technology (2015) and SZTAKI (2012–2014). Current Role: Research Fellow (2021–present) Teaching: Programming, Control Theory, Traffic Modeling (English language course) Research Interests: Varga specializes in road traffic modeling and control, focusing on AI-based traffic estimation and dynamic traffic management. His work integrates machine learning with mesoscopic and microscopic traffic simulation tools like SUMO to optimize urban mobility and reduce emissions. Projects: He leads the 2020–2024 national development project 'Dynamic, adaptive traffic control services and evaluation tools based on digitally connected data sources' (2019-1.1.1-PIACI KFI). This initiative leverages connected data sources for real-time traffic control and policy evaluation. Key Publications Trends: His recent articles explore topics such as graph neural networks for sensor placement, multiobjective control of emissions, and mixed-reality V2X testing. These works emphasize data-driven approaches, emission reduction, and simulation frameworks for autonomous vehicles.