Find Academic Supervisors

Lionel C. BriandView profile

Professor

• Software Engineering
• Software Verification and Validation
• Trustworthy AI
• +6 more

Lionel C. Briand is a Professor of Software Engineering with shared appointments at the University of Luxembourg's SnT Centre for Security, Reliability, and Trust and the School of Electrical Engineering and Computer Science at the University of Ottawa. He holds a Canada Research Chair (Tier 1) in Intelligent Software Dependability and Compliance and serves as Director of Lero, Ireland's national software research center. His academic leadership spans over 25 years of collaborative research with industry partners across automotive, aerospace, energy, financial, and legal domains. Professor Briand's research focuses on software verification and validation, trustworthy AI systems, model-driven engineering, and empirical software engineering methodologies. His work bridges theoretical foundations with industrial applications, particularly in cyber-physical systems where machine learning components interact with safety-critical control systems. He has pioneered techniques for testing AI-enabled systems, GDPR compliance automation, and mutation analysis for space systems. His publication portfolio demonstrates consistent innovation in software testing, with recent emphasis on large language models for test generation, automated compliance checking, and safety analysis of deep neural networks. Key trends include black-box testing methodologies, metamorphic testing for security, and search-based approaches for DNN validation. IEEE Fellow and ACM Fellow IEEE Computer Society Harlan Mills Award (2012) ACM SIGSOFT Outstanding Research Award (2022) IEEE Reliability Society Engineer-of-the-Year Award (2013) ERC Advanced Grant recipient (2016) Fellow of the Academy of Science, Royal Society of Canada (2023) ICSE 2011 Most Influential Paper Award As Director of Lero and holder of a Canada Research Chair, Professor Briand leads major research initiatives including an ERC Advanced Grant on cyber-physical system modeling and testing. His industrial collaborations generate substantial grant funding, particularly in automotive safety validation and regulatory compliance automation. He mentors numerous researchers through his dual appointments and serves on program committees for top software engineering conferences. Professor Briand directs research activities at the SnT Centre's SVV department, focusing on software verification and validation. His team develops practical tools like MASS for space system mutation analysis and COREQQA for compliance requirements understanding, with strong industry adoption in automotive and aerospace sectors.

Jakob BeetzView profile

Professor

• Building Information Modeling (BIM)
• Digital Twins
• Artificial Intelligence in Construction
• +3 more

Prof. Dr. Jakob Beetz serves as a University Professor at RWTH Aachen University's Faculty of Architecture, leading the Design Computation (DC) research group. His work addresses critical challenges in sustainable built environments through digital innovation, focusing on integrating knowledge, information, and data across disciplines to reduce the sector's energy and material consumption—which accounts for over one-third of global totals—while advancing climate goals under the European Green Deal. His research spans Building Information Modeling (BIM), digital twins, and artificial intelligence, with emphasis on graph-based data federation, semantic web technologies, and large language models in construction. Key interests include evidence-based planning, parametric design optimization, building physics simulation, and networked knowledge modeling. Recent projects explore federated digital twin ecosystems for infrastructure management, intelligent damage assessment systems, and AI-driven solutions for wood structure preservation, directly contributing to sustainable development targets. Analysis of his 2024-2025 publications reveals a cohesive trajectory toward decentralized data environments and AI integration in Architecture, Engineering, and Construction (AEC). His work bridges theoretical foundations in knowledge representation with practical applications in bridge maintenance, road infrastructure, and timber construction, demonstrating consistent innovation in spatial data querying, federated issue management, and ontology-based process modeling. Prof. Beetz actively supervises PhD candidates, as evidenced by DC.Promotions 2024, and drives international collaboration through events like the Forum Construction Informatics 2025 and CIB W78 conferences. His research group engages with industry standards including Industry Foundation Classes (IFC) and Common Data Environments (CDEs), emphasizing open data principles and interoperability to transform construction workflows.

Marc ReichenbachView profile

Professor

• Integrated Circuits
• Embedded Systems
• VLSI Design
• +7 more

Prof. Dr.-Ing. Marc Reichenbach serves as the Chair of Integrated Systems at the Institute for Applied Microelectronics and Data Technology at the University of Rostock. His office is located at Albert-Einstein-Straße 26, 18059 Rostock, Room 102 (1st floor), with contact information including telephone (0381) 498 7270 and email marc.reichenbach@uni-rostock.de. Professor Reichenbach's research focuses on the intersection of hardware design and artificial intelligence, with particular expertise in memory technologies and computing architectures. His work spans several key areas: Development of specialized computer architectures for deep learning applications Advanced VLSI design and CPU architecture Emerging memory technologies, particularly RRAM (Resistive Random-Access Memory) FPGA-based acceleration systems Hardware implementations for neural networks and AI applications Analysis of Professor Reichenbach's recent publications (2023-2025) reveals a strong focus on memory computing technologies, particularly RRAM-based systems. His work demonstrates expertise across multiple dimensions of computer architecture including ASIC design, FPGA acceleration, and novel memory systems. The publications show a clear trajectory toward implementing AI and machine learning capabilities directly in hardware, with applications ranging from edge computing to satellite systems. A significant portion of his recent work addresses the challenges of implementing neural networks using emerging memory technologies, focusing on efficiency, reliability, and performance optimization. Professor Reichenbach teaches several advanced courses including: Computer architectures for deep learning applications Project seminar Embedded Systems Advanced VLSI Design (Advanced CPU Design) His research group appears to be actively engaged in several cutting-edge projects related to hardware acceleration for AI applications, memory computing, and embedded systems design. The group collaborates on projects involving digital twins for hardware systems, real-time operating systems for heterogeneous architectures, and specialized computing systems for various applications from medical devices to drone technology.

Andreas MaierView profile

Researcher

• Drug Repurposing
• Network Medicine
• Bioinformatics
• +3 more

Andreas Maier is a Researcher at the University of Hamburg's Faculty of Mathematics, Informatics and Natural Sciences, affiliated with the Computational Systems Biology department. He began his PhD in May 2021 with Cosy.Bio (Center for Systems Biology) at UHH, focusing on drug repurposing projects such as REPO-TRIAL. Previously, he completed a Bioinformatics master's thesis at TUM (Technical University of Munich), developing a web application for analyzing molecular disease networks. His research interests emphasize network medicine, drug repurposing, and computational tools for biomedical discovery. He has contributed to platforms like NeDRex-Web, Drugst.One, and BioCypher, which democratize access to systems medicine workflows. His work bridges heterogeneous data integration, federated learning for rare diseases, and quantum computing applications in genetics. Maier's publications highlight innovations in knowledge graph-based drug discovery, privacy-preserving federated learning, and single-cell network analysis. He actively develops open-source bioinformatics tools to address challenges in disease module identification and patient stratification. His projects align with the REPO4EU consortium and other collaborative initiatives in translational bioinformatics.

Jann WeinandView profile

Research Professor

• Energy System Optimization
• Renewable Energy Resource Assessment
• Complexity Management
• +4 more

Dr. Jann Michael Weinand is the head of the Integrated Scenarios department at the Institute of Climate and Energy Systems (ICE-2) within Forschungszentrum Jülich GmbH. He leads a team of 30 scientists, PhD students, and master students focusing on energy system analysis, complexity management, and AI integration. His work addresses regional and international energy systems, emphasizing renewable energy resource assessment and techno-economic feasibility. Dr. Weinand holds a Dr.-Ing. from the Karlsruhe Institute of Technology (2020) and a Mechanical Engineering and Business Administration degree from RWTH Aachen University (2016). His research spans energy autonomy, renewable resource optimization, and the socio-technical challenges of energy transitions. Key research areas include energy system modeling, geothermal and wind energy potential, and data-driven methodologies. He coordinates interdisciplinary projects with academic and industrial partners, contributing to high-impact journals like Nature Energy and Joule. His team develops open-source tools (e.g., ETHOS workflows) for reproducible energy assessments and advocates for spatially disaggregated energy planning. Publications highlight trade-offs in energy system design, AI risks, and land-use conflicts for renewables. He emphasizes integrating social, technical, and environmental factors into energy policy frameworks.

• Explainable AI
• Human-AI Interaction
• Machine Learning
• +7 more

Andreas Holzinger is a Professor at Graz University of Technology, with additional affiliations at Medical University Graz and University of Natural Resources and Life Sciences Vienna in Austria. He is recognized as an IFIP Fellow (2021) for his significant contributions to information processing and computer science. His work spans multiple institutions across Europe, with notable collaborations extending to the University of Alberta in Canada. Professor Holzinger's research focuses on Human-Centered AI, Explainable AI (XAI), and their practical applications across diverse domains. His work bridges theoretical AI advancements with real-world implementations in healthcare, forestry, and human-robot interaction. He has pioneered approaches in counterfactual explanations, graph neural networks, and human-in-the-loop systems that emphasize transparency and trustworthiness in AI decision-making processes. His recent publications demonstrate a strong trend toward integrating large language models with traditional AI systems while maintaining explainability. Holzinger's work consistently emphasizes the human element in AI systems, ensuring that technological advancements serve human needs rather than obscuring decision processes. His research in medical AI, smart forestry, and agricultural applications shows a commitment to solving practical problems with human-centered technological solutions. Scientific Awards: IFIP Fellow (2021) Professor Holzinger has been instrumental in establishing design guidelines for explainable AI systems, particularly through his work on post-hoc versus ante-hoc explanations. His research on Kandinsky Patterns has provided valuable experimental frameworks for pattern analysis and machine intelligence. He has secured significant research funding for projects bridging AI with practical applications in healthcare and environmental monitoring. His leadership extends to the organization of major conferences and workshops, including the CD-MAKE conference series, where he has fostered interdisciplinary collaboration between AI researchers and domain experts. His work on the CLARUS platform demonstrates practical implementations of interactive explainable AI for medical applications.

Philip WadlerView profile

Professor

• Theoretical Computer Science
• Functional Programming
• Programming Language Foundations
• +6 more

Philip Wadler is Professor of Theoretical Computer Science at the University of Edinburgh and Senior Research Fellow at IOHK. He is an ACM Fellow, Fellow of the Royal Society, and Fellow of the Royal Society of Edinburgh. His work spans programming language design, type systems, and formal verification, with significant contributions to Haskell, Java, and XQuery. He has held leadership roles in ACM SIGPLAN and served on editorial boards for major journals. Research Interests: Wadler's research focuses on the foundations of programming languages , including Gradual and session typing Language-integrated query Functional and logic programming XML data models Parametricity and free theorems Verification of smart contracts Publication Trends: Recent articles emphasize type safety, formal verification, and blockchain applications. Key themes include gradual typing (blame calculus), session types for concurrency, and logical foundations of programming. His 2015–2025 papers show sustained focus on type theory and language design . Awards & Recognition: POPL Most Influential Paper (2003 for 1993 work) SIGPLAN Distinguished Service Award Best Paper SBMF 2018 Royal Society-Wolfson Fellowship (2004–2009) ACM Fellow (2007) Fellow of Royal Society of Edinburgh (2005) Advising & Grants: He has supervised numerous PhD students in programs like the Centre for Doctoral Training in Pervasive Parallelism. His EPSRC Programme Grant "From Data Types to Session Types" (2013–2020) funded major advances in concurrency theory. Current work with IOHK explores blockchain verification using Haskell-based Plutus.

• Telecommunication Networks
• Distributed Machine Learning
• Edge Computing
• +5 more

Falko Dressler is a Full Professor and Chair for Telecommunication Networks at the School of Electrical Engineering and Computer Science, Technische Universität Berlin. He holds a Ph.D. and M.Sc. in Computer Science from Friedrich-Alexander University of Erlangen-Nuremberg (1998-2003). His research focuses on next-generation wireless systems , distributed machine learning , edge computing , and applications in Internet of Things (IoT) , cyber-physical systems , and internet of bio-nano-things . Editorial roles: IEEE Trans. on Mobile Computing, Elsevier Computer Communications, IEEE/ACM Trans. on Networking Conference leadership: IEEE INFOCOM, ACM MobiSys, IEEE VNC Textbooks: Self-Organization in Sensor and Actor Networks (Wiley), Vehicular Networking (Cambridge) Recent publications highlight trends in Edge Computing Resilience and 6G Network Architecture , with a strong emphasis on Molecular Communication , Terahertz Band Synchronization , and Federated Learning in vehicular environments. Scientific contributions include multiple IEEE Fellow , ACM Fellow , and VDE ITG Prize 2023 recognitions. Advisory and professional activities include membership in the German National Academy of Science and Engineering (acatech), IEEE COMSOC Conference Council, and ACM SIGMOBILE Executive Committee. His work spans cooperative driving, ultra-low power sensor networks, and security in nano-communication systems.

Tobias NeckelView profile

Associate Professor

• Uncertainty Quantification
• Random Differential Equations
• Numerical Algorithms
• +4 more

Tobias Neckel is an Associate Professor at the Institute for Informatics at the Technical University of Munich (TUM), where he leads research projects and coordinates academic programs. He has been the project team leader of the IGGSE Project ExaNIML since 2018, main coordinator of the Ferienakademie since 2014, and Program Coordinator of the Bavarian Graduate School of Computational Engineering (BGCE) since 2009. Diploma in Technomathematik from TU München (2005) Dr. rer. nat. in Informatics from TU München (2009) Neckel's research focuses on Uncertainty Quantification, Random Differential Equations, and High Performance Computing. His work develops efficient numerical algorithms using hierarchic and adaptive methods such as octrees/spacetrees and sparse grids, with applications in fluid-structure interactions and incompressible fluid flow simulation. His research bridges theoretical mathematics with practical computational science, emphasizing robust and efficient implementations. His recent publications demonstrate a strong trajectory in multi-fidelity modeling, uncertainty quantification, and high-performance computing. Neckel has made significant contributions to scalable hierarchical approximation methods, dynamic resource management in HPC, and the application of machine learning techniques to computational science problems. His work spans diverse application domains including plasma physics, hydrology, and computational engineering. Lehrfonds prize of the TUM (2014) Ernst Otto Fischer prize of the TUM (2011) Promotionspreis des Bunds der Freunde der TU München (2009) Neckel has supervised numerous graduate students and has been actively involved in curriculum development and teaching innovation. His book "Bits and Bugs: A Scientific and Historical Review of Software Failures in Computational Science" (2019) represents a significant contribution to understanding software reliability in scientific computing. He has organized minisymposia at major conferences including SIAM CSE and SIAM UQ, and serves on program committees for various computational science conferences. As coordinator of the Ferienakademie and the BGCE, Neckel plays a central role in advanced computational engineering education in Bavaria. His research group develops software for exascale computing and contributes to the Transregional Collaborative Research Centre 89 on Invasive Computing. Neckel also maintains international collaborations, with research stays at institutions including the Australian National University and Tokyo Institute of Technology.

• Traffic Data Acquisition
• Traffic Data Analysis
• Video-Based Microscopic Traffic Flow Simulation
• +2 more

Dr. Adrian Fazekas is a Lecturer at the Institute of Highway Engineering, RWTH Aachen University, and collaborates with the Federal Highway Research Institute (BASt). He holds a Dr.-Ing. in Computer Science from RWTH Aachen (2005–2011), specializing in Media Engineering. His professional trajectory includes roles as a Research Assistant at RWTH Aachen and industry experience as a Software Developer at Continental AG. Research interests focus on traffic data acquisition , microscopic traffic flow simulation , and intelligent transportation systems . Key projects include: DROVA: Drone-based traffic analysis for infrastructure optimization ESIMAS: Real-time tunnel safety management Digital Twin Road: Physical-informational mapping of future highways AUTUKAR: Automated tunnel monitoring systems His publications emphasize real-time traffic detection , safety analytics , and data-driven modeling , with recent work exploring thermal-camera nudging systems and weigh-in-motion accuracy. He actively contributes to the Research Association for Roads, Earth and Tunneling (SETAC). No awards or student advising roles are documented.

Katharina EggenspergerView profile

Researcher

• Automated Machine Learning (AutoML)
• Hyperparameter Optimization (HPO)
• Tabular Machine Learning
• +1 more

Katharina Eggensperger is an Early Career Research Group Leader at the University of Tübingen , leading the AutoML for Science group within the Cluster of Excellence Machine Learning for Science . She previously completed her Ph.D. at the University of Freiburg under Frank Hutter and Marius Lindauer (2022), and actively contributes to the AutoML community through open-source tool development and competition leadership. Co-developer of AutoML.org tools Faculty member of IMPRS-IS Chair for multiple AutoML workshops/conferences (2019-2025) Her research focuses on automated machine learning (AutoML) with specific attention to: AutoML Systems Hyperparameter Optimization Tabular Machine Learning Scientific Applications of ML She has organized multiple AutoML schools and conferences, including serving as Program Chair for AutoML 2024 and Non-archival Track Chair for AutoML 2025. Her work emphasizes making machine learning accessible through automation while maintaining scientific rigor and interpretability, particularly for tabular data applications. Katharina actively recruits PhD students through IMPRS-IS and collaborates with institutions like the University of Freiburg and Cyber Valley .

• Database Systems
• Distributed Computing
• Cloud Computing
• +2 more

Bettina Kemme is a faculty member at McGill University in Montreal, Canada. Her research focuses on database systems , distributed computing , and cloud data management . She has made significant contributions to database replication, consistency models, and middleware frameworks for scalable applications. Research Themes : Database replication, distributed systems, cloud computing, and software engineering. Notable Collaborations : Jörg Kienzle, Joseph Vinish D'silva, Yunjia Zheng, and Marta Patiño-Martínez. Publications span critical areas such as graph database view management, transactional recovery in key-value stores, and latency-aware publish/subscribe systems. Her work is published in venues like VLDB , ICDE , Middleware , and SRDS .

• 5G/6G Networking
• Network Slicing
• Intent-Based Networking
• +5 more

Adlen Ksentini is a Professor at EURECOM, a leading graduate school and research center in Sophia Antipolis, France, specializing in digital science and communication systems. His extensive research focuses on next-generation mobile networks (5G/6G), network management, and the integration of artificial intelligence with telecommunications infrastructure. Dr. Ksentini actively contributes to major EU research initiatives including 6G-BRICKS and AC3, serving as a key researcher and project leader in the development of future network architectures. Dr. Ksentini's research interests center around network slicing, intent-based networking, edge computing, and the application of machine learning to network management problems. His work bridges theoretical advancements with practical implementations in 5G/6G systems, with particular emphasis on zero-touch network management, energy efficiency optimization, quality of service assurance, and the integration of large language models with network operations. His research has significantly contributed to the development of O-RAN (Open Radio Access Network) frameworks and the evolution of network automation. His recent publication trends reveal a strategic shift toward AI-native network architectures, with increasing focus on integrating large language models (LLMs) with network management systems. His work demonstrates a clear progression from traditional network management approaches to more autonomous, AI-powered systems capable of intent-based configuration, self-optimization, and predictive maintenance. The publications show strong emphasis on practical implementations within the 6G research ecosystem, addressing critical challenges in network slicing, resource allocation, and energy efficiency. As a research supervisor, Dr. Ksentini mentors several PhD students including Abdelkader Mekrache, Karim Boutiba, Bouziane Brik, and Houda Hafi, who frequently appear as co-authors on his publications. His research is primarily funded through major EU research projects such as 6G-BRICKS (Building Reusable Testbed Infrastructures for Cloud-to-Device Breakthrough Technologies) and AC3 (which focuses on Cloud Edge Continuum). Dr. Ksentini is actively involved with the 6G-BRICKS project consortium and the AC3 project team, where he contributes to developing next-generation network architectures that integrate communication, computing, and sensing capabilities. His work within these projects focuses on creating reusable testbed infrastructures and addressing security and trust management challenges in the cloud-edge continuum.

Stephan RaveView profile

Researcher

• Numerical Analysis
• Scientific Computing
• Machine Learning
• +4 more

Dr. Stephan Rave is a Researcher in the Institute for Analysis and Numerics at the University of Münster. He is affiliated with the Applied Mathematics Münster cluster and serves as an Investigator in Mathematics Münster. His work focuses on numerical analysis, scientific computing, and machine learning, with a strong emphasis on model reduction techniques for complex systems. Education : PhD in Mathematics (2012), University of Münster, thesis on finitely summable K-homology. Master's and Bachelor's degrees in Mathematics from the University of Münster. Research Interests : Dr. Rave specializes in model order reduction (MOR) methods, including reduced basis techniques, localized orthogonal decomposition (LOD), and nonlinear approximation strategies. His work addresses challenges in multiscale modeling, domain decomposition, and parametrized partial differential equations. He also develops open-source software tools like pyMOR for MOR and contributes to initiatives like the MaRDI (Mathematical Research Data Initiative) to enhance interoperability in scientific computing. Projects : Key initiatives include the MaRDI project (2021–2026), EXC 2044 Cluster of Excellence (Geometry-based modeling), and MULTIBAT (lithium-ion battery simulation). His research bridges theoretical developments with practical applications in battery modeling, electrochemistry, and computational fluid dynamics. Grants & Awards : Funded by DFG, the German Federal Ministry of Research, and internal university grants, his work addresses strategic areas like sustainable research software and energy storage systems. He leads projects on distributed model reduction and communication-avoiding algorithms. Teaching : Dr. Rave teaches advanced numerical methods courses, including Model Order Reduction, Numerical Methods for PDEs, and Python-based computational labs. He co-organizes seminars and workshops on MOR and scientific software engineering.

Ruben MartinsView profile

Assistant Professor

• Constraint Programming
• Program Synthesis
• Formal Verification
• +3 more

Ruben Martins is an Assistant Professor at Carnegie Mellon University's School of Computer Science and serves as the program director of the Master of Science in Computer Science (MSCS) . His research focuses on the intersection of constraint programming, program synthesis, analysis, and verification, with recent work aiming to make formal methods tools more accessible through automated reasoning. Ruben earned his Ph.D. with honors from the Technical University of Lisbon, Portugal (2013) , followed by postdoctoral research at the University of Oxford (2014-2015) and UT Austin (2015-2017) . Research Interests : Ruben's work bridges constraint programming and program synthesis , with applications in software verification , optimization , and automated reasoning . He has developed award-winning tools like Open-WBO , a modular MaxSAT solver that has won gold medals in international competitions. His publications span top-tier venues such as POPL , PLDI , FSE , SAT , and CP , often addressing real-world challenges from program analysis to network security. Scientific Awards include: Distinguished Paper Award at PLDI 2018 Distinguished Paper Award at FSE 2021 Distinguished Paper Award at SAT 2022 Gold medals for Open-WBO in MaxSAT competitions Teaching & Advising : Ruben mentors Ph.D., Master’s, and undergraduate students in research projects related to program synthesis, formal methods, and constraint solving. He teaches courses such as Bug Catching: Automated Program Verification and Advanced Topics in Logic: Automated Reasoning and Satisfiability , emphasizing hands-on experience with tools like Why3. His advising spans topics from AI-driven program repair to network protocol verification , fostering collaboration across disciplines.