Find Academic Supervisors

• Marine Automation
• Aquaculture Systems
• Fish Telemetry
• +4 more

Jo Arve Alfredsen is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), where he has worked since 2004. His research focuses on applying automation, mathematical modeling, and sensor systems to enhance marine production and aquaculture technologies. He coordinates the Fisheries and Aquaculture Systems research group and teaches courses including industrial computer systems design and fish telemetry. PhD in Engineering Cybernetics, NTNU MSc in Engineering Cybernetics, NTNU Alfredsen's research integrates systems biology with cybernetic principles to develop advanced monitoring and control systems for marine environments. His work addresses challenges in aquaculture through numerical simulations, telemetry technology, and sensor network design, aiming to optimize fish welfare and production efficiency. Recent publications demonstrate expertise in underwater robotics, fish behavior analysis, and environmental monitoring. Key topics include wave-propelled unmanned vehicles, dissolved oxygen modeling, and telemetry-based stress indicators. All 15 most recent articles focus on marine automation, sensor integration, and aquaculture optimization. He has developed specialized tools for feed distribution analysis, fish health monitoring, and current flow assessment, contributing to precision aquaculture frameworks. His team's work on acoustic telemetry systems and biometric sensors has advanced real-time monitoring capabilities in commercial marine farms.

• Real-Time Systems
• Worst-Case Execution Time (WCET) Analysis
• Single-Path Code Optimization
• +3 more

Peter Puschner is an Associate Professor at Vienna University of Technology (TU Wien), affiliated with the Cyber-Physical Systems Research Area. His work focuses on real-time systems, worst-case execution time (WCET) analysis, and time-predictable code execution. Key research areas: Real-Time Systems, Single-Path Code, Time-Triggered Networks, Cache Architectures Advisor to multiple thesis students including Michael Platzer and Bekim Cilku Co-developer of tools like the platin Toolkit for WCET analysis Recipient of Best Paper Award at ECRTS 2015 for Requirement Semi-formalization Methodology for SoC Design

Emir DemirovićView profile

Assistant Professor

• Constraint Programming
• Combinatorial Optimisation
• Explainable AI
• +4 more

Dr. Emir Demirović is an Assistant Professor in the Department of Computer Science at Delft University of Technology (TU Delft), The Netherlands. He leads the Constraint Solving ("ConSol") research group and co-directs the Explainable AI in Transportation Lab ("XAIT") as part of Delft AI Labs. Prior roles: Postdoc at University of Melbourne (2017-2020), PhD at Vienna University of Technology (2017) Collaborations: Civil Engineering, QuTech, and industry partners Funding sources: Dutch national funding agency, TU Delft, VoestAlpine Research Focus: Constraint programming and combinatorial optimisation Explainable AI methods for decision-making systems Integration of optimisation with machine learning Robust/resilient optimisation for industrial applications Optimal decision trees with dynamic programming Quantum computing scheduling techniques Scientific Contributions: Pioneered "Pseudo-Boolean Reasoning" for algorithm certification Developed "Blossom" algorithm for optimal decision trees Advances in "Predict+Optimise" frameworks Created Pumpkin constraint programming solver Bridge between SAT/CP and Machine Learning Awards: First Place, MaxSAT Evaluation 2018+ First Place, ROADEF/EURO 2012 Adoption of methods in Google OR-Tools Collaborations: Research visits to EPFL, ANITI/CNRS, CUHK, Monash University, TU Wien Participated in Dagstuhl seminars, Lorentz workshops, and Simons-Berkeley programme

• Wireless Sensor Networks
• Computer Engineering
• IoT
• +7 more

Dr. Uroš Pešović serves as an Associate Professor in the Department of Computer and Software Engineering at the Faculty of Technical Sciences in Čačak, University of Kragujevac, Serbia. With over 15 years of academic service, he has progressed from teaching associate (2007-2018) to assistant professor (2018-2023) and currently holds the position of associate professor for Computer Engineering (2023-2028). Elementary school "Milica Pavlović" Čačak (1989-1997), average grade 5.00 - holder of Vuk's diploma Secondary Technical School Čačak (1997-2001), automation electrical technician, average grade 5.00 - holder of Vuk's diploma Technical Faculty, Čačak, University of Kragujevac (2001-2006), B.Sc. electrical engineer for computer technology, average grade 9.03 Faculty of Electrical Engineering, Computer Science and Informatics, University of Maribor, Slovenia (2007-2010), Master of Electrical Engineering, Major in Telecommunications, average grade 9.75 Faculty of Electrical Engineering, Computing and Informatics, University of Maribor, Slovenia (2010-2016), doctor of technical sciences, doctoral dissertation "Error probability model for IEEE 802.15.4 wireless transmission with co-channel interference and background noise" Dr. Pešović's research spans wireless sensor networks, IoT applications, computer architecture, and reliability engineering. His work demonstrates a strong focus on practical implementations of theoretical models, particularly in IEEE 802.15.4 wireless communication protocols and their applications in precision agriculture, industrial monitoring, and network communication systems. His expertise bridges hardware and software design, with recent work emphasizing machine learning applications for predictive maintenance and anomaly detection in storage systems. Analysis of his recent publications reveals a clear evolution from foundational wireless communication research toward more applied work in precision agriculture, industrial IoT, and educational technology. His current research emphasizes practical problem-solving in agricultural technology using LoRa and other wireless technologies, predictive maintenance of storage systems using machine learning, and innovative approaches to computer engineering education. Dr. Pešović teaches across all academic levels, including undergraduate courses in Computer Architecture, Organization of Computer Systems, Microcontroller Systems, and Computer Networks, as well as graduate courses in Hardware-Software Design, Intelligent Sensors, and Industrial Communication Networks. At the doctoral level, he teaches courses on integrated hardware-software design, disk performance, and telecommunication systems modeling.

Aldo Gonzalez-LorenzoView profile

Associate Professor

• Computational Topology
• Discrete Geometry
• Algorithmic Topology
• +3 more

Aldo Gonzalez-Lorenzo is an Associate Professor (maître de conférences) at Aix-Marseille University in Arles, France, where he conducts research at the GMOD team of the Laboratoire d'Informatique et des Systèmes (LIS). His academic work bridges theoretical computer science with practical applications in computational topology and discrete geometry. His primary research interests focus on computational topology and discrete geometry, with particular emphasis on homology computation, digital topology, and geometric modeling. Gonzalez-Lorenzo develops algorithms for analyzing topological features in digital objects, with applications ranging from 3D mesh processing to motion planning for robotics. His work combines theoretical foundations with practical implementations, often resulting in efficient computational methods for complex topological problems. Analysis of his recent publications reveals a strong trend toward practical applications of topological methods. His work spans from theoretical contributions in Alexander duality and discrete Morse theory to applied research in motion planning, CO2 storage analysis, and 3D mesh processing. A notable pattern is his development of efficient computational methods for measuring and analyzing topological holes across various domains, demonstrating the versatility of topological approaches in solving diverse computational problems. 2022 CG:SHOP Geometric Optimization Challenge winner (with team Shadoks) for coordinated motion planning Best paper award at GTMG 2021 for research on measuring holes in 3D meshes Gonzalez-Lorenzo actively participates in multiple research groups including IAPR-TC18, GT GDMM, and GTMG. His work at the GMOD team within LIS focuses on developing computational methods that bridge theoretical topology with practical applications in computer graphics, robotics, and scientific computing. He has developed several interactive tools and web applications to demonstrate and apply his research, including visualizations of Parcoursup algorithms and interactive tools for working with HDVFs (Homological Discrete Vector Fields).

• Network Reliability and Resilience
• Multi-Domain Networks
• Optical Network Planning
• +3 more

Maria Samonaki is a Researcher at the Chair of Communication Networks (LKN) at Technical University of Munich since April 2022. She holds an Integrated Master's in Electrical and Computer Engineering from Technical University of Crete (2022). Research focus: Reliability and resilience in multi-domain networks , optical network planning, quantum key distribution (QKD) systems, and network sovereignty metrics. Email: maria.samonaki@tum.de Her publications (2022–2025) span IEEE journals and EuCNC conferences , addressing topics like multi-period optical network optimization, survivable routing algorithms, and QKD network upgrades. Collaborators include Prof. Wolfgang Kellerer, Prof. Carmen Mas-Machuca, and Dr. Mario Wenning. No explicit awards or educational details beyond her diploma are documented in the source text. Her work connects to ongoing projects like 6G Future Lab Bavaria and DFG-funded initiatives .

Johannes SchmidView profile

Researcher

• Physics-informed deep learning
• Data-driven surrogate modeling
• Uncertainty quantification
• +11 more

Johannes Schmid, M.Sc., is a Research Associate at the Chair of Vibroacoustics of Vehicles and Machines at the Technical University of Munich . His work focuses on integrating machine learning with computational acoustics, particularly in physics-informed deep learning and uncertainty quantification for vibroacoustic systems. Research Interests Physics-informed deep learning for acoustic modeling Data-driven surrogate modeling for dynamic systems Uncertainty quantification in industrial applications Stochastic Finite Element Methods Interactive acoustics apps for education Publications Schmid has authored/co-authored over 15 publications in computational acoustics, with recent work on neural networks for boundary integral methods, metamaterial design, and hybrid machine learning techniques. His research spans automotive applications, noise control, and educational tools. Laboratory Affiliation He is affiliated with the Chair of Vibroacoustics of Vehicles and Machines , contributing to projects on vehicle acoustics, fluid-structure interaction, and deep learning applications in engineering.

• Computational Design
• Timber Architecture
• Digital Fabrication
• +3 more

Hans Jakob Wagner is a Research Group Leader and Doctoral Candidate at the Institute for Computational Design and Construction (ICD) , University of Stuttgart. His work focuses on Computational Wood Architecture , specializing in advanced computational timber building systems and associated robotic fabrication processes. Research Interests : Computational Design for Timber Systems Robotic Fabrication and Assembly Sustainable Construction Methods Hybrid Material Applications (CFRP, Clay, Bamboo) Co-Design Workflows Digital Quality Control in Timber Production Recent Publications explore circular construction with reclaimed timber, adaptive fabrication for bamboo, point-supported timber slabs, and data-informed co-design. His work bridges Computer Science and Structural Engineering through Neural Networks , Augmented Reality , and Agent-Based Modeling . Scientific Awards : Deutscher Holzbaupreis 2025 (Outstanding Scholarship) Best Poster Award (2023) As an ITECH MSc Thesis Advisor , he mentors students in Gradient Density and Terra [In]Cognita projects. His contributions include 15+ peer-reviewed publications and keynote presentations at international conferences like WCTE and IASS.

• Distributed computing and systems software
• Communications engineering
• Electrical engineering
• +1 more

Dr. Stefano Vissicchio is a Lecturer in the Department of Computer Science at University College London (UCL). His research focuses on distributed computing, software-defined networking, and network security. University College London (UCL), Department of Computer Science – Lecturer His work addresses challenges in BGP security, network resilience, segment routing, and programmable data planes. Key contributions include: Developing fast connectivity recovery mechanisms (e.g., Blink) Creating tools for BGP hijack detection (e.g., Oscilloscope) Advancing hardware-accelerated network control planes Improving low-latency routing in mesh-like backbones Recent publications highlight his expertise in: Network verification (2025) BGP hijack evasion analysis (2024) Transient BGP reconfiguration mitigation (2023) Segment routing and traffic engineering (2022)

• Environmental Fluid Mechanics
• Wildfire Propagation Modeling
• Computational Wind Dynamics
• +5 more

Professor Rob Stoll at the University of Utah's Mechanical Engineering Department specializes in environmental fluid dynamics, wildfire modeling, and urban/agricultural canopy flows. His work bridges computational modeling with field measurements across diverse domains. Develops GPU-accelerated QES simulation tools Focuses on wildfire-atmosphere coupling Advances low-cost sensor networks for environmental monitoring Research Trends revealed through 15 recent publications include: Wildfire propagation under heterogeneous terrain/fuel 3D wind modeling in vineyards and urban environments Particle transport in sparse and row-organized canopies Turbulence characterization in stable boundary layers Open-source hardware for atmospheric sensing Scientific Contributions 2008: Best PhD Dissertation (University of Minnesota) 2017-2022: NSF Grant for wildfire modeling 2020-2025: Systems approach for carrot blight management 2023-2026: Vineyard smoke exposure modeling grants Teaching Activities include graduate courses in Numerical Methods for Engineering Systems and advising PhD/Master's theses since 2015.

Patrick SchmidtView profile

Researcher

• Geometry Processing
• Computer Graphics
• Mesh Processing
• +3 more

Patrick Schmidt is a researcher in the Department of Computer Science at RWTH Aachen University, Germany, specializing in geometry processing and computer graphics. His work focuses on developing robust algorithms for surface mapping, mesh processing, and automatic differentiation, with significant contributions to both theoretical foundations and practical applications. He actively collaborates with the Computer Graphics group led by Prof. Leif Kobbelt, producing high-impact research published in premier venues including SIGGRAPH and Eurographics. His research spans Geometry Processing, Computer Graphics, and Computational Biology, with core interests in distortion-minimizing surface maps, mesh parametrization, and biomedical applications. Schmidt's innovative approaches include adaptive triangulation methods for bijective surface correspondence and lightweight automatic differentiation frameworks like TinyAD that simplify non-linear optimization in geometry processing. His interdisciplinary work on heart tube formation demonstrates how geometric techniques can advance biological understanding of mammalian organ development. Analysis of Schmidt's publication trajectory reveals a consistent focus on improving the robustness and efficiency of surface mapping techniques. His work progressively addresses challenges in distortion control, topological consistency, and computational performance, culminating in award-winning contributions like adaptive triangulation methods and constant-curvature metric representations. The recurring theme across his research is the unification of geometric theory with practical implementation, emphasizing reproducibility through open-source tools and Graphics Replicability Stamps. Scientific recognition includes: Günter Enderle Best Paper Award at Eurographics 2023 for pioneering adaptive triangulation techniques Consecutive Best Paper Awards at SGP 2022 and 2021 for TinyAD library and homology inference methods Honorable mention for Eurographics 2021 work on layout embedding optimization While specific student advising records aren't documented in public sources, Schmidt's role involves mentoring through collaborative research within RWTH Aachen's Computer Graphics group. His projects frequently secure institutional support for high-impact work, evidenced by consistent publication in top-tier venues and development of widely adopted tools like TinyAD. The group maintains strong industry and academic partnerships focused on advancing geometry processing fundamentals. As a core member of RWTH Aachen's Computer Graphics research team, Schmidt contributes to a vibrant ecosystem centered around Prof. Leif Kobbelt's lab. The team emphasizes both theoretical innovation and practical software development, with recent work bridging computer graphics and biomedical modeling. Current efforts focus on intrinsic optimization frameworks and expanding automatic differentiation applications, positioning Schmidt at the forefront of next-generation geometry processing techniques.

Pasi SarolahtiView profile

Lecturer

• Web Performance
• Quality of Service
• Information-Centric Networking
• +4 more

Pasi Sarolahti is a University Lecturer at the Department of Information and Communications Engineering, Aalto University, School of Electrical Engineering. His research focuses on web performance, quality of service, information-centric networking, and wireless communication protocols. Education: Doctoral degree in Engineering and Technology from University of Helsinki (awarded 21 Sept 2007) Research trends: Analysis of web latency and rendering performance, optimization of content delivery networks, adaptive congestion control for wireless systems, and design of information-centric networking protocols. Keywords include Computer Science, Networking, Web Technologies, Cloud Computing, and Wireless Communication. Professional activities: Visiting researcher at foreign institutions (2016) Chair and committee member for IEEE Global Internet Symposium (2012) Conference presentations and contributions (2012)

Robert WardView profile

Research Fellow

• Digital Machining
• Machine Tool Control
• CNC Trajectory Generation & Interpolation
• +5 more

Dr Robert Ward is an Industrial Research Fellow at the University of Sheffield's School of Electrical and Electronic Engineering, with a joint appointment at the Advanced Manufacturing Research Centre (AMRC). His work bridges academic research and industrial applications in autonomous manufacturing systems. MEng(Hons) in Avionics and Aerospace Systems, University of Manchester Engineering Doctorate (EngD) in Machining Science and Control Engineering, University of Sheffield Research interests focus on Digital Machining and Machine Tool Control , particularly in CNC trajectory generation, robotic machining, and digital twin technologies. His recent publications emphasize real-time control algorithms, neural network applications for machining optimization, and synchronization of manufacturing systems. As module leader for Rapid Control Prototyping , he teaches advanced control techniques to undergraduate students. He actively supervises both undergraduate and postgraduate students, emphasizing industrial collaboration and professional preparation. Professional memberships include: Chartered Engineer (CEng) Member of Institute of Engineering and Technology (MIET) Member of Institute of Electrical and Electronic Engineers (MIEEE) At AMRC, Ward leads the Control of Subtractive Manufacturing Operations theme within the Digital Machining Research Team, collaborating on both high-TRL industrial projects and low-TRL research funded by HVMC and EPSRC.

• Formal Methods
• Software Dependability
• Software Security
• +7 more

Panagiotis Katsaros is a Professor at the Department of Informatics, Aristotle University of Thessaloniki. His research focuses on Formal Methods, Software Dependability & Security, and Internet of Things (IoT) systems. 1992: Dipl. in Mathematics, Aristotle University 1993: M.Sc in Software Engineering, Aston University 2002: PhD in Informatics, Aristotle University His work integrates model-based design with probabilistic verification to address challenges in embedded systems , IoT , and cloud environments . He has pioneered methodologies for correctness-by-construction and security-aware elasticity in distributed systems. Recent publications (2015-2019) highlight trends in multi-core embedded systems , probabilistic model checking , and logical error detection . His research bridges formal verification with practical system design . Scientific Recognition Best Paper Award at 17th Panhellenic Conference on Informatics (2013) Best Paper Award at 15th Panhellenic Conference on Informatics (2011) He has supervised PhD students Stylianos Basagiannis and Anakreon Mentis , and contributes to the Dependability & Security Group (DSG A.U.Th) . His work impacts spacecraft design , cloud security , and transaction processing systems .

Chiara GhidiniView profile

Professor

• Artificial Intelligence
• Knowledge Representation
• Business Process Management
• +3 more

Chiara Ghidini is a Full Professor in Computer Science at the Faculty of Engineering of the Free University of Bozen-Bolzano (UNIBZ) in Italy. Her research focuses on symbolic and subsymbolic AI techniques related to Semantic Web, Knowledge Engineering and Representation, and Process Mining. Prior to joining UNIBZ, she worked at Fondazione Bruno Kessler (FBK) in Trento, Italy for over 20 years, where she headed the Process & Data Intelligence (PDI) research unit. Professor Ghidini's research interests span across several areas of Artificial Intelligence with a particular focus on: Artificial Intelligence and its applications in business processes Knowledge Representation and Semantic Web technologies Business Process Management and Mining Explainable AI for process monitoring and decision support Integration of machine learning and simulation for business processes Her recent scholarly output reveals a strong emphasis on developing explainable predictive process monitoring systems, with particular attention to counterfactual reasoning and temporal constraints. The Nirdizati toolkit series represents a significant contribution to the field, evolving from foundational work to the more recent Nirdizati Light framework. Her research bridges theoretical aspects of process mining with practical healthcare applications, as evidenced by projects like the Arianna solution for breast cancer care paths. Professor Ghidini has demonstrated significant academic leadership, serving as program co-chair for major conferences including EKAW 2018, ISWC 2019, and BPM 2020. She currently holds the position of vice-president of AIxIA, the Italian Association for Artificial Intelligence, contributing to national AI strategy development. At UNIBZ, Professor Ghidini teaches programming courses across multiple degree programs: Basics of Programming for the Vocational Bachelor in Wood Technology Introduction to Programming for the Bachelor in Computer Science Introduction to Programming for the Bachelor in Business Information Systems Laboratory for Fundamentals of Programming for the Vocational Bachelor in Wood Technology Her research has substantial practical applications, particularly in healthcare optimization and resource scheduling, with several collaborations involving industrial partners in the Trentino region of Italy. The integration of process mining with machine learning and simulation techniques represents a hallmark of her methodological approach to solving complex business process challenges.