Find Academic Supervisors

Wojciech KmiecikView profile

Researcher

• Optical Networks
• Elastic Optical Networks
• Survivable Overlay Multicasting
• +12 more

Dr. Eng. Wojciech Kmiecik is a researcher at the Department of Computer Systems and Networks, Faculty of Electronics, Photonics and Microsystems, Wrocław University of Science and Technology. He is actively involved in multiple research teams including Machine Learning, Computer Networks, Advanced Data Analysis Methods, and Metaheuristics. He also contributes to teaching and supervises diploma theses. His research focuses on optical networks , survivable multicasting , multi-criteria optimization , and metaheuristic algorithms . He has led and contributed to projects such as Dark-Box Optimization, evolutionary multi-criteria optimization, and advanced methods for multi-layer networks. His work bridges theoretical algorithm development with practical network design. The publication trends from 2010 to 2020 show a consistent focus on network survivability , elastic optical networks , and task allocation in parallel systems . His research integrates optimization techniques into networking solutions, particularly in dual homing architectures and deadline-sensitive provisioning. Key themes include resilience, efficiency, and scalability in both optical and computational systems. Scientific Awards: Medal for long-standing service to Wrocław University of Science and Technology Dr. Kmiecik has supervised diploma theses and is involved in teaching. He has not received externally reported grants, but his sustained project involvement suggests institutional or collaborative funding. He collaborates extensively with Prof. Krzysztof Walkowiak and other researchers in the department. Research Labs and Teams: Machine Learning Team Teaching Team Computer Networks Team Advanced Data Analysis Methods Team Metaheuristics Team

Bernd MeyerView profile

Professor

• Collective Behavior
• Artificial Intelligence
• Robotics
• +6 more

Bernd Meyer is a Professor and Assoc. Dean for Sustainability at Monash University's Faculty of Information Technology. His research spans collective behavior in biological systems, swarm robotics, edge computing, and intelligent diagrammatic systems. He has contributed extensively to understanding task allocation in social insects, noise-driven decision-making in collective systems, and the application of machine learning in resource-constrained environments. Key research themes include: Swarm Intelligence and Ant Colony Optimization Machine Learning for Environmental Sound Classification Self-Organized Infrastructure in Social Insects Adaptive Diagramming Tools for Education Recent work focuses on deep learning pipelines for edge devices and developing frameworks for multi-object tracking in biological systems. His contributions bridge computational models with real-world applications in robotics, sustainability, and educational technology.

Aviral KumarView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +3 more

Aviral Kumar is an Assistant Professor in the School of Computer Science at Carnegie Mellon University (CMU), jointly appointed in the Computer Science Department (CSD) and Machine Learning Department (MLD). He earned his PhD from UC Berkeley in 2023, where he received prestigious awards including the CV Ramamoorthy Distinguished Research Award, Apple PhD Fellowship, and Facebook PhD Fellowship. His research focuses on reinforcement learning (RL), particularly offline RL, scaling RL methods, and their intersection with foundation models. He leads the CMU AI & Reinforcement Learning (AIRe) lab, exploring core RL algorithms, robotics applications, and AI foundation models. Education: PhD in Computer Science from UC Berkeley (2023) Research Interests: Reinforcement Learning Offline Reinforcement Learning Robotics Foundation Models His lab emphasizes scalable RL techniques and real-world applications. Current advisees include Bhavya Kumar Agrawalla and Max Sobol Mark. Notable awards include the CV Ramamoorthy Distinguished Research Award for pioneering contributions to CS research. Lab activities and future work focus on advancing RL for autonomous systems and integrating foundation models into decision-making frameworks. The lab recruits PhD students annually from CMU SCS programs (CS, ML, RI). Undergraduates and MS students can apply via a dedicated form for research opportunities.

Shermin SherkatView profile

Researcher

• Computational Design
• Construction Automation
• Artificial Intelligence
• +1 more

Shermin Sherkat is a doctoral researcher and research associate at the Institute for Computational Design and Construction (ICD) within the Cluster of Excellence IntCDC at the University of Stuttgart. Holding a Master of Science in Digital Technology in Architecture from the University of Tehran, her work focuses on integrating computational design with artificial intelligence applications in construction and fabrication. Her research explores classical Automated Task Planning (ATP) techniques using Planning Domain Definition Language (PDDL) to optimize robotic assembly processes. Current projects involve modeling fabrication domains for pick-and-place operations with multiple end-effectors, addressing challenges in 3D geometry representation and multi-object stacking limitations within classical planning frameworks. Recent publications examine the application of symbolic AI in construction automation, including systematic reviews of ATP methods and case studies on the BUGA Wood Pavilion's cassette fabrication. She contributes to advancing AI-driven task planning while identifying constraints in numerical tracking and geometric grouping within deterministic environments.

• Artificial Intelligence
• Multi-Agent Systems
• Evolutionary Computation
• +4 more

Jörg Denzinger is a Professor at the University of Calgary, Canada, specializing in artificial intelligence, multi-agent systems, and automated testing. His work bridges evolutionary computation, cybersecurity, and game-based simulations, with a focus on emergent behavior and cooperative strategies. Key Research Areas: Multi-Agent Systems, Evolutionary Algorithms, Automated Theorem Proving, Game AI, Data Mining, Cybersecurity, Distributed Systems. Recent Trends: He has concentrated on applying exploratory evolutionary testing to security policy optimization, using deep learning for medical imaging, and modeling emergent coordination in self-organizing systems. Scientific Contributions: Developed frameworks for combining security mechanisms and testing multi-agent systems. Explored decentralized control in water distribution networks and adaptive risk management. Contributed to code reuse tools like Jigsaw and structural correspondence analysis.

Haibo WangView profile

Assistant Professor

• Network Traffic Measurement
• Data Sketches and Streaming Algorithms
• Internet of Things
• +4 more

Haibo Wang is an Assistant Professor in the Department of Computer Science at the University of Kentucky's Stanley and Karen Pigman College of Engineering. His work focuses on network systems, traffic measurement, and data stream algorithms with applications in IoT and edge computing infrastructure. Education: PhD in Computer Science, University of Florida (2019-2023) MS in Computer Science, University of Science and Technology of China (2016-2019) B.Eng, University of Science and Technology of China (2012-2016) Research interests center on network traffic measurement and monitoring using advanced data sketches, streaming algorithms for high-speed networks, edge-cloud computing architectures, and IoT systems optimization . His lab develops novel techniques for real-time network analysis with applications in distributed systems and vehicular edge environments. Recent publications (2023-2025) reveal a strong trajectory in sketch-based network measurement, evolving toward distributed systems for deep learning clusters and vehicular edge computing. Key themes include non-duplicate sampling techniques, burst detection in high-speed streams, and robust resource allocation across multi-cluster environments. Scientific Awards: IEEE ICNP 2021 Best Paper Award Excellent Reviewer of IEEE Transactions on Network Science and Engineering (2023) Distinguished TPC Member of IEEE INFOCOM (2024, 2025) Dr. Wang advises four graduate students: Aayush Karki and Ahashan Habib Niloy (PhD candidates), Jiamu Song and Machael Hentz (Master's students). His service includes TPC roles for IEEE INFOCOM, CLOUD, and MASS conferences, plus journal reviewing for IEEE/ACM Transactions on Networking and IEEE Transactions on Mobile Computing. He leads an active research group developing next-generation network measurement frameworks, with current projects spanning optical circuit switching in reconfigurable datacenters, distributed task offloading for vehicular networks, and efficient cross-cluster communication systems.

• Cyber-Physical Systems
• Computer Science
• Robotics
• +4 more

Jaroslav Klapalek is a PreDoc Researcher in the Cyber-Physical Systems department at Vienna University of Technology (TU Wien). His work focuses on formal verification of distributed timed-automata, resilient control mechanisms, and timing anomalies in automotive architectures. He is affiliated with the Embedded Systems Group (E191-01) and has contributed to research on machine learning for industrial predictive maintenance, consensus algorithms, and energy-efficient scheduling. Research Trends: Recent publications highlight expertise in formal verification of real-time constraints, security analysis in industrial CPS, and timing predictability under clock drift and network delays. Key methods include model checking, hybrid system modeling, and safety-critical protocol validation. Scientific Awards:

• Computer Networks
• Machine Learning
• Reinforcement Learning
• +6 more

Alessio Sacco is a Fixed-term Assistant Professor at the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, Italy. He's affiliated with the NETGROUP - Computer Networks Group and actively contributes to research in computer networks, machine learning, and software-defined networking. Current academic position: Assistant Professor Research group: NETGROUP Key collaborations: Guido Marchetto, Flavio Esposito Research Interests focus on: Computer networks and communications Dynamic programming and reinforcement learning Machine learning applications in network management Software-defined networks Cybersecurity and privacy analysis Intelligent Internet of Things systems Recent Publications demonstrate expertise across: Network security verification (PathSafe) AI-driven agricultural optimization (FertilizeSmart) Cross-layer congestion control (Flecto) Reinforcement learning for computing continuum scheduling Explainable AI for network transparency (ClearNET) Multi-agent exploration systems (MARS) Scientific Contributions include: Commercial research on network security and anomaly detection ERC sector expertise in distributed systems and machine learning Teaching in computer engineering programs since 2019 Advising involves: Supervising Federico Rinaudi (ongoing) Co-supervising Doriana Monaco (ongoing)

• Software Engineering
• Software Evolution
• Knowledge Worker Productivity
• +4 more

Gail Murphy is a Professor in the Department of Computer Science and Vice-President Research & Innovation at the University of British Columbia (UBC). A co-founder of Tasktop Technologies Incorporated, she leads the Software Practices Lab and is affiliated with CAIDA (Centre for Artificial Intelligence Decision-making and Action) and ICICS (UBC Institute for Computing, Information and Cognitive Systems). B.Sc. (Honours), University of Alberta (1987) M.Sc., University of Washington (1994) Ph.D., University of Washington (1996) Her research focuses on software engineering , particularly improving knowledge worker productivity and tools for evolving large-scale systems. She combines lightweight design approaches with industrial evaluations, addressing structural degradation in software systems through: Techniques for understanding/imposing structure in existing systems Design approaches for component reuse and flexible architectures Her recent work includes developer discussion analysis for design recovery (2022), semantic-based task relevance detection (2022), and automated resource-task association (2023). Awards include SIGSOFT Outstanding Research Award (2023), ACM Fellow (2023), and Royal Society of Canada Fellow (2023). She has taught courses like Introduction to Software Engineering (CPSC 310) and Software Construction (CPSC 210).

• Parallel Computer architectures
• High Performance Computing
• Multi-/Manycore architectures
• +5 more

PD Dr. Josef Weidendorfer is a qualified private lecturer at Technische Universität München (TUM) and leads the Future Computing Group at the Leibniz Computing Centre (LRZ). He holds a dual affiliation with TUM's Department of Informatics, Chair of Computer Architecture and Parallel Systems (Prof. Schulz), and the Leibniz Rechenzentrum der Bayerischen Akademie der Wissenschaften. His work focuses on developing smooth migration strategies for future HPC systems and evaluating novel technologies to improve system-level and workload analysis tools. Weidendorfer's research interests encompass Parallel Computer Architectures, High Performance Computing, Multi-/Manycore architectures, GPGPU, Performance analysis and optimization, Cache Simulation, Virtual Machines, and dynamic code generation. He is particularly interested in strategies for improving computational efficiency across various hardware structures, including specialized accelerator hardware for HPC codes. He regularly organizes the UCHPC workshop (since 2010 with Euro-Par) about unconventional hardware for HPC computing and is co-organizer of the PSTI workshop series. His recent publications reveal a strong focus on HPC system optimization, with particular emphasis on load balancing techniques, cache partitioning, application malleability, and performance monitoring. The research trajectory shows increasing attention to practical implementation challenges in modern heterogeneous computing environments, especially regarding GPU utilization, resource partitioning under power constraints, and phase-aware system monitoring. Weidendorfer maintains the open-source tools Callgrind/KCachegrind for cache simulation and has supervised numerous student projects across bachelor's, master's, and guided research programs. He teaches courses including Virtualization Techniques, Parallel Programming Systems, and Advanced Computer Architecture, demonstrating strong commitment to both research and education in computer architecture and parallel systems. As principal investigator for multiple large-scale projects including EU Project SEANERGYS (2025-2028) and BMBF Project ScalNext (2022-2025), he leads significant research initiatives focused on future computing technologies and HPC system development.

Vittorio CozzolinoView profile

Researcher

• Edge Computing
• Computer Vision
• Lightweight Virtualization
• +7 more

Vittorio Cozzolino is a PhD Researcher at the Technical University of Munich since 2015, supervised by Professor Jörg Ott. His work focuses on edge-cloud infrastructures , unikernels , and distributed orchestration frameworks . He previously worked at AgileLab (Turin, Italy) as a Technical Lead and completed an ERASMUS internship at IMDEA Networks (Madrid, Spain) studying MANETs. M.Sc. in Computer Science and Engineering, University Federico II of Naples (2014) His research interests include edge computing architectures , lightweight virtualization , and computer vision for mobile augmented reality . Publications highlight trends in edge-cloud collaboration , unikernel-based systems , and IoT security . He actively contributes to teaching courses on edge computing , recommender systems , and networking fundamentals .

Torben Bach PedersenView profile

Professor

• Data Engineering
• Artificial Intelligence
• Machine Learning
• +5 more

Professor Torben Bach Pedersen at Aalborg University's Department of Computer Science within The Technical Faculty of IT and Design is a leading expert in Data Engineering, Artificial Intelligence, and Energy Systems. With over 394 publications and 19 completed projects, he directs research at the Daisy – Center for Data-intensive Systems and leads innovations in energy flexibility and smart grid technologies. Key research areas: Data Warehousing, AI/ML, Energy Systems, Smart Grids Major projects: domOS (Smart Building OS), FEVER (Virtual Power Plants), DiCyPS (Cyber-Physical Systems) His research spans data-intensive systems, AI applications in energy management, and smart infrastructure development. Recent work focuses on transformer-based network AI and energy flexibility metrics. Scientific recognition includes: Æresdoktor (Honorary Doctor) at TU Dresden (2021) Best Paper Award Runner-Up (2019) WWW 2017 Best Demo Award Best Poster Award World Smart Grid Forum (2013) Member of Danish Academy of Technical Sciences (2013) As principal investigator and supervisor in 17 PhD projects, he advances AI-driven solutions for 6G wireless systems, smart buildings, and energy market optimization.

L. PeternelView profile

Researcher

• Human-Robot Interaction
• Robotics
• Rehabilitation Robotics
• +4 more

L. Peternel is a Robotics researcher at Delft University of Technology's Faculty of Mechanical, Maritime and Materials Engineering, specializing in Human-Robot Interaction and Rehabilitation Robotics. With 56 research outputs including conference contributions, journal articles, and patents, Peternel leads cutting-edge work in impedance control, teleoperation, and collaborative robotics applications spanning medical rehabilitation, off-Earth habitat construction, and service robotics. Research focuses on developing advanced control frameworks enabling natural human-robot collaboration. Key areas include impedance-based teleoperation systems, human motor control-inspired co-manipulation strategies, and biomechanics-aware robotic physiotherapy. Recent work explores quadruped navigation, supermarket service robots with LLM interfaces, and off-Earth habitat construction systems that integrate computer vision with human-robot teamwork. Publications demonstrate strong emphasis on practical implementations with real-world impact across medical, industrial, and extraterrestrial environments. Article trends reveal consistent focus on physical human-robot interaction mechanisms, particularly impedance control methodologies and safety-critical applications. The research portfolio spans rehabilitation robotics (shoulder therapy systems), collaborative construction (off-Earth habitats), and service robotics (supermarket assistants), with increasing integration of AI and computer vision components in recent years. Best Paper Award at FICTA 2024 for Computer Vision- and Human-Robot Interaction-Supported Assembly Finalist Best Interactive Paper Award at Humanoids 2023 for robotic shoulder rehabilitation work Peternel leads the Rhizome project developing autonomous systems for off-Earth habitat construction and collaborates extensively across disciplines including biomechanics, computer science, and aerospace engineering. Media coverage including the Dutch press feature "Een robot als collega" highlights real-world impact of this research. Supervised work includes PhD candidates in robotics, with ongoing projects focusing on patient-centered rehabilitation systems and autonomous construction robotics for space applications.

• Management Information Systems
• Workflow Management
• Complex Event Processing
• +7 more

Dr. Yiannis Verginadis is an Assistant Professor in Management Information Systems at the Department of Business Administration, Athens University of Economics and Business (AUEB). With over 15 years of experience in ICT projects, his research focuses on management of information systems workflow management complex event processing cloud computing and security adaptive systems context-aware access control He holds a Diploma and Ph.D. in Electrical and Computer Engineering from the National Technical University of Athens, specializing in inter-organizational workflow systems for e-Government. His career includes adjunct lecturer roles at the University of Thessaly and Technological Educational Institute of Kalamata. Research trends from his publications include: Cloud continuum brokerage and decentralized data management (2023-2025) Attribute-based access control for healthcare data (2021-2024) Workflow optimization in multi-cloud environments (2016-2023) Federated learning applications (2022-2024) Event-driven architectures and ontological modeling (2009-2021) Security-by-design frameworks (2013-2021) Dr. Verginadis has authored a book on Information Management Systems and contributed to works on e-Participation, adaptive web services, and ambient assisted living systems. He has served on over 25 journal and conference review committees and published 60+ peer-reviewed papers.

• Distributed Computing
• Grid Computing
• Cloud Computing
• +3 more

Chronopoulos Antonis is a Professor at the University of Patras, specializing in distributed computing, grid and cloud computing, numerical algorithms, high performance computing, and scientific computing. His research spans both theoretical numerical methods and practical applications in distributed systems. His primary research interests focus on Distributed Computing , Grid and Cloud Computing , Numerical Algorithms , High Performance Computing , and Scientific Computing . His work bridges theoretical numerical methods with practical distributed systems implementations, particularly in the areas of load balancing, parallel iterative methods, and computational efficiency. Analysis of his publication record reveals a career trajectory that began with foundational work in numerical linear algebra and iterative methods in the late 1980s and 1990s, then evolved toward distributed and cloud computing applications in the 2000s and 2010s. His recent work shows increasing focus on healthcare applications of AI and machine learning, particularly in medical diagnostics and decision support systems. His research demonstrates consistent contributions to both theoretical numerical methods and practical distributed computing systems, with recent work expanding into healthcare applications of artificial intelligence.