Find Academic Supervisors

• Materials Science
• Mechanical Engineering
• Chemical Engineering
• +7 more

Fang Liu is an Assistant Professor at Chalmers University of Technology, Department of Materials and Manufacturing. Her research focuses on uncovering the physical and chemical mechanisms in material systems such as high-temperature alloys, polymer composites, and semiconductors, using advanced microscopy and spectroscopy techniques. Specializes in structural battery composites, creep behavior, and high-temperature corrosion Collaborates with industry partners and theoretical researchers Develops reliable prediction tools for material performance Research Trends (2023–2025): Structural battery composites with carbon fibers and hybrid electrolytes Microstructural analysis via atom probe tomography and focused ion beam High-temperature oxidation and corrosion resistance Mechanical-electrochemical coupling in multifunctional materials Key Collaborations : Leif Asp (Chalmers), Johanna Xu (Chalmers), Marcus Johansen (Chalmers) Industry partners: Office of Naval Research, VINNOVA, Wallenberg AI Program

• Bayesian Statistics
• Reliability Engineering
• Risk Analysis
• +4 more

Ahmad BahooToroody is an Academy Research Fellow at Aalto University’s Department of Energy and Mechanical Engineering, specializing in Bayesian statistics, reliability engineering, and risk analysis for autonomous maritime systems. He is actively involved with the Marine and Arctic Technology research group and leads projects that integrate machine learning with safety-critical applications in the maritime and offshore sectors. Research Interests Bayesian statistical methods for reliability and risk modeling Machine learning and deep learning for anomaly detection in autonomous systems Safety assessment of offshore installations and maritime operations Prognostic health management of marine renewable energy systems Human factors and expert judgment in sociotechnical maritime systems His work often incorporates advanced techniques such as Gaussian processes, LSTM-based neural networks, and dynamic Bayesian networks to address uncertainties in complex engineering systems operating in harsh marine environments. Publication Trends Across 2022–2025, BahooToroody’s publications reveal a strong trajectory toward integrating data-driven models with physics-based simulations. Dominant themes include real-time risk monitoring of autonomous ships, failure prognosis for unattended machinery, and safety assessment frameworks for offshore structures. His collaborative outputs span high-impact journals such as Reliability Engineering & System Safety and Safety Science , underscoring his leadership in maritime safety analytics. Research Groups & Labs Marine and Arctic Technology Research Group, Aalto University Academy Research Fellow network within the Department of Energy and Mechanical Engineering Doctoral Supervision & Grants While specific grant details are not listed, his role as Doctoral Candidate Supervisor since 2020 indicates active mentorship of PhD researchers. He is also the Principal Investigator on projects funded under the Academy of Finland Fellowship scheme, supporting next-generation maritime risk analytics.

• Wireless Communication
• Machine Learning
• Signal Processing
• +4 more

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.

• Wireless Communications
• Integrated Sensing and Communication (ISAC)
• Cell-Free Massive MIMO
• +3 more

Zinat Behdad is a Researcher at the Division of Communication Systems within the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology , Stockholm, Sweden. Her work bridges wireless communications and sensing technologies. Education: Master of Science in Electronics and Communications Engineering, Isfahan University of Technology, Iran (2017) Research Interests: Wireless Communications Integrated Sensing and Communication (ISAC) Cell-Free Massive MIMO URLLC (Ultra-Reliable Low-Latency Communication) Energy Efficiency RF Energy Harvesting Article Trends: Her publications emphasize Cell-Free Massive MIMO systems, with a focus on integrated sensing and communication (ISAC) , mmWave technology , and energy efficiency . Key areas include target detection , power allocation , and URLLC optimization , reflecting her work on balancing sensing accuracy and communication reliability. The 2018 paper explores RF energy harvesting in IoT networks through cooperative strategies. Affiliation and Lab: She is based at the Division of Communication Systems , KTH EECS, working on advanced wireless technologies with applications in security, energy sustainability, and 5G/6G networks.

Damian FlynnView profile

Associate Professor

• Renewable Energy Integration
• Power System Stability
• Wind Energy
• +7 more

Dr Damian Flynn is an Associate Professor at the UCD School of Electrical and Electronic Engineering , University College Dublin. His research focuses on the challenges of integrating renewable energy sources like wind and solar into power systems while maintaining stability and reliability. Research Themes: High renewable penetration, grid-forming converters, energy storage, and smart grid technologies. Collaborations: EirGrid, Glen Dimplex, Electricite de France, and General Electric. Dr Flynn’s work addresses the technical and economic feasibility of transitioning to 100% renewable energy systems, particularly for islanded grids like Ireland’s. His models explore scenarios for 2030–2050, emphasizing the need for adaptive infrastructure and policy frameworks. Key challenges include balancing unpredictable renewable supply with demand, managing grid congestion, and leveraging technologies such as electric vehicles and blockchain for system stability. Recent Publications highlight trends in grid-forming converter design, renewable curtailment reduction, and multi-carrier energy systems. He investigates solutions like transportable storage and dynamic line rating to enhance grid flexibility. Scientific Awards: Smurfit Kappa Newman Fellowship Award.

• Programming Languages
• Formal Verification
• Concurrent and Parallel Systems
• +10 more

Professor Sophia Drossopoulou is a Professor of Programming Languages in the Department of Computing at Imperial College London, part of the Faculty of Engineering. Her affiliations include the Centre for Cryptocurrency Research and Engineering and the Sound Programming Languages research group. She holds a visiting researcher position at Microsoft Research (UK) from May 2019 to May 2020. Her research focuses on foundational programming language design and formal methods, emphasizing concurrency, type systems, and program verification. Key areas include concurrent program reasoning (e.g., TaDA framework), memory management (reference capabilities, garbage collection), and secure systems (smart contracts, cyber-physical systems). She explores practical language extensions for performance optimization (e.g., cache locality) while maintaining safety guarantees through formal verification techniques. Her work spans theoretical contributions (formal semantics, logical frameworks) and applied systems (compilers, runtime verification tools like Zeno). Recent trends show strong engagement with actor-based models (Pony language), digital twins, and cybersecurity challenges in modern software systems. Awards and recognitions are not explicitly listed in the provided text, but her extensive publication record in top venues (ECOOP, POPL, TOPLAS) indicates academic impact. Her advising focuses on graduate students in systems programming and formal methods, though specific student names are not mentioned here. Labs and collaborations involve the Sound Programming Languages group at Imperial College, emphasizing interdisciplinary work between formal methods and practical language implementation. Current projects include improving concurrency semantics and verifying complex systems through compositional reasoning techniques.

Niels Henrik MortensenView profile

Professor

• Product Architecture
• Modularization
• Maintenance Performance
• +3 more

Niels Henrik Mortensen is a Professor and Head of Section in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU). His research focuses on engineering design and manufacturing systems, with emphasis on product architecture, modularization, maintenance performance, and AI-driven design solutions. He leads initiatives in engineer-to-order systems, lifecycle costing, and digital transformation in manufacturing. Key research interests include optimizing product architectures for modular systems, enhancing maintenance strategies through data analytics, and applying AI to improve CAD design reuse. His work aligns with UN Sustainable Development Goals related to innovation and infrastructure, and sustainable energy systems. Supervisor for 5 active PhD projects focused on modular architectures, logistics services, and configuration systems Published 175+ peer-reviewed articles, including work on AI-based maintenance frameworks and configurator development Recipient of industry collaboration projects with offshore energy and manufacturing sectors Notable contributions include frameworks for maintenance performance diagnostics and adaptable configuration models. His team operates through MEK and CONSTRUCT research groups at DTU.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +4 more

Prof. Frank-Peter Schilling is a Senior Lecturer at Zurich University of Applied Sciences (ZHAW) School of Engineering and Deputy Director of the Centre for Artificial Intelligence (CAI). He leads the Intelligent Vision Systems group and coordinates the PhD Programme in Data Science with the University of Zurich. As an Adjunct Professor at Victoria University of Wellington, he specializes in AI, Machine Learning, and applications in healthcare and physical sciences. His research focuses on deep learning-based computer vision, MLOps, and trustworthy AI certification frameworks. Education: PhD in Physics (University of Heidelberg, 2001) Dipl.-Phys. (MSc equivalent in Physics, University of Heidelberg, 1998) CAS University Didactics (PH Zurich, 2024) Research Interests: Developing AI systems for medical imaging (e.g., CBCT artifact reduction) Certification schemes for AI trustworthiness (e.g., certAInty project) Applications of deep learning in particle physics and industrial vision Achievements: Recipient of the EPS HEP Prize (2013) for contributions to the Higgs boson discovery at CERN Lead author of over 20 peer-reviewed articles on AI, MLOps, and medical imaging Principal investigator for projects like AI-BRIDGE (responsible AI development) and GenAI4SKA (Square Kilometre Array simulations) Teaching: Courses in MLOps, Machine Learning Operations, and Computer Vision at BSc and MSc levels. Developed the CAS Advanced Machine Learning program. Labs & Networks: Active in ELLIS (European Lab for Learning and Intelligent Systems), CLAIRE (AI research), and ZHAW’s Digital Health/Datalab initiatives.

Daniel RomeroView profile

Professor

• UAV Communications
• Wireless Communications
• Time-Series Analysis
• +5 more

**Daniel Romero** is a **Professor** in the **Department of Information and Communication Technology** at the **University of Agder**, Norway. His research focuses on UAV communications, time-series analysis using machine learning and network science, and decentralized processing for sensor networks. He holds a Ph.D. in Signal Theory and Communications from the University of Vigo (2015), an M.Sc. in Signal Theory (2011), and a Telecommunication Engineering degree (2009). **Education**: Ph.D. in Signal Theory and Communications, University of Vigo (2015) M.Sc. in Signal Theory and Communications, University of Vigo (2011) Telecommunication Engineering, University of Vigo (2009) **Research Interests**: His work spans UAV communication systems (focusing on low-latency, high-reliability networks), time-series analysis for complex systems (using ML and network science), and decentralized computation in sensor networks to improve robustness and hardware efficiency. Recent projects include radio map estimation for mmWave beam alignment, spoofing detection via graph neural networks, and aerial base station placement optimization. **Publications**: Over 30+ peer-reviewed articles in top venues like IEEE Transactions on Wireless Communications and ICC. Recent trends emphasize radio map estimation (2023–2024), UAV-enabled spectrum surveying (2022), and robust D2D communications (2022). **Advising & Grants**: Teaches PhD courses (Statistical Signal Processing, Advanced Optimization) and leads the **Advanced Signal Processing Lab (ASL)**. Collaborates with the **CIEM (Center for Integrated Emergency Management)** on crisis-related communication systems. **Labs/Teams**: Directs the Advanced Signal Processing Lab (ASL.uia.no) and contributes to CIEM, applying ML and signal processing to emergency management challenges.

Kaspar RiesenView profile

Researcher

• Pattern Recognition
• Machine Learning
• Computer Vision
• +4 more

PD Dr. Kaspar Riesen is the Head of the Pattern Recognition Group at the Institute of Computer Science, University of Bern. His research focuses on graph-based methods for pattern recognition, with applications in document analysis, environmental modeling, and healthcare. Key interests include graph matching, neural networks, and spatio-temporal modeling. His work spans structural pattern recognition, graph embeddings, and keyword spotting in historical documents. Recent projects involve river network analysis using graph regression and hypoglycemia prediction via LSTM-GNN hybrid models. Publications emphasize graph theory advancements, such as normalized graph compression and geometric similarity learning. Collaborations include developing specialized algorithms for automated error detection and improving decision-making in simulated sports. Labs/Teams: Pattern Recognition Group (PRG) at the University of Bern.

George ExarchakosView profile

Assistant Professor

• P2P Computing
• Data Mining
• Machine Learning
• +4 more

George Exarchakos is an Assistant Professor in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e), affiliated with the EAISI High Tech Systems and the Center for Wireless Technology. His research focuses on P2P computing, data mining, machine learning, network optimization, and swarm intelligence. He holds an MSc in Advanced Computing from Imperial College London and a PhD in P2P Computing from the University of Surrey (2008). Prior to his current role, he conducted postdoctoral research on autonomous networks at TU/e before becoming an Assistant Professor in 2011. Key projects include HiCONNECTS: Heterogeneous Integration for Connectivity and Sustainability (2023–2025) and RHIADA: Reliable Hybrid Intra Aircraft Datanetwork Architectures (2021–2025). His work contributes to UN Sustainable Development Goals related to innovation and infrastructure (Goal 9). Research interests span predictive networks, gossip protocols, overlay networks, and network complexity. Notable publications include studies on beyond-5G networks, avionics communication protocols, and edge computing resource management.

Manos KapritsosView profile

Associate Professor

• Distributed Systems
• Formal Verification
• Fault-Tolerant Replication
• +3 more

Manos Kapritsos is an Associate Professor in the Department of Computer Science & Engineering at the University of Michigan, Ann Arbor. His research focuses on increasing the reliability of distributed systems through formal verification, fault-tolerant replication, and automated proof techniques. He has contributed to projects such as Aegean (replication beyond client-server models), I4 (automated inductive invariant inference), and Armada (concurrent code verification). His work emphasizes practical applications of formal methods to ensure system correctness and performance. Education details are not explicitly provided in the text, but his career trajectory suggests advanced academic training in computer science. His research interests include distributed systems, formal verification of protocols, and high-performance systems. Notable awards include the NSF CAREER Award (2021), Google Faculty Award (2017), and Distinguished Paper Awards at PLDI 2020 and USENIX Security 2017. Teaching includes courses on formal verification, operating systems, and distributed systems, reflecting his expertise in systems software. He has advised students through his GLaDOS research group, though specific advisee names are not listed. Key grants include NSF FMitF and Large grants to advance verification techniques for distributed systems. Recent articles focus on automating proofs for undecidable protocols (Basilisk), efficient replication communication (Scrooge), and formal latency analysis (Performal). His work bridges theory and practice, aiming to simplify end-to-end verification of complex systems.

Mehdi SadiView profile

Assistant Professor

• Secure and Reliable System-on-Chip Design
• AI and Neuromorphic Hardware
• Safety-critical Hardware
• +2 more

Mehdi Sadi is an Assistant Professor of Electrical and Computer Engineering at Auburn University's College of Engineering. He holds a Ph.D. from the University of Florida, an M.S. from the University of California-Riverside, and a B.S. from Bangladesh University of Engineering and Technology. His research focuses on secure and reliable system-on-chip design, AI/ML-driven VLSI CAD/EDA, neuromorphic hardware, and emerging post-CMOS computing technologies. Notable achievements include earning the NSF CAREER Award for chiplet-based design optimization and a $175k NSF grant for magnetic RAM research. His work integrates machine learning with hardware co-design to enhance AI accelerators' performance, energy efficiency, and security. Recent projects include adversarial attack mitigation on AI hardware and reliability analysis of neuromorphic systems. Dr. Sadi's contributions span chiplet architecture, memory systems (e.g., STT-MRAM/SOT-MRAM), and fault-tolerant computing. He actively publishes on topics like skyrmion logic gates and TRNG implementations using MRAM. His work bridges theoretical machine learning advancements with practical hardware implementations, addressing critical challenges in next-generation computing systems.

• Human-Machine Interaction (HMI)
• Augmented Reality (AR)
• Virtual Reality (VR)
• +4 more

Overview ASSILA Ahlem is a Researcher-Lecturer at CESI, specializing in Human-Machine Interaction (HMI), Augmented Reality (AR), and Virtual Reality (VR). She holds a PhD in Computer Science from Université de Valenciennes (2016) and a postdoctoral position at Institut Image ARTS ET METIERS PARISTECH (2017). Her research focuses on usability evaluation, digital twin technology, and BIM-integrated XR systems. She has supervised multiple engineering and master’s projects, including AR application development for network management. Research Contributions Developed frameworks for integrating subjective/objective usability metrics using ISO standards Proposed maturity models for BIM-based AR/VR systems Explored digital twin applications in manufacturing and construction industries Education & Responsibilities Teaches computer science at all engineering levels (L1-M2) at CESI Reims, including algorithmics, HMI design, and project-based learning. Served as pilot for engineering program cycles (2017–2020). Active in organizing international conferences (e.g., HCI 2020, Flexible Automation 2018) and peer review for journals like IJISE and IEEE VR. Awards & Recognition No specific awards listed, but recognized for contributions to HCI and industry-relevant research. Advising & Grants Supervised over 10 student projects including PFEs and internships. Actively participates in jury panels for engineering thesis defenses and academic promotions across multiple institutions. Labs & Collaborations Member of the CESI Chair for Industry and Services of Tomorrow, focusing on technology integration in construction and manufacturing sectors.

• Computer Operating Systems
• Computer Systems Analysis
• Distributed Systems
• +5 more

Liuba Shrira is a Professor of Computer Science at Brandeis University, affiliated with the Michtom School of Computer Science and the Benjamin and Mae Volen National Center for Complex Systems. Her research focuses on distributed systems, storage systems, blockchain technology, concurrent programming, and system architectures. She holds a Ph.D., M.S., and B.S. from the Technion – Israel Institute of Technology. Her work emphasizes reliable and highly available systems, including innovations in snapshot management, transactional memory, and adversarial cross-chain commerce. She has been recognized with awards such as the ACM Distinguished Scientist (2009), Lady Davis Fellowship (2010-2011), and a Best Paper Award (2020). Her research has been supported by grants from the National Science Foundation and other institutions. Recent publications highlight advancements in optimistic concurrency control, blockchain interoperability, and modular past-state systems. Shrira has also contributed to middleware design and distributed computing frameworks, with applications in both academic and industry settings.