Find Academic Supervisors

Cao JiannongView profile

Academic

• Cloud and edge computing
• Parallel and distributed computing
• Big data analytics
• +2 more

Cao Jiannong is currently a Chair Professor and Director of the University Research Facility in Big Data Analytics at Hong Kong Polytechnic University . He has held academic roles including Assistant Professor at City University of Hong Kong and University Lecturer at the University of Adelaide and James Cook University. His research spans Cloud and Edge Computing , Parallel and Distributed Systems , Big Data Analytics , and Wireless Sensing . Ph.D. in Computer Science, Washington State University (1990) MSc in Computer Science, Washington State University (1986) BSc in Computer Science, Nanjing University, China (1982) His work focuses on solving theoretical and practical challenges in distributed computing , mobile cloud systems , and wireless sensor networks . Recent projects include coupled network embedding models for heterogeneous networks and SDN architectures for vehicular communication. His research also pioneers WiFi-based non-invasive health monitoring and fault-tolerant sensor deployment for structural health applications. Dr. Cao's publications highlight advancements in network embedding , edge computing , and WSN optimization . Key papers address multi-user computation partitioning , energy-efficient SHM systems , and consensus protocols for mobile networks. These works have been cited over 15,000 times, with an h-index of 60. Ministry of Education (China) Natural Science Award (2018) Distinguished Member, ACM (2017) Fellow, IEEE (2014) Best Paper Awards at IEEE DSAA, SMARTCOMP, and WCNC Dr. Cao has advised multiple PhD students, including Linchuan Xu and Weigang Wu , whose research on WSN-based SHM and coupled network embedding has practical impact. His leadership includes directing Hong Kong Polytechnic University's Big Data Research Facility and serving on technical committees for IEEE INFOCOM and ACM/IEEE conferences.

Yunjie YangView profile

Associate Professor

• AI-powered sensing and imaging
• Machine learning
• Soft sensors & electronics for robotics
• +5 more

Dr. Yunjie Yang is an Associate Professor at the University of Edinburgh's School of Engineering, with affiliations at the Edinburgh Futures Institute (EFI), the Edinburgh Generative AI Laboratory (GAIL), and the Edinburgh Centre for Robotics. He previously held the Chancellor's Fellow in Data Driven Innovation (2018-2023) and Bayes Innovation Fellow (2023-2024) positions. His research focuses on AI-powered sensing and imaging, machine learning, and soft sensors & electronics for robotics. Yang received his PhD in Engineering Electronics from the University of Edinburgh, MSc in Control Science & Engineering from Tsinghua University, and BEng in Measurement & Control Engineering from Anhui University. After his PhD, he worked as a Postdoctoral Research Associate in Chemical Species Tomography before securing his lectureship. His research interests center on developing intelligent sensing systems that replicate human perception capabilities for robotics and intelligent systems. He pioneers flexible sensing and imaging technologies across various scales through innovative multi-modal sensors, soft electronics, and their modeling using machine learning approaches. His work aims to enable autonomous physical artificial intelligence by bridging the gap between robotic systems and human-like perception. Analysis of his recent publications reveals a strong focus on soft robotics perception, particularly through electrical impedance tomography (EIT) and transformer-based architectures. His research spans medical imaging applications, digital twin modeling for industrial processes, and machine learning approaches for sensor data interpretation. The trend shows increasing integration of physics-informed deep learning with traditional tomographic techniques to achieve higher accuracy and efficiency. European Research Council (ERC) Starting Grant (2024) IEEE J. Barry Oakes Advancement Award (2024) IEEE I&M Society Graduate Fellowship Award (2015) Multiple Best Paper Awards Senior Member of IEEE Fellow of the International Society for Industrial Process Tomography Fellow of the Higher Education Academy ESI highly cited papers Dr. Yang serves as Associate Editor for IEEE Transactions on Instrumentation and Measurement and holds editorial positions with Scientific Reports and IEEE Sensors Journal. His research has been licensed to overseas research institutes and industry partners and received wide media coverage including BBC, EFE, USA Today, and STV. He has secured significant grant funding including the prestigious ERC Starting Grant. He leads the Edinburgh SMART Lab (Sensing/imaging + Machine Learning + Robotics), which aims to replicate human perception capabilities for robotics and advance flexible sensing technologies through innovative multi-modal sensors and machine learning approaches. The lab focuses on enabling autonomous physical artificial intelligence with applications spanning medical diagnostics, industrial monitoring, and advanced robotics systems.

• Dependable Distributed Systems
• Web Engineering
• Convergence of Voice and Data
• +5 more

Karl Michael Göschka is an Associate Professor at Vienna University of Technology (TU Wien) in the Department of Distributed Systems (E194-02) within the Faculty of Computer Science. His academic title of Privatdozent indicates he has completed habilitation, the highest academic qualification in the German/Austrian system. He maintains an active research and teaching profile with courses scheduled through 2026S, including Bachelor Thesis supervision, Computer Science Projects, and PhD Seminars. His research spans over two decades with consistent publications from 2004 to 2022. Göschka's primary research interests focus on Dependable Distributed Systems, Web Engineering, and the convergence of voice and data technologies. His work demonstrates particular expertise in middleware, service-oriented architecture, wireless sensor networks, and fault tolerance mechanisms. He has supervised numerous master's and diploma theses since 2006, with his most recent doctoral supervision completed in 2022. Göschka's publication record shows a clear evolution from foundational work in distributed systems and replication (2004-2013) to more recent contributions in wireless sensor networks and dependable computing (2020-2022). His research consistently addresses the challenge of balancing dependability, performance, and security in distributed environments, with notable contributions in temporal decoupling techniques and adaptive systems. GIT-Preis des Österreichischen Vereins für Elektrotechnik (1999) Würdigungspreis des Ministeriums für Wissenschaft und Forschung (1998) GIT-Förderpreis des Österreichischen Vereins für Elektrotechnik (1994) Göschka has led and participated in multiple significant research projects including TRADE (2008-2011) on adaptive performance optimization, COMPASS (2005-2007) for automotive software systems, S-Cube (2008-2012), ALL-TIMES (2007-2010), and DeDiSys (2004-2007) on dependable distributed systems. His research has strong practical applications in automotive systems, online auctions, and critical infrastructure monitoring.

• Computer Vision
• Autonomous Driving
• Robotics
• +7 more

Dominik Schörkhuber is a PreDoc Researcher at the Vienna University of Technology (TU Wien) in the Computer Vision department. With a background in Informatics (BSc, Dipl.-Ing.), he focuses on computer vision applications for autonomous driving, robotics, and human-machine interaction. His work spans driver action recognition, pedestrian prediction, and adaptive lighting systems. Current projects: Empathic Vehicle (2024–2026), SyntheticCabin (2021–2025), SmartProtect (2020–2025) Research themes: Video transformers, synthetic data transfer learning, multi-task learning, and sensor-lighting integration Specializes in 3D sensing, nighttime driving analysis, and mobile video creation tools

Jiannong CaoView profile

Professor

• Cloud and edge computing
• Parallel and distributed computing
• Big data analytics
• +2 more

Jiannong Cao is a Chair Professor and Director of the University Research Facility in Big Data Analytics at the Department of Computing, Hong Kong Polytechnic University. He has held various academic roles since 1990, including Assistant Professor at City University of Hong Kong and Lecturer at Australian universities. PhD in Computer Science, Washington State University (1990) MSc in Computer Science, Washington State University (1986) BSc in Computer Science, Nanjing University (1982) His research focuses on cloud and edge computing , parallel and distributed computing , and mobile computing , with significant contributions to wireless sensor networks (WSN) for structural health monitoring (SHM) and software-defined networking (SDN) for vehicular communications. Recent work includes WiFi-based non-invasive health monitoring systems and multi-user computation partitioning in mobile cloud environments. Dr. Cao’s publications demonstrate trends in WSN optimization , SDN architectures , and cognitive modeling for network embedding , with applications in smart healthcare , transportation systems , and industrial IoT . Ministry of Education Natural Science Award (2018) ACM Distinguished Member (2017) IEEE Fellow (2014) Best Paper Awards at IEEE DSAA, SMARTCOMP, WCNC He has mentored numerous researchers, including Linchuan Xu , Xuefeng Liu , and Weigang Wu , who have authored key publications in top venues like ACM WSDM and IEEE INFOCOM . His professional roles include chairing IEEE committees and serving on grant panels for the Hong Kong Research Grant Council.

Atakan AralView profile

Associate Professor

• Edge Computing
• Distributed Systems
• Cloud Computing
• +6 more

Atakan Aral serves as an Associate Professor at the Faculty of Computer Science, University of Vienna, where he leads research in edge computing, distributed systems, and environmental monitoring applications. His work focuses on developing efficient and resilient computing systems for environmental applications, with particular emphasis on neuromorphic edge AI and the cloud-edge continuum. He maintains an active teaching schedule offering courses in Distributed Systems Engineering, Cloud Computing, and Practical Software Courses with Bachelor's Thesis work across multiple semesters through 2025. Dr. Aral's research interests span several critical areas in modern computing including edge computing architectures, federated learning approaches, neuromorphic computing for environmental monitoring, and resilient systems design. His work addresses fundamental challenges in resource-constrained environments, particularly focusing on latency-sensitive applications and energy-efficient computation. The interdisciplinary nature of his research bridges theoretical computer science with practical environmental applications, developing systems that can operate effectively in remote or resource-limited settings. Analysis of his recent publication trajectory reveals a clear evolution from foundational cloud computing research toward increasingly specialized edge intelligence systems. Early work focused on resource allocation and scheduling in cloud environments, while his current research emphasizes neuromorphic approaches for sustainable environmental monitoring. His publications demonstrate growing interdisciplinary collaboration, particularly with environmental scientists, and increasing focus on practical implementations of theoretical concepts in real-world monitoring systems. Dr. Aral leads significant research projects including TROCI (Towards Resilient Operation of Critical Infrastructure), an ongoing initiative, and SWAIN (Sustainable Watershed Management Through IoT-Driven AI), which ran from February 2021 to February 2024. His work spans multiple dimensions of computing systems, from hardware-aware algorithms to application-level implementations, with consistent contributions to major conferences and journals in distributed systems and edge computing. He is an active member of the Scientific Computing research group at the University of Vienna, working from Room 6.49 at Währinger Straße 29. His research environment includes collaboration with the Environment and Climate Research Hub, reflecting the interdisciplinary nature of his work that bridges computer science with environmental applications. His publications indicate strong international collaboration across European institutions and research groups.

Hui PanView profile

Professor

• Networking
• Complex networks
• Mobile computing
• +3 more

Hui Pan is a distinguished academic holding dual positions as Nokia Chair in Data Science and Professor of Computer Science at the University of Helsinki, and Chair Professor of Computational Media and Arts at the Hong Kong University of Science and Technology (HKUST). His research spans networking, mobile computing, augmented reality, and computational social science. He earned his Ph.D. in Computer Science from the University of Cambridge in 2007. His work bridges social networks with mobile systems, pioneering fields like mobile social networks and opportunistic forwarding algorithms. Research interests include data science, complex networks, and innovative applications of augmented reality. His recent publications focus on low-latency AR frameworks, blockchain for computation offloading, and mobile web visualization. He has received prestigious awards, including IEEE Fellow (2018), ACM Distinguished Scientist (2016), and the Nokia Chair Endowment (2017). He has supervised over 15 PhD and 12 MPhil graduates, with 13 current Ph.D. students and 2 MPhil students. His editorial roles include Associate Editorships at IEEE Transactions journals and guest editorships at top venues like IEEE JSAC and ACM Transactions. He has organized conferences such as WWW Track Chair and ExtremeCom General Chair.

Cornelia SchneiderView profile

Professor

• Health Informatics
• Augmented & Mixed Reality Applications
• Sensor Data Analysis
• +4 more

Cornelia Schneider is a Professor and Head of the Institute of Computer Science at the University of Applied Sciences Wiener Neustadt. She leads research in digital health and care technologies, focusing on applications like Augmented Reality, sensor data analysis, and social robotics for vulnerable populations. Her work emphasizes user-centered design and has been supported by projects funded by FFG and the Active Assisted Living Programme. Her research spans telemedicine infrastructure, elderly care robotics, and health monitoring systems. Notable projects include 24/7-Digital (2023-2026), Care about Care (2021-2023), and AgeWell (2019-2022). She has pioneered solutions like CARU cares and DigiCare training programs. Dr. Schneider has been recognized with the AAL Award (2014) and the tecnet | accent Innovation Award (2021). She coordinates interdisciplinary teams and actively participates in conferences like Health Informatics Meets Digital Health. Her lab collaborations include FOTEC and Salzburg Research, where she previously led the e-Health Competence Center until 2019.

Markus VinczeView profile

Associate Professor

• Robotics
• Computer Vision
• Human-Robot Interaction
• +5 more

Markus Vincze is an Associate Professor at the Institute of Automation and Control Engineering (ACIN) at Vienna University of Technology (TU Wien). He founded the Vision for Robotics (V4R) group in 1996 to advance robotic perception, particularly in real-world environments and homes. His work focuses on cognitive computer vision techniques for robotics. Education: Diplom in Mechanical Engineering (1988) and PhD (1993) from TU Wien; M.Sc. (1990) from Rensselaer Polytechnic Institute. V4R coordinates EU projects like ActIPret, robots@home, HOBBIT, and national initiatives like vision@home. Markus has edited a book on Robust Vision with Gregory Hager and authored 62 peer-reviewed journal articles and over 400 reviewed publications. His recent research explores zero-shot 6D pose estimation, sim-to-real transfer, and transparent object detection. Markus has served as program chair for ICRA 2013 and organized HRI 2017 in Vienna. He has advised numerous students and secured grants from the Austrian Academy of Sciences for work at HelpMate Robotics and Yale's Vision Laboratory. The V4R group leads innovations in robotic vision, including frameworks for synthetic data generation (Unrealgensyn), depth completion (CAGT), and educational robotics applications for sustainability. Their work spans household robotics (RH3), agricultural robotics (EdgeSoil), and human-robot collaboration.

Marco Di RenzoView profile

Professor

• Wireless Communications
• Cellular Networks
• Reconfigurable Intelligent Surfaces
• +7 more

Marco Di Renzo is a CNRS Professor (Directeur de Recherche Titulaire) at University of Paris-Saclay, affiliated with CentraleSupelec and the Signals and Systems Laboratory (L2S). He serves as Coordinator of the Communications Networks Area at the DigiCosme Laboratory of Excellence and Editor-in-Chief of IEEE Communications Letters. His academic leadership includes membership in the Ph.D. School on ICT Admission Committee at Paris-Saclay University. His educational background includes a Laurea (cum laude) and Ph.D. in Electrical Engineering from University of L'Aquila, Italy (2003, 2007), and a Habilitation à Diriger des Recherches from University Paris-Sud (2013). Laurea (cum laude), Electrical Engineering, University of L'Aquila (2003) Ph.D., Electrical Engineering, University of L'Aquila (2007) Habilitation à Diriger des Recherches, University Paris-Sud (2013) Di Renzo's research focuses on next-generation wireless communications, particularly reconfigurable intelligent surfaces (RIS), 6G technologies, and stochastic geometry modeling. His work bridges theoretical communication theory with practical implementations in cellular networks, millimeter-wave communications, and ultra-wide band systems. Recent publications demonstrate leadership in holographic metasurfaces, integrated sensing and communication (ISAC), and AI-empowered network design, establishing him as a pioneer in electromagnetic wave manipulation for future networks. His award-winning publications span RIS-aided communications, channel modeling, and security frameworks. Analysis of his recent work reveals consistent focus on three pillars: (1) fundamental electromagnetic theory for wave manipulation, (2) practical RIS implementations across frequency bands, and (3) integration with AI for network optimization. His articles frequently address industrial applications including factory automation and space-air-ground networks. Di Renzo's scientific recognition includes: IEEE Fellow (2020) and IET Fellow (2020) Highly Cited Researcher (Web of Science, 2019) SEE-IEEE Alain Glavieux Award (2017) Multiple Best Paper Awards (IEEE ICC, EURASIP) Nokia Foundation Visiting Professorship (2020) As Principal Investigator for CNRS, he coordinates multiple Horizon 2020 projects including SURFER, PathFinder, and MetaWireless. His leadership extends to serving as Project Coordinator for H2020 5Gwireless, 5Gaura, MAPNET, and REDESIGN. With over 350 publications, 17,000+ citations, and h-index of 66+, his research group maintains strong industry partnerships with Nokia and other telecommunications leaders. Di Renzo directs the Signals and Systems Laboratory (L2S) at Paris-Saclay and coordinates the DigiCosme Excellence Lab's Communications Networks Area. His team specializes in electromagnetic modeling for wireless networks and has pioneered the European Telecommunications Standards Institute (ETSI) Industry Specification Group on RIS. The group maintains active collaborations with Aalto University (Finland), University of Technology Sydney (Australia), and University of L'Aquila (Italy).

• Quantum Physics
• Quantum Information Science
• Quantum Computing
• +2 more

Marco Canteri is a Researcher in the Department of Experimental Physics at the University of Innsbruck, Austria. He holds an MSc degree and contributes to quantum information science with a focus on trapped-ion systems for quantum computing and networking. His educational background includes a Master of Science degree. The specific institution is not provided in available sources. Canteri's research spans quantum networking, quantum communication, and quantum sensing using trapped ions. Key areas include ion-photon interfaces, quantum memory, entanglement distribution, and scalable quantum hardware. His work addresses challenges in noise resilience and long-distance quantum links through experimental implementations of multiqubit systems and telecom-wavelength interfaces. Analysis of his 12 recent publications (2020-2025) reveals consistent advancement in trapped-ion quantum technologies. Notable achievements include entanglement distribution over 101 kilometers, multimode quantum networking, and development of ten-qubit quantum registers. The research demonstrates strong interdisciplinary integration of atomic physics, quantum optics, and quantum information theory for practical quantum network applications. No scientific awards are listed in the available information. Information regarding students advised or research grants obtained by Marco Canteri is not provided in current sources. He operates within the quantum information research group of the Department of Experimental Physics, utilizing advanced laboratories equipped for trapped-ion quantum computing experiments including optical cavities, precision laser systems, and single-photon detection infrastructure under department head Hanns-Christoph Nägerl.

Giles FoodyView profile

Professor

• Citizen science
• Geographical information science
• Remote sensing
• +3 more

Giles Foody is a Professor of Geographical Information Science at the School of Geography, University of Nottingham, and a member of the Rights Lab in the Faculty of Social Science. He is recognized as the UK’s most prolific and highly cited researcher in remote sensing, with a focus on interdisciplinary applications for real-world impact. Education: BSc (1st class honours) and PhD from the University of Sheffield. His research spans image classification for thematic mapping, particularly in land cover and human-induced changes. He pioneered soft image classifications, object-based methods, neural networks in remote sensing, and citizen sensors in mapping. Current projects include 'slavery from space' and Sargassum beaching analysis to meet UN SDGs. The trends in his publications highlight advancements in remote sensing, citizen science, and land cover mapping. His work integrates machine learning and geospatial analysis for social and environmental challenges. Scientific awards: IEEE Fellowship, David Landgrebe Award, Founder's Award (ISARA), multiple RSPSoc accolades, and SDG-related honors. Giles has supervised 51 research students and contributed to academic service via editorial roles, peer review leadership, and participation in national research assessment panels. His interdisciplinary work extends to European National Mapping Agencies and anti-slavery initiatives.

Jiang HongboView profile

Professor

• Distributed systems
• Mobile computing
• Smart sensing
• +5 more

Hongbo Jiang is a Distinguished Professor and Vice Dean of the College of Computer Science and Electronic Engineering at Hunan University, China. He holds concurrent roles as Director of the Trusted Systems and Networking Key Laboratory of Hunan Province and Director of the Hunan International Technical Cooperation Base for High-Performance Computing and Distributed Systems. His academic journey includes tenures as a Professor at Huazhong University of Science and Technology and a Hong Kong Scholar Research Fellow at The Chinese University of Hong Kong. Education: PhD in Computer Science (Case Western Reserve University, 2008), B.S./M.S. in Mathematics (Huazhong University of Science and Technology, 2002). Research Interests: Distributed systems, mobile computing, smart sensing, wireless networks, IoT, and edge computing. Ongoing projects include mobile/wireless applications, data science in IoT, and edge computing platforms. His work emphasizes practical implementations such as DriverSonar for driving safety and SmileAuth for biometric authentication. Key Achievements: Elected Member of Academia Europaea (2022), Fellow of AAIA, IET, and BCS. Notable awards include the Wu Wenjun Science and Technology Award (2020) and multiple best paper recognitions. Over 100+ publications in top venues like ACM MobiCom, IEEE/ACM Transactions. Professional Contributions: Editorial roles across 8+ journals including IEEE Transactions on Mobile Computing and ACM Transactions on Sensor Networks. Conference leadership includes co-founding ACM TURC and EAI ICECI. Active in technical committees for INFOCOM, MOBIHOC, and ICDCS. Labs/Teams: Leads research groups focused on networking, IoT, and edge computing. Current openings for PhD/MSc students and PostDoc researchers with strong mathematical and systems backgrounds.

Thorsten SchummView profile

Associate Professor

• Quantum Metrology
• Precision Measurements
• Nuclear Physics
• +4 more

Thorsten Schumm - Academic Overview Thorsten Schumm is an Associate Professor at Vienna University of Technology (TU Wien), leading the Quantum Metrology research group within the Atomic Institute. He is a key member of the Erwin Schrödinger Center for Quantum Science & Technology (ESQ) and the Vienna Center for Quantum Science and Technology (VCQ). His research focuses on developing novel quantum measurement techniques, particularly nuclear clocks using thorium-229 isotopes and matter-wave interferometry with collective many-body states. Key Affiliations & Roles Associate Professor, TU Wien (since 201X) ERC Synergy Grant recipient (2019) for the 'Thorium Nuclear Clock' project Principal Investigator for EU-funded MoSaiQC network (2019) and AQUclock project (2022) Research Interests His work bridges quantum metrology with nuclear physics , precision spectroscopy , and many-body quantum systems . He pioneers the development of nuclear clocks—next-generation timekeeping devices using nuclear transitions instead of electronic transitions for unprecedented accuracy. Recent breakthroughs include direct measurement of the thorium-229 isomer energy and advances in laser-driven nuclear excitation techniques. Notable Achievements 2019 ERC Synergy Grant: Enabled global collaboration toward the world's most precise atomic clock 2022 AQUclock project: TU Wien collaboration with Austrian authorities to build state-of-the-art atomic infrastructure 2019: First experimental determination of thorium-229 isomer energy published in Nature Academic Leadership He has mentored 5 PhD students and hosted 5 postdoctoral researchers. His group actively participates in the Vienna Graduate Program on Complex Quantum Systems (COQUS), training the next generation of quantum scientists.

• Sensor technology
• Electrical measurement technology
• Robotics
• +2 more

Hubert Zangl is a Professor at the University of Klagenfurt and Head of the Institute for Intelligent System Technologies . He serves as Chairman of the Information Technology Curricular Commission and participates in the Faculty Conference of the Faculty of Technical Sciences. Key research areas include: Sensor technology Electrical measurement technology Robotics Signal processing Electronics Recent research trends focus on: High-fidelity FMCW radar simulation frameworks Energy-efficient sensor systems Printed electronics for structural health monitoring Uncertainty propagation in measurement science Modular robotics with secure transducer identification Capacitive tactile sensing for robotic grasping Contact: Hubert.Zangl@aau.at