Find Academic Supervisors

• Database systems
• Large scale analytics
• Distributed systems
• +3 more

Beng Chin Ooi is a Lee Kong Chian Centennial Professor at the National University of Singapore (NUS), School of Computing (SoC). He holds a B.Sc. (1st Class Honors) and Ph.D. in Computer Science from Monash University. His research focuses on database systems, large-scale analytics, and distributed computing. He has held leadership roles including Dean of School of Computing (2007–2013) and Director of Smart Systems Institute (2011–2021). Key achievements include the Singapore President’s Science Award (2011), ACM Fellow (2011), IEEE Fellow (2009), and multiple best paper awards. His work emphasizes scalable data management, blockchain systems, and healthcare data analytics. Education: Monash University (B.Sc., Ph.D.) Leadership: Dean (SoC), Director (Smart Systems Institute) Awards: Over 15 major honors including ACM SIGMOD E.F. Codd Innovations Award (2020) His research spans distributed databases, big data systems, and innovative applications of blockchain technology. Recent work includes NASI (neural architecture search) and Rafiki (ML-as-a-service).

Katja HoseView profile

Professor

• Knowledge Graphs
• Semantic Web
• Data Management
• +7 more

Katja Hose is a Full Professor of Data Management at TU Wien's DBAI research unit, heading the Data Management and Knowledge-Driven AI Lab. She previously held a Poul Due Jensen Foundation Professorship at Aalborg University. Her research focuses on data and knowledge engineering, including graph databases, knowledge graphs, querying, analytics, and machine learning, with interdisciplinary applications in bioscience, healthcare, and environmental assessment. Education: PhD in Computer Science (Ilmenau University of Technology, 2009), Postdoc at Max Planck Institute for Informatics (2009–2012). Academic roles include Program Co-Chair for ISWC 2024 and EDBT 2023, and editorial board membership at VLDBJ and TGDK. She leads projects like TARGET (health virtual twins) and ARMADA (data management). Research Interests: Knowledge Graphs, Semantic Web, Big Data, Machine Learning, Data Integration, and Provenance Systems. Key contributions include SHACL shape extraction, conversational data analytics, and environmental knowledge graphs. Awards include the 2025 Distinguished Meta-Reviewer Award and 2024 Manfred Paul Award. Advising and Grants: Supervised students including E. Pürmayr (Diploma Thesis 2025). Active in EU projects (TARGET, ARMADA) and grant coordination. Labs/Teams: DMKI Lab at TU Wien, collaborating with interdisciplinary teams in healthcare and environmental science.

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.

• 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

Jiao LichengView profile

Professor

• Artificial Intelligence
• Machine Learning
• Image Understanding
• +6 more

Jiao Licheng is a Distinguished Professor and Doctoral Supervisor at Xidian University, leading the School of Artificial Intelligence and the Department of Computer Science and Technology. He holds prominent roles such as Director of the Key Laboratory of Intelligent Perception and Image Understanding (Ministry of Education) and the International Joint Research Center for Intelligent Perception and Computing. His research focuses on Artificial Intelligence, Deep Learning, Evolutionary Computation, and Remote Sensing, with significant contributions to image understanding and brain-inspired computing. Education: B.E. (1982) from Shanghai Jiao Tong University, M.E. (1984) and Ph.D. (1990) from Xi'an Jiaotong University. Postdoctoral research at Xidian University (1990–1992). Research Interests include AI, Machine Learning, Image Processing, and Big Data Analysis. His work bridges theoretical advancements and practical applications, such as medical imaging, SAR image analysis, and autonomous systems. Recent articles emphasize innovations in remote sensing, deep learning architectures, and evolutionary algorithms. Awards include IEEE Fellow, IET Fellow, and the Wu Wenjun Artificial Intelligence Outstanding Contribution Award. Labs/Teams: Key Lab of Intelligent Perception, International Joint Research Center, and leadership in national innovation bases. Active in academic societies, including editorial roles in IEEE Transactions on Cybernetics and Geoscience and Remote Sensing.

Vladimir KazeevView profile

Assistant Professor

• Numerical Analysis
• Tensor Methods
• Partial Differential Equ to the data-driven solution of PDEs
• +3 more

Vladimir Kazeev is an Assistant Professor at the Faculty of Mathematics, University of Vienna , where he has held a faculty position since 2019. He also held previous academic appointments as a Szegő Assistant Professor at Stanford University (2017–2019), a postdoctoral researcher at the University of Geneva (2015–2017), and research positions at ETH Zurich (2011–2015), Russian Academy of Sciences (2008–2011), and Moscow Institute of Physics and Technology (2009). His research focuses on adaptive, data-driven numerical methods for differential equations, nonlinear low-parametric approximation, and numerical linear algebra. His work intersects computational mathematics, tensor methods, and high-dimensional problem-solving, particularly in the context of partial differential equations (PDEs) and stochastic modeling. The 15 most recent publications reveal a strong emphasis on quantized tensor-structured methods for PDEs, low-rank approximations, and high-dimensional numerical analysis. His research spans theoretical advancements in tensor decomposition, practical applications in chemical reaction networks, and novel discretization techniques for multiscale and degenerate diffusion problems. Scientific awards include the prestigious ETH Medal for outstanding doctoral theses (2016) Russian Academy of Sciences Medal for outstanding student works in mathematics (2011) Advising and teaching activities include supervising Jason Zhu (Stanford, 2019) and Simon Etter (ETH Zurich, 2014), as well as teaching advanced courses in tensor methods, numerical analysis, and PDEs at the University of Vienna, Stanford University, and the University of Geneva. His service to the community includes peer review for 15+ journals and co-organizing minisymposia at SIAM meetings.

Yao LiangzhongView profile

Professor

• Renewable Energy Grid Integration
• Smart Grid
• HVDC Grid Control
• +4 more

Liangzhong Yao is a Professor and Director of the Smart Grid Research Institute at Wuhan University, China. He holds dual roles as Fellow of IEEE and IET, and has served in leadership positions such as Vice President of China Electric Power Research Institute (CEPRI) and Chair of IEC TC122. His expertise spans renewable energy grid integration, HVDC systems, smart grid technologies, and energy storage. Yao has led over 25 years of R&D projects with combined funding exceeding GBP 20 million and RMB 90 million, resulting in applied technologies in wind farms and HVDC grids. He has been awarded over 10 national and international accolades, including the IEC 1906 Standard Award and China Science and Technology Innovation Awards. Research focuses include AC-DC hybrid systems, distributed energy resource integration, and high-renewable energy grid operation. He has authored/co-authored 350+ publications, 60+ patents, and 4 books. Yao currently supervises 8 PhD and 10 Master's students, and serves on editorial boards of journals like CSEE Journal of Power Energy & Systems. His leadership roles in global standards bodies like IEC and CIGRE further highlight his contributions to advancing electrical engineering standards and practices.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +7 more

Professor Thomas Lukasiewicz is a Full Professor and Head of the Artificial Intelligence Techniques research group at the Faculty of Informatics, Vienna University of Technology (TU Wien). His research focuses on enabling machines to mimic human-like intelligence through techniques spanning deep learning, symbolic reasoning, and predictive coding. Key areas include explainable AI, hybrid neurosymbolic systems, and applications in healthcare and law. He teaches courses such as Deep Learning for Natural Language Processing, Scientific Research and Writing, and multiple seminars in artificial intelligence and knowledge representation. His research projects include Explainable AI in Healthcare (2023–2027) and foundational work on predictive coding networks. His publications (15+ recent articles) address medical image segmentation, neurosymbolic frameworks, and language model evaluation in mathematics. Notable work includes neurosymbolic hybrid models (CCN⁺), reinforcement learning for medical report generation, and theoretical foundations of predictive coding networks.

Xu JinchaoView profile

Professor

• Multigrid methods
• Machine learning
• Finite element methods
• +5 more

Xu Jinchao is a Professor of Applied Mathematics and Computational Sciences at King Abdullah University of Science and Technology (KAUST) and the Verne M. Willaman Professor of Mathematics at Penn State University. He has held distinguished roles, including Director of the Center for Computational Mathematics and Applications at Penn State since 1997 and is an Affiliated Faculty member of the College of Information Sciences and Technology at Penn State. His research focuses on numerical partial differential equations (PDEs), multigrid methods, machine learning, finite element methods, and domain decomposition methods. He is renowned for pioneering contributions such as the Bramble-Pasciak-Xu (BPX) preconditioner, Hiptmair-Xu (HX) preconditioner, Xu-Zikatanov (XZ) identity, and Morley-Wang-Xu (MWX) element. His work bridges computational mathematics and machine learning, including the development of MgNet, which unifies multigrid methods with convolutional neural networks. Xu has been recognized with numerous awards, including Fellowships from SIAM, AMS, AAAS, and the European Academy of Sciences. Notable accolades include the 2008 DOE Top 10 Breakthroughs for his HX preconditioner and the 1995 Feng Kang Prize for Scientific Computing. He has organized over 100 conferences and serves on editorial boards of top journals such as Mathematics of Computations and Numerische Mathematik . His leadership includes directing research centers and advancing computational science through collaborative efforts.

Alan BovikView profile

Professor

• Image quality
• Video compression
• Streaming video
• +7 more

Alan Bovik is the Cockrell Family Regents Endowed Chair Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin's Cockrell School of Engineering. He also holds positions at The Institute for Neurosciences and serves as Director of the Laboratory for Image and Video Engineering (LIVE). With a career spanning over three decades at UT Austin, he has progressed from Assistant Professor (1984-1988) to Associate Professor (1988-1994) to his current position as Full Professor (1994-present). Dr. Bovik received his Ph.D. in Electrical and Computer Engineering in 1984 from the University of Illinois, Urbana-Champaign. Professor Bovik's research focuses on image and video quality assessment, visual perception, and digital media processing. He is renowned for developing groundbreaking algorithms including the Structural Similarity (SSIM) index, Visual Information Fidelity (VIF), and various blind quality assessment models like BRISQUE and NIQE. His work bridges engineering and neuroscience, creating perception-based models that optimize visual media delivery while reducing bandwidth consumption. These innovations have had profound industry impact, with his algorithms processing a significant proportion of global internet video traffic. His recent publications demonstrate continued leadership in perceptual quality assessment, with increasing focus on AI-generated content, high dynamic range (HDR) video, and novel applications in medical imaging. The research shows a clear trajectory toward more sophisticated, neural network-based quality metrics that better align with human visual perception across diverse content types. Professor Bovik has received numerous prestigious awards recognizing his contributions to the field: John Fritz Medal (2024) IEEE Edison Medal (2022) IAMB BaM Award (2022) Elected to the United States National Academy of Engineering (2022) Technology and Engineering Emmy Award (2021) IEEE Fourier Award for Signal Processing (2019) Progress Medal from The Royal Photographic Society (2019) Named Honorary Fellow of The Royal Photographic Society (2019) Primetime Emmy Award (2015) Edwin H. Land Medal from The Optical Society (2017) As Director of the Laboratory for Image and Video Engineering (LIVE), Professor Bovik has secured substantial research funding from organizations including the National Science Foundation and the National Institute for Standards and Technologies. His lab has produced numerous influential datasets including the LIVE Image and Video Quality Databases. He has mentored many successful students who have gone on to make significant contributions in academia and industry, though specific student names are not provided in the source materials. The Laboratory for Image and Video Engineering (LIVE) under Professor Bovik's direction has become a world-renowned center for research in perceptual image and video quality. The lab maintains close collaborations with major technology companies including Netflix, Amazon, and YouTube, ensuring that research has direct practical applications. LIVE has developed numerous influential tools and databases that are widely used in both academic research and industrial applications worldwide.

• Data Mining
• Machine Learning
• Clustering Algorithms
• +6 more

Claudia Plant is a Professor in the Faculty of Computer Science , leading the Research Group Data Mining and Machine Learning . Her research focuses on clustering algorithms, data mining, and machine learning applications in areas like biomedical data, wind energy, and causality inference. She has contributed to projects such as Knowledge-infused Deep Learning for Natural Language Processing (2020–2028) and Hybrid Computational Sciences (2021–2021). Plant has authored over 160 publications, with recent work emphasizing deep learning, anomaly detection, and GPU-optimized algorithms. She actively engages in academic activities, including talks on clustering methods and interdisciplinary projects like Governing Algorithms: The Politics of Data and Decision-Making . Her research interests span clustering algorithms , graph neural networks , causality discovery , and ethical digital transformation . Notable projects include causal analysis of wind farm dynamics and AI-enhanced education tools. Plant’s work bridges computational methods with societal challenges, such as empowering marginalized communities through ethical technology adoption.

Jussi ParikkaView profile

Professor

• Environmental humanities
• Media archaeology
• Media ecology
• +3 more

Jussi Parikka is a Professor in Technological Culture and Aesthetics at the Winchester School of Art, University of Southampton (UK), and a Visiting Professor at FAMU, Academy of Performing Arts in Prague. He holds an adjunct professorship at the University of Turku, Finland. His research spans media archaeology, environmental humanities, and new materialism, focusing on intersections of technology, ecology, and culture. Education: PhD in Cultural History, University of Turku, 2007 Licentiate of Philosophy in Cultural History, University of Turku, 2004 Master of Arts in Cultural History, University of Turku, 2002 Research Interests: Parikka investigates media archaeology, environmental media studies, and digital culture. Key themes include the geology of media, operational images, and the ecological implications of technological systems. His work bridges art, science, and philosophy, addressing urgent issues like climate change and data infrastructures. Awards & Honors: 2021: Elected Member of Academia Europaea 2012: Anne Friedberg Award (SCMS) for Insect Media 2016: Choice Magazine Outstanding Academic Title ( A Geology of Media ) 2017: Moebius Fellowship (Kone Foundation) Grants & Projects: Parikka leads the Digital Aesthetics Research Centre (DARC) and collaborates on initiatives like the Critical Environmental Data project with the Helsinki Biennial. His work integrates art, science, and activism, addressing topics such as environmental sensing and climate justice. Labs/Teams: Director of the Digital Aesthetics Research Centre (DARC), Aarhus University, and co-curator of exhibitions like Weather Engines (Onassis Stegi, Athens).

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.

Sun MaosongView profile

Professor

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +5 more

Prof. Maosong Sun is a Professor at the Department of Computer Science and Technology, Tsinghua University, China. He holds additional leadership roles including Executive Vice Dean of the Institute for Artificial Intelligence and Deputy Director of the National Engineering Laboratory for Cyberlearning and Intelligent Technology. His research focuses on natural language processing (NLP), artificial intelligence, machine learning, and computational education. He leads interdisciplinary projects in computational humanities, knowledge graphs, and MOOC platforms like XuetangX, which has over 58.8 million registered learners. Key contributions include pioneering work in Chinese NLP tools, poetry generation systems like Jiuge, and large-scale research initiatives funded by Chinese and Singaporean programs. Awards include the Tsinghua University Education Award (2019) and the National Outstanding Practitioner Award (2007). Established NLP and Computational Humanities & Social Sciences Lab (2008) Co-director of the Joint Research Center for Extreme Search (2011-present) Over 200 publications with 11,000+ citations (h-index 47)

Celso GrebogiView profile

Professor

• Nonlinear and complex dynamical systems
• Chaos theory
• Fractal geometry
• +5 more

Professor Celso Grebogi, Sixth Century Chair in Nonlinear & Complex Systems at the University of Aberdeen, is a globally recognized leader in nonlinear dynamics , chaos theory , and systems biology . He founded the Institute for Complex Systems and Mathematical Biology and co-founded the Aberdeen-Lanzhou-Tempe Research Centre. His career spans institutions including University of Maryland, University of São Paulo, and Max-Planck-Society (External Scientific Member since 1998).