Dr. Li Baowen is Chair Professor at Southern University of Science and Technology (SUSTech) with joint appointments in the Department of Physics and Department of Materials Science and Engineering. A pioneer in phononics and thermal metamaterials, he previously held endowed professorships at University of Colorado Boulder and UC Berkeley. His research focuses on controlling heat transfer at nano scales, developing thermal metamaterials like thermal cloaks, and applying complex networks to physical systems. Dr. Li has published over 400 papers including 3 in Reviews of Modern Physics and 30 in Physical Review Letters, with more than 34,800 citations (H-index 98). He is recognized with the Brillouin Medal from the International Phononics Society and is a Fellow of the American Physical Society. He founded research centers including the China-EU Joint Lab for Nanophononics at Tongji University. His current research explores quantum phononics, machine learning applications in thermal materials, phonon lasers, and quantum sensing technologies.
Matteo Maffei is a Full Professor at TU Wien, leading the Security and Privacy group. He joined in 2017 after 11 years at Saarland University's CISPA. He holds a Ph.D. in Computer Science from Ca’ Foscari University of Venice (2006). He coordinates the TU Wien Cybersecurity Center, the SecInt Doctoral School, and the FWF Special Research Program SPyCoDe. His research focuses on formal methods for security and privacy, blockchain technologies, and web security. Roles: Full Professor, Coordinator of TU Wien Cybersecurity Center, Module Head of Christian Doppler Lab for Blockchain Technologies (CDL-BOT), Board Member of Vienna Cybersecurity and Privacy Research Cluster (ViSP). His work emphasizes formal verification of cryptographic protocols, smart contracts, and decentralized systems. Key achievements include ERC Advanced (2024) and Consolidator (2018) Grants, and leadership roles in conferences like IEEE Computer Security Foundations Symposium (CSF). Research Interests: Formal methods, smart contracts, blockchain scalability, web security, privacy-preserving protocols, and decentralized systems. His recent projects include optimizing Lightning Network channels, secure multi-hop payments, and AI-driven robustness verification. Publications: Over 200 publications in top venues like CCS, IEEE S&P, and CRYPTO. Recent work includes advancements in blockchain interoperability (e.g., Alba bridges), light client protocols (Blink), and neural network verification techniques. Grants & Awards: ERC Advanced Grant (2024), ERC Consolidator Grant (2018), DFG Emmy Noether Fellowship (2009). Led projects funded by EU Horizon, FWF, and industry partners like ABC Research GmbH. Advising & Labs: Supervised over 20 PhD/Master theses. Active in the Christian Doppler Lab for Blockchain Technologies and the SPyCoDe SFB. Collaborates with institutions like SBA Research and Stanford University.
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.
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.
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 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.
Zhao Guoying is an Academy Professor at the Academy of Finland and holds a tenured Full Professorship at the University of Oulu, Finland. His research focuses on human behavior understanding, emotion AI, and computer vision. He has held visiting positions at institutions including Stanford University and Aalto University. He earned his PhD (2005) in Computer Science from the Chinese Academy of Sciences. His work has led to pioneering contributions in facial expression analysis, micro-expression recognition, and remote physiological signal measurement. Zhao has secured over €19.8 million in research grants as PI, including the prestigious Academy Professor Grant (2021-2026) and Profi-7 Hybrid Intelligence funding. He has supervised 22+ PhD students and 16+ postdocs, many of whom hold academic and industry leadership roles. His awards include IEEE Fellow (2022), IAPR Fellow (2020), and Finland’s Most Publishing AI Researcher (2017). His research interests span machine learning, affective computing, and feature representation. Notable contributions include the first systems for spontaneous micro-expression analysis, novel methods for face anti-spoofing, and remote health monitoring via video. He actively organizes conferences (e.g., Arctic AI Days) and chairs committees such as the Finnish AI Society board.
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.
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.
Adam Jatowt is a Professor and Head of the Data Science group at the Department of Computer Science, University of Innsbruck. He also serves as Deputy Head of the Digital Science Center and Research Center for Digital Humanities. His academic career spans roles at Kyoto University (2010-2020), National Institute of Advanced Industrial Science and Technology (AIST), and visiting positions at Karlsruhe Institute of Technology, University of La Rochelle, and University of California Berkeley. Research interests focus on temporal aspects of NLP/IR, computational history, large language models, and future forecasting. He leads projects combining digital humanities with advanced AI techniques, including temporal validity assessment and hint generation systems. Recent publications (2025) emphasize LLM applications in temporal analysis, QA systems, and energy sector digitalization. His work has been recognized through awards like the Friedrich Wilhelm Bessel Research Award (2024) and top-cited paper distinction in Information Sciences. He actively organizes conferences like ECIR 2026 and Text2Story workshops. Key contributions include developing WikiHint dataset, PlausibleQA framework, and tools like Rankify. His research also addresses societal challenges through ESG rating prediction and medical LLM applications.
Dr. Borivoje Dakic is an Associate Professor at the University of Vienna , affiliated with the Faculty of Physics and the Quantum Optics, Quantum Nanophysics and Quantum Information department. His research spans foundational and applied aspects of quantum theory. Operational reconstruction of quantum formalism Quantum interference as a resource for communication Tomography of large-scale quantum systems Macroscopic quantum phenomena His work includes scalable verification techniques for quantum devices and collaborations with experimental teams like Philip Walther’s and Markus Aspelmeyer’s groups. He received the Marko Jarić Prize (2025) for his contributions. Recent projects focus on diagnostics of quantum devices (FWF BeyondC SFB), information-theoretic foundations of quantum interference (FWF P36994), and local operations in quantum field theory (Cluster of Excellence QuantA). His research on quantum coherence in networks and macroscopic entanglement challenges traditional assumptions about quantum-classical boundaries. Publications emphasize resource-efficient tomography, device-independent verification, and foundational frameworks for quantum statistics and field theory. Teaching: Quantum Information (2025W), Theory in Quantum Optics (2025S), VCQ Summerschool Labs: Dakić Group at University of Vienna
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.
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.
Professor Moncef Gabbouj is a distinguished academic and researcher currently serving as Professor of Signal Processing at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Finland. Previously, he held the same position at Tampere University of Technology before the merger in 2019. He has also held visiting professorships at prestigious institutions including Hong Kong University of Technology and Science, University of Southern California, and Purdue University. Ph.D. and MSc. in Electrical Engineering from Purdue University, USA (1989 and 1986) B.Sc. in Electrical Engineering from Oklahoma State University, USA (1985) Prof. Gabbouj's research spans multiple domains within signal and image processing, with a strong focus on machine learning applications. His primary research interests include artificial intelligence, machine learning, Big Data analytics, multimedia content-based analysis, indexing and retrieval, nonlinear signal and image processing, voice conversion, and video processing and coding. His work bridges theoretical advancements with practical applications across various industries, particularly in multimedia communications and biomedical applications. His extensive publication record demonstrates a clear evolution from traditional signal processing techniques toward more sophisticated machine learning and deep learning approaches. Recent work shows increasing focus on convolutional neural networks for various applications including ECG classification, video processing, financial time-series analysis, and image recognition tasks, reflecting the broader trend in the field toward deep learning methodologies while maintaining strong foundations in signal processing theory. IEEE Fellow (2011) Member, Finnish Academy of Science and Letters (2014) Knight, First Class, of the Order of the White Rose of Finland (2006) Nokia Foundation Recognition Award (2005) Nokia Foundation Visiting Professor Award (2012) Finnish Cultural Foundation for Art and Science Award (2017) TUT Foundation Grand Award (2015) Prof. Gabbouj has supervised 64 doctoral and 72 Master's theses, demonstrating his significant contribution to academic mentoring. His research has been supported by substantial funding, including research grants totaling 8.5 million Euro (2001-2015). He has served as Academy of Finland Professor during 2011-2015 and has been involved in numerous EU research projects including Horizon, ESPRIT, HCM, IST, COST, Tempus and Erasmus programs. As Editor, Guest Editor or member of the Editorial Board of 6 international scientific journals, he has significantly influenced the academic discourse in his field. He leads the Signal Analysis and Machine Intelligence (SAMI) research group at Tampere University and serves as the Finland Site Director of the NSF IUCRC funded Center for Visual and Decision Informatics. His research unit focuses on applying advanced machine learning techniques to solve complex problems in signal processing, computer vision, and multimedia analytics, with applications ranging from healthcare to multimedia communications and financial analysis.
Chris Marone is a Full Professor (Professore Ordinario) at La Sapienza Università di Roma since 2020, with prior roles as Professor of Geophysics at The Pennsylvania State University (2003–2020) and Associate/Assistant Professor at MIT (1997–2000, 1992–1997). His research spans earthquake physics, geomechanics, and rock deformation. Ph.D. in Geophysics from Columbia University (1988) 40+ years of academic experience across multiple institutions His work focuses on frictional mechanics, slow earthquakes, and fault slip behaviors, integrating laboratory experiments and field observations. Recent themes include rate-state friction laws, rock-fluid interactions, and granular mechanics. Key trends in his publications reveal interdisciplinary approaches combining deep learning (2022), high-frequency seismic signatures (2022), and poromechanics (2022) with traditional geophysical methods. His research has dominated fault healing and stress dynamics for decades. ERC Advanced Grant: TECTONIC Louis Néel Medal (European Geosciences Union) Fellow of the American Geophysical Union Paul F. Robertson Award for Breakthrough of the Year Kerr-McGee Career Development Professorship
