Dr. Rajkumar Buyya is a Redmond Barry Distinguished Professor at the University of Melbourne and Founder & CEO of Manjrasoft , a spin-off commercializing cloud innovations. He has held visiting roles at Imperial College London , University of Birmingham , and Tsinghua University . Research Interests : His work spans Cloud Computing , Edge Computing , Grid Systems , and Energy-Efficient Computing , focusing on utility-driven resource allocation, simulation tools, and scalable IoT application frameworks. He pioneered the CloudSim toolkit and Aneka Cloud technologies. Scientific Awards : IEEE Fellow (2015), Web of Science Highly Cited Researcher (2016-2021) Khwarizmi International Award (2020), Scopus Researcher of the Year (2017) Frost & Sullivan New Product Innovation Award (2010), IEEE TCSC Medal (2009) Impact : Authored over 850 publications, including the widely adopted textbook Mastering Cloud Computing . Graduated 54 PhD students now in leadership roles at institutions like Newcastle University and companies such as IBM , Google , and Amazon . His research has driven global adoption of cloud/edge technologies in 50+ countries.
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.
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.
Mark Coeckelbergh is a Professor of Philosophy at the University of Vienna, specializing in the philosophy of technology, AI ethics, and robot ethics. He is affiliated with the Department of Philosophy and the Research Network Data Science at the University of Vienna. In addition to his academic position, Coeckelbergh has held significant roles including Former President of the Society for Philosophy and Technology (SPT) and Member of the European Commission's High-Level Expert Group on Artificial Intelligence (AI HLEG). His research focuses on the ethical, political, and philosophical implications of emerging technologies, particularly artificial intelligence and robotics. Coeckelbergh has published extensively in these areas, authoring influential books such as Robot Ethics (2022), The Political Philosophy of AI (2022), and Why AI Undermines Democracy and What To Do About It . His work explores how AI systems affect democratic processes, human agency, and social relationships. Recent publications demonstrate a strong focus on the relationship between AI, democracy, and ethical governance, examining how AI systems can both threaten and potentially enhance democratic processes through algorithmic transparency and participatory design approaches. Finalist of the World Technology Award 2017 Coeckelbergh teaches various courses including 'Introduction to Philosophy of LLMs,' 'LLMs and the Future of Writing,' 'Ethics and Robotics,' and 'Global Governance of AI.' He has supervised numerous students working on topics related to technology ethics and philosophy. He has been involved in several major research projects including H2020 PERSEO, WWTF Democracy Responsible Entrepreneurship, and FP7 DREAM (Development of Robot-Enhanced therapy for children with Autism spectrum disorders). Coeckelbergh has also announced upcoming guest professorships at the Institute of Philosophy of the Czech Academy of Sciences and Uppsala University, where he will work on environmental and technology ethics projects.
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.
Béla Bollobás is a renowned mathematician affiliated with the University of Memphis as the Jabie Hardin Chair of Excellence in Combinatorics and the University of Cambridge as a Fellow of Trinity College and Honorary Professor at the Centre for Mathematical Sciences. His work spans combinatorics, probability theory, and graph theory, with significant contributions to percolation and random graphs. Dr. Rer. Nat. (Budapest, 1967) Ph.D. (Cambridge, 1972) Sc.D. (Cambridge, 1984) Bollobás pioneered extremal graph theory, random graphs, and probabilistic combinatorics. He introduced novel graph polynomials and advanced bootstrap percolation models, impacting both theoretical mathematics and statistical physics. His research includes inhomogeneous random graphs and cellular automata in random environments. His selected publications reveal a focus on percolation thresholds, graph invariants, and stochastic processes. Notably, he derived sharp thresholds for bootstrap percolation and defined critical probabilities for Voronoi percolation. Senior Whitehead Prize (2007) Fellow of the Royal Society (2011) Foreign Member, Hungarian Academy of Sciences (1990) Foreign Member, Polish Academy of Sciences (2013) Honorary Doctorate, Adam Mickiewicz University (2013) Szechenyi Prize (2017) Bollobás has supervised over 50 Ph.D. students and authored over 450 publications, including 10 books. He co-founded the journal Combinatorics, Probability and Computing and served on eight editorial boards. He organized numerous conferences, including Bill Tutte and Paul Erdős events.
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.
Olaf Steinbach is a University Professor (Univ.-Prof.) at the Institute of Applied Mathematics at Graz University of Technology. His academic career spans over three decades with continuous research activity from 1992 to the present, including publications scheduled for 2026. He serves as a project manager for several research initiatives including the Special Research Area (SFB) F90 Computational Electric Machine Laboratory, which runs from 2022 to 2026. Professor Steinbach's research interests primarily focus on Numerical Analysis and Computational Mathematics . His work centers around developing and analyzing advanced numerical methods, particularly Finite Element Methods (FEM) and Boundary Element Methods (BEM), for solving partial differential equations (PDEs) and optimal control problems. His research spans both theoretical aspects (such as error analysis, stability, and convergence) and practical applications (including electric machines, electromagnetics, and biomechanics). He has made significant contributions to space-time finite element methods, which treat time as an additional dimension in the discretization process, leading to more robust and efficient solvers for time-dependent problems. Analysis of his recent publications (2021-2026) reveals a strong focus on optimal control problems governed by partial differential equations, with particular emphasis on elliptic, parabolic, and hyperbolic PDEs. His work demonstrates a consistent pattern of developing robust numerical methods with rigorous error analysis, often incorporating regularization techniques to handle challenging constraints. The applications span computational electromagnetics (particularly electric machines), fluid dynamics, and wave propagation problems. His research increasingly incorporates advanced computational techniques including parallel computing and isogeometric analysis. Professor Steinbach has supervised numerous doctoral students and has been actively involved in organizing academic events, including summer schools on Boundary Element Methods. His collaborative network extends across multiple disciplines and institutions, reflecting the interdisciplinary nature of his work in computational mathematics. His research has been supported through multiple significant projects including DK-W1244 Doctoral Program on Partial Differential Equations, the EU CASOPT project on optimization of industrial devices, and the ongoing Special Research Area on Computational Electric Machine Laboratory. These projects demonstrate his leadership in establishing research frameworks that bridge theoretical mathematics with practical engineering applications. Professor Steinbach maintains an active research group within the Institute of Applied Mathematics, collaborating closely with researchers in computational engineering, electrical engineering, and biomechanics. His work on the Computational Electric Machine Laboratory represents a particularly strong interdisciplinary effort combining mathematical theory with electrical engineering applications.
Joseph Sifakis is a CNRS Research Director and founder of Verimag Laboratory in Grenoble, France. He holds the INRIA-Schneider endowed industrial chair since 2008 and has been instrumental in advancing concurrent systems specification and verification. Education: Electrical Engineering (Technical University of Athens), Computer Science (University of Grenoble) Research interests focus on component-based design , real-time systems , and correct-by-construction techniques . He pioneered the development of the BIP framework and contributed to model checking, a cornerstone of industrial system verification. Recent publications emphasize component-based modeling, formal verification, and distributed system design, reflecting his work's impact on embedded systems and critical applications like aerospace and telecommunications. Scientific awards include: Turing Award (2007) CNRS Silver Medal (2001) Test-of-Time Award (2012) Multiple honorary doctorates (2008-2011) Member of prestigious academies Industry collaborations span Airbus, ST Microelectronics, and the European Space Agency, with applications in aeronautics, telecommunications, and industrial software standards. He leads the ARTIST2 Network of Excellence and directs the CARNOT Institute 'Intelligent Software and Systems'.
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.
Gerald Reiner serves as Head of the Institute for Production Management at the Vienna University of Economics and Business (WU), within the Department of Information Systems and Operations Management. He holds a Magister Degree, doctorate, and Habilitation in Business Administration from WU. His academic career includes positions as full professor in Production Management and Logistics at the University of Neuchatel (Switzerland, 2007-2014) and Universitaet Klagenfurt (Austria, 2014-2018), where he also served as head of the department of Operations, Energy, and Environmental Management. Dr. Reiner has held visiting professorships at Aston Business School (UK), HEC Lausanne (Switzerland), University of Bergamo, and Università Cattolica del Sacro Cuore in Milan (Italy). His research spans several critical areas including Industry 4.0 implementation, integrated capacity and inventory management, humanitarian logistics operations, circular supply chains, and operations management for base of the pyramid contexts. His work particularly focuses on practical applications addressing food waste reduction, sustainable manufacturing, and blockchain technology in supply chains. His publication portfolio demonstrates a clear evolution toward digital transformation in operations, with recent focus on hydrogen production systems, AI integration in manufacturing, and blockchain applications for food supply chain transparency. The research shows increasing emphasis on sustainability integration within traditional operations management frameworks, particularly addressing European manufacturing challenges and food system inefficiencies. Publication Excellence Award 2021 (2023) Researcher of the month (January 2023) Highly Commended paper in the 2017 Emerald Literati Network Awards for Excellence ISIR Service Award (2014) Emerald Outstanding Paper Award (2013) Dr. Reiner coordinates multiple significant international research projects including EU-project 'Keeping Jobs in EU', EU/Ecsel-project 'Power Semiconductor and Electronics Manufacturing 4.0', 'Integrated Development 4.0', and 'Artificial Intelligence in Manufacturing leading to Sustainability and Industry 5.0'. His current projects focus on FOODIS (cross-border ecosystem for innovation in food supply chains), Circular Design implementation, and blockchain applications for banana supply chains. He actively supervises research teams working on food waste reduction, sustainable packaging systems, and AI applications in operations management.
Arthur Lesk is a Professor of Biochemistry and Molecular Biology at Pennsylvania State University since 2003. Previously, he held roles including faculty member at the clinical school of the University of Cambridge (1990–2003), group leader at the European Molecular Biology Laboratory (1987–1990), and professor of chemistry at Fairleigh Dickinson University (1971–1987). He earned a B.A. from Harvard University (1961), Ph.D. from Princeton University (1966), and M.Sc. from the University of Cambridge (1999). His research focuses on bioinformatics, genomics, protein structure, and molecular biology. He has authored 189 scientific articles, 10 books, and has an h-index of 61. Notable works include Protein Science (2021) and Introduction to Bioinformatics (2019). Lesk chairs CODATA’s Biological Macromolecules Task Group and is a Fellow of the AAAS and Royal Society of Biology. He maintains active teaching and research roles, delivering lectures globally. His contributions include advancing protein structure databases and computational methods for molecular biology. Lesk is a Life Member of Clare Hall, Cambridge, and has held visiting positions at universities in New Zealand, Australia, and Europe.
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.
Prof. Wang Cheng-Xiang is a Professor of Wireless Communications at the Institute for Signal and System Processing (ISSS), School of Engineering and Physical Sciences (EPS), Heriot-Watt University, Edinburgh, UK since 2011. His academic journey includes roles as Deputy Head of ISSS (2014-2018), Programme Director for BEng Telecom Engineering (2015-2018), and Director for China Development Group (2006-2018). He holds a PhD from Aalborg University (2004) and prior research experience at Siemens AG and Universities in Germany, Norway, and the UK. His research focuses on applying artificial intelligence to wireless networks, 6G systems, and wireless channel modeling. He has published over 390 papers with an h-index of 57 and 13,460+ citations. Notable recognitions include IEEE/Clarivate Analytics' Highly Cited Researcher (2017-2019) and 11 Best Paper Awards. Prof. Wang serves as Executive Editorial Committee (EEC) Member for IEEE Transactions on Wireless Communications, and has held editorial roles in 10 journals. He actively organizes conferences, serving as chair for events like the IEEE World Congress on Computational Intelligence (2008) and 25th International Conference on Neural Information Processing (2018).
