Dana S. Nau is a Professor in the Department of Computer Science and a member of the Institute for Systems Research at the University of Maryland. He is renowned for his contributions to automated planning and game theory, including landmark algorithms like SHOP and foundational studies on game-tree pathology and strategic planning in computer bridge. With over 500 refereed publications and an H-index of 61, his work bridges theoretical computer science and practical applications in multiagent systems and evolutionary game theory. His research interests include hierarchical task network (HTN) planning, Bayesian network inference techniques, and the evolution of social norms through evolutionary game theory. Recent work focuses on spatial evolutionary games, surrogate Bayesian models, and strategic communication in multiagent environments. Awards: AAAI Fellow (202?), ACM Fellow (202?) Key Collaborations: Co-authored papers with leaders like Malik Ghallab (LAAS-CNRS), Satyandra K. Gupta (USC), and Vincent Hsiao (Bayesian networks research). Grants/Advising: Supervised students including Sunandita Patra (17+ joint papers) and Ruoxi Li, contributing to HTN planning and reinforcement learning advancements. His labs and research teams actively explore AI planning systems, probabilistic reasoning, and the intersection of game theory with social science phenomena like gossip evolution.
Dr. Sharib Ali is a Lecturer (Assistant Professor) in the School of Computer Science at the University of Leeds, Faculty of Engineering and Physical Sciences. He is affiliated with the Leeds Cancer Research Centre and actively contributes to research in biomedical image analysis and computer vision. His work bridges cutting-edge AI with clinical applications, particularly in endoscopy and surgical technologies. PhD in Medical Image Analysis, University of Lorraine, France MSc in Computer Vision (by research), University of Burgundy, France Dr. Ali's research focuses on biomedical image analysis , computer vision , and machine learning , with applications in early cancer detection , computational endoscopy , and 3D reconstruction . He develops robust algorithms for segmentation, registration, depth estimation, and mosaicking, using both classical mathematical models and deep learning. His work emphasizes translational research and generalisability in real-world clinical settings. The recent publications highlight a strong trend in generalisability assessment , multi-modal data fusion , and AI benchmarking in endoscopy. His work spans from foundational algorithm development to clinical deployment, including federated learning , mixed reality in surgery , and multi-centre datasets , addressing key challenges like bias, data imbalance, and privacy. Dr. Ali has co-supervised multiple DPhil/PhD students and currently supervises several PhD candidates at the University of Leeds, University of Oxford, and Tec de Monterrey. He is actively involved in securing research funding and leading projects such as Leveraging multi-modality data for targeted biopsy and Federated learning in healthcare . He is a founding member of NAAMII, Nepal, where he volunteers to train students from LMICs. He also organizes international research initiatives including the EndoCV and P2ILF challenges at MICCAI, and serves on program committees and as a reviewer for journals like Nature Communications and Medical Image Analysis . His research is conducted within interdisciplinary teams, collaborating with clinicians from Oxford NHS University Hospitals, neuroscientists at Forschungszentrum Jülich, and engineers across Europe. He leads the development of open tools and datasets to advance the field of endoscopic computer vision.
Maurizio Zamboni is a Full Professor at the Department of Electronics and Telecommunications (DET) at the Polytechnic University of Turin, where he also serves as Student Ombudsman. His academic career spans over three decades with continuous teaching and research contributions in electronics and computing fields. Professor Zamboni's research interests focus on cutting-edge areas including CMOS integrated circuits, computer architecture, quantum computing, semiconductor devices, and VLSI design. His work particularly emphasizes emerging nanotechnologies for digital microelectronic architectures and the design of high-performance or low-consumption processing systems. He has developed expertise in circuit architectures for probabilistic computing, logic-in-memory computing, magnetic devices, and quantum architectures. His recent publications (2021-2025) reveal a strong trend toward quantum computing applications, in-memory processing architectures, and novel approaches to overcoming the memory wall problem. These works span both theoretical algorithm development and practical hardware implementations, with significant focus on quantum annealing, FPGA-based quantum emulation, and memory-mapped processing architectures. Professor Zamboni has been actively supervising PhD students working on quantum computing algorithms, hardware AI accelerators for automotive applications, and quantum-related optimization approaches. He leads research within the VLSILAB Group at DET, focusing on the intersection of nanoelectronics, quantum computing, and advanced computer architectures. His work bridges theoretical computer science with practical electronic design, creating novel solutions for next-generation computing challenges.
Christopher Bishop is a Microsoft Technical Fellow and Director of Microsoft Research AI for Science, concurrently serving as Honorary Professor of Computer Science at the University of Edinburgh and Fellow of Darwin College, Cambridge. His distinguished career spans theoretical physics, neural computing, and leadership in AI research. Fellow of the Royal Academy of Engineering (2004) Fellow of the Royal Society of Edinburgh (2007) Fellow of the Royal Society (2017) Founding member of UK AI Council Member of Prime Minister's Council for Science and Technology (2019) Delivered Royal Institution Christmas Lectures (2008) His research focuses on probabilistic models and machine learning, with significant contributions to AI for scientific discovery. Bishop pioneered the concept of the fifth paradigm of scientific discovery , where AI transforms traditional research methodologies across natural sciences. His work bridges theoretical computer science with practical applications in fusion energy, materials science, and computational biology. Analysis of his recent publications reveals a strategic shift toward AI-driven scientific infrastructure , with emphasis on machine learning foundations that endure technological evolution. His 2024 textbook became Springer Nature's top-selling publication, demonstrating exceptional impact in both academic and industrial contexts. Deep Learning: Foundations and Concepts (2024) Pattern Recognition and Machine Learning (2006) Neural Networks for Pattern Recognition (1995) Bishop leads Microsoft's global AI for Science initiative, establishing research teams in Berlin and coordinating interdisciplinary projects that apply machine learning to climate science, fusion energy, and molecular biology. His leadership in the Prime Minister's Council shapes national AI strategy while maintaining active engagement in public science communication through lectures and media appearances.
Professor George Ghinea is a distinguished academic in the Department of Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. With over 350 publications and 33 successfully supervised PhD students, he leads cutting-edge research at the intersection of computer science, media studies, and psychology. His educational background includes a PhD from the University of Reading (1999) where he pioneered the Quality of Perception (QoP) metric - a precursor to today's widely adopted Quality of Experience (QoE) concept. He holds multiple degrees with distinction from the University of the Witwatersrand in South Africa, including BSc, BSc (Hons), and MSc in Computer Science. Professor Ghinea's research focuses on perceptual multimedia quality and human-centered e-systems, with particular emphasis on mulsemedia (multiple sensorial media) - his own conceptual framework extending multimedia to engage non-traditional senses. His work spans eye-tracking applications, telemedicine, multi-modal interaction, and ubiquitous computing. Current research explores mulsemedia integration in autonomous vehicles, security-enhanced systems, and accessibility solutions. His publications reveal strong trends in multisensory computing (42% of recent works), telemedicine applications (28%), accessibility research (18%), and network optimization (12%). The work consistently bridges theoretical frameworks with practical implementations, often incorporating physiological data and user perception metrics. Distinguished Visiting Fellow of the Royal Academy of Engineering (2018) SPARC DUO-India 2020 Fellowship Programme recipient Principal Investigator for multiple EU Horizon 2020 projects Research featured in major media including BBC, Forbes, and Daily Telegraph Professor Ghinea has secured substantial research funding through projects like the EU H2020 NEWTON initiative, Royal Academy of Engineering partnerships, and multiple Newton Fund collaborations. His supervision portfolio includes 33 PhD completions with diverse research spanning security behavior in Ghana, physiological QoE in VR, smart city adoption in Oman, and sustainable digital transformation in Qatar. He leads the IMUSY research group focusing on mulsemedia systems and human perception. His laboratory work centers on the IMUSY research group where they develop mulsemedia applications integrating thermal, wind, and olfactory devices for enhanced user experiences. Current team projects include mulsemedia in autonomous vehicles (MulsEAV), physiological data for QoE assessment, and smart city adoption studies.
Dr. Mingfeng Wang is a Senior Lecturer in Robotics and Autonomous Systems at Brunel University London, affiliated with the Department of Mechanical and Aerospace Engineering within the College of Engineering, Design and Physical Sciences. His research focuses on specialized robotic systems including continuum, legged, soft, precision farming, and miniaturized robots. Chartered Engineer (CEng) with Engineering Council UK Fellow of the Higher Education Academy (FHEA) Member of IEEE, IEEE-RAS, IMechE, and IFToMM Editorial roles: Associate Editor of International Journal of Advanced Robotic Systems (JCR-Q3); Associate Editor of Frontiers in Robotics and AI (JCR-Q2); Editor of Information Processing in Agriculture (JCR-Q1), Biomimetic Intelligence and Robotics (JCR-Q1), and STEM Education Research expertise includes: Continuum Robotics : Design of extra-slender continuum robots (diameter-to-length ratio Legged Robotics : Parallel mechanism-based biped and hexapod robots for extreme environments Miniaturized Robotics : Active locomotion and drug delivery in capsule endoscopes Soft Robotics : Compliant end-effectors and bio-inspired designs Precision Farming : Laser weeding systems and agricultural automation Key scientific awards: BRIEF award (2022) TAROS Best Paper Post Nomination (2022) IFToMM Asian-MMS Best Paper Award (2014) Recent publications focus on: Cochlear implant surgery robotics Passive compliance in train fluid servicing Snake-biomimetic sealing surfaces Parallel kinematic manipulators Capsule endoscope image enhancement Professional services include conference organization (TAROS 2023/2024 Steering Committee; TAROS 2024 Programme Chair) and journal refereeing for IEEE-ASME Transactions on Mechatronics and Scientific Reports.
Ghyslain Gagnon is a Professor in the Department of Electrical Engineering at École de technologie supérieure (ÉTS) in Montreal, Canada. He leads research activities within the LACIME – Communications and Microelectronic Integration Laboratory, focusing on cutting-edge developments in microelectronics, sensors, and communication systems. His work bridges theoretical research and practical applications across multiple domains including health technologies, wireless communications, and quantum engineering. Education: B.Ing. from École de technologie supérieure M.Ing. from École de technologie supérieure Ph.D. from Université de Carleton Professor Gagnon's research spans several interconnected domains with emphasis on Radiofrequency circuits and antennas, Microelectronics, Wireless communications, Sensors and monitoring systems, Machine learning applications, Health technologies, and Quantum engineering. His work demonstrates a strong commitment to translating theoretical concepts into practical solutions with real-world impact, particularly in the areas of health monitoring systems and advanced communication technologies. His recent publications reveal a clear trajectory toward increasingly interdisciplinary research, combining traditional electrical engineering with machine learning, health monitoring, and quantum technologies. The trend shows growing emphasis on practical applications in automotive safety systems, wireless communications for next-generation networks, and health monitoring technologies that leverage flexible electronics and novel sensor designs. Professor Gagnon has successfully supervised numerous graduate students through their doctoral and master's research, with recent theses focusing on smart hearing protection devices, machine learning applications, energy monitoring systems, and flexible sensor technologies. His supervision record demonstrates consistent productivity and relevance to contemporary engineering challenges. He is an active member of the LACIME research laboratory, which focuses on six key areas: Functional materials, Micro- and nanofabrication processes, Conception and design of integrated circuits, Design and fabrication of hybrid components, Photonic and electronic microsystems, and Signal processing and communication. This environment provides students with access to cutting-edge tools and fosters innovation through interdisciplinary collaboration.
Thilo Stadelmann is the Founding Director of the Centre for Artificial Intelligence at the Zurich University of Applied Sciences (ZHAW) . A computer scientist by training, he earned his Doctor of Science degree from Marburg University, Germany, and has held engineering and leadership roles in the automotive industry before transitioning to academia. His research interests lie at the intersection of representation learning and the societal implications of artificial intelligence . He is particularly focused on understanding how AI systems can be designed to enhance human capabilities while addressing ethical concerns and societal challenges. Stadelmann is a prolific speaker and educator, delivering TEDx talks and lectures on topics such as "How Not to Fear AI" , "AI vs Human: Understanding the Fundamental Differences" , and "Decoding AI Fear: The Philosophy Behind It" . His work emphasizes the importance of demystifying AI and fostering a balanced perspective on its potential and limitations. His recent publications span a wide range of AI applications, from safety-critical network infrastructures and medical imaging to industrial process control and AI governance . Notable works include studies on AI risk assessment for public policy, document recognition, and the societal impact of AI technologies. Beyond his academic role, Stadelmann is actively involved in the digital ecosystem as a (co-)founder and senior leader in several organizations, bridging the gap between research and practical implementation in the AI space.
Professor David Xu is a distinguished faculty member in the Department of Information Systems at City University of Hong Kong, where he has served as Professor since 2023 after progressing from Associate Professor (2017-2023). Prior to joining CityU, he held academic positions at Wichita State University from 2011-2017, culminating in the Bomhoff Endowed Professor of Business title in 2017. His educational background includes a PhD in Management Information Systems from the University of British Columbia (2011), an MPhil in Information Systems from City University of Hong Kong, and a First-class honors BBA in Business Administration from Lingnan University. As Programme Leader for both BBA Information Management and Bachelor's Degree in Information Systems programs since 2018, he plays a significant administrative role in curriculum development. Professor Xu's research spans human-computer interaction, artificial intelligence applications, and technology adoption across diverse domains. With over 90 publications including 40+ journal papers in top-tier venues like MIS Quarterly and Information Systems Research, his work demonstrates exceptional scholarly impact. His Google Scholar metrics (3,300+ citations, h-index of 22) reflect substantial influence in the field. His recent publications reveal a strategic focus on AI ethics, information cocoon mitigation in social media, healthcare applications of AI, and digital transformation effects on business. The work spans theoretical contributions and practical implementations, with increasing emphasis on societal impacts of technology. AIS Early Career Award (2018) AIS Distinguished Member – Cum Laude (2020) Multiple teaching excellence awards (2021, 2024, 2025) ICIS and ISR Best Associate Editor Awards (2020-2021) Numerous paper award nominations across major conferences Professor Xu has secured significant research funding including NSFC, RGC GRF, and CityU Strategic Research Grants totaling millions in funding. His current projects address critical issues like AI beauty filters, depression treatment systems, and information cocoon mitigation. As Senior Editor for Information Systems Journal and Associate Editor for Information Systems Research, he shapes the field's scholarly discourse while supervising DBA and PhD students across multiple programs.
Luis Castedo Ribas is a Professor at the Faculty of Informatics , University of A Coruña (UDC) , Spain, since 2001. Previously held research positions at the University of Southern California (USC) and École supérieure d'électricité (SUPELEC). PhD in Telecommunications Engineering (1993), Technical University of Madrid Department of Computer Engineering Research Group: Electronic and Communications Technology Group Research Interests: Specializing in Signal Processing and Information Theory for Wireless Communications Engineering , with focus on MIMO Communication Systems , 5G Radio Interfaces , Joint Source-Channel Coding , and High-Speed Wireless Communications . His work bridges theoretical advancements with practical prototyping of digital communication systems. Recent Article Trends: Publications span Massive MIMO , Beamforming , AI/ML for Wireless , and Quantum-Inspired Coding , reflecting his leadership in evolving telecom standards (5G→6G). Collaborative work with institutions like IEEE and European consortia. Scientific Awards: Best Student Paper (2007, 2013, 2017) General Co-Chair, IEEE Sensor Array Workshop (2014) General Co-Chair, European Signal Processing Conference (2019) Grants & Collaborations: Principal Investigator in over 50 projects funded by Spanish Ministry of Science, EU programs, and companies like Atos Origin. Key roles in national and international research consortia.
Professor Georg Gottwald is a distinguished academic in the School of Mathematics and Statistics at the University of Sydney, where he has been a faculty member since 2002, progressing from Lecturer to his current position as Professor since 2013. He also holds a Visiting Professor position at the University of Surrey in the UK since 2013. His extensive research career spans dynamical systems theory, geophysical fluid dynamics, and the intersection of machine learning with complex systems. Professor Gottwald's research focuses on dynamical systems theory as an abstract formalism for studying systems evolving in time and space. His work has significant applications across diverse fields including climate modeling, biological systems, and complex networks. He is particularly known for developing methods for model reduction of complex dynamical systems, stochastic modeling approaches, and the application of machine learning techniques to dynamical systems. His research aligns with the Faculty of Science Research Strengths in Understanding the Universe, Fundamental Laws of Nature, Complex Systems, Climate and Environmental Change, Data and Decisions, and National Security. His most recent publications demonstrate a strong trajectory toward integrating machine learning with dynamical systems theory, particularly in developing stable generative models, learning dynamical systems with random feature maps, and combining data assimilation with machine learning for forecasting. His work spans pure mathematical theory to practical applications in climate science, finance, and biological systems, showing remarkable breadth while maintaining deep mathematical rigor. Future Fellowship, 'Stochastic methods in mathematical geophysical fluid dynamics', Australian Research Council, 2010-2014 Australian Research Fellowship, 'Stochastic methods in mathematical geophysical fluid dynamics', Australian Research Council, 2010-2015 (declined) Australian Research Fellowship, 'Geometric methods in geophysical fluid dynamics', Australian Research Council, 2004-2009 Professor Gottwald has successfully supervised numerous PhD and Master's students who have gone on to academic and industry positions worldwide. His current research group includes postdocs and PhD students working on machine learning for dynamical systems, stochastic model reduction, physics-informed machine intelligence, and tensor methods for scientific machine learning. He has secured multiple ARC Discovery Project grants and has been involved in significant international collaborative research projects. He is actively involved with the Sydney Dynamics Group, which he co-founded in 2007, fostering collaboration between the University of Sydney and UNSW. Professor Gottwald maintains strong editorial commitments as Associate Editor for Geophysical and Astrophysical Fluid Dynamics, SIAM Journal of Applied Dynamical Systems, and Journal of Computational Dynamics, and serves on the Editorial Advisory Board for Chaos and the Editorial Board for Physical Review E. His professional activities demonstrate leadership in the dynamical systems community through organizing workshops, seminars, and special journal issues.
Denny Yu is an Associate Professor at the Edwardson School of Industrial Engineering, Purdue University. His work bridges human factors, neuroergonomics, and healthcare safety through advanced sensor systems and AI. Primary Affiliation : Edwardson School of Industrial Engineering, Purdue University Research Themes : Surgical ergonomics, autonomous vehicle human factors, cognitive workload assessment, multimodal physiological sensing Dr. Yu's research focuses on neuroergonomics and human-robot interaction , particularly in surgical and transportation contexts. His team develops sensor-based systems for workload monitoring, including: EEG-eye tracking fusion for situation awareness Wearable exoskeletons for surgical posture support Computer vision tools for lifting task risk analysis Smart infusion pump usability frameworks AI-driven surgical coaching systems Recent publications emphasize deep learning applications in soft tissue deformation estimation and real-time adaptive systems for robotic surgery augmentation. His work spans both occupational health (veterinary surgeons, airport workers) and medical device innovation domains.
Arianna Bisazza is an Associate Professor in the Computational Linguistics Group at the University of Groningen, where she leads the InClow research group focused on Interpretable, Cognitively inspired, Low-resource language models. Her work bridges computational linguistics, cognitive science, and language acquisition to develop more robust and interpretable language processing algorithms that can adapt to diverse linguistic phenomena worldwide. Dr. Bisazza's research interests span statistical modeling of human languages in multilingual contexts, with particular focus on improving language model performance for "challenging" or low-resource languages. Her work explores how insights from human language acquisition can inform better language modeling techniques, and she investigates methods to make state-of-the-art NLP systems more interpretable and transparent. As a cross-disciplinary researcher, she actively seeks to enhance our understanding of human language processing and evolution through computational modeling tools. Her recent publications reveal a strong emphasis on multilingual evaluation frameworks (like TurBLiMP and MultiBLiMP), interpretability of language models, and connections between human language acquisition and neural network learning. Her work consistently addresses the challenge of making language technology more robust across diverse linguistic structures and typological features. Outstanding Paper Award at the BabyLM Challenge (CoNLL'24 Shared Task) for "BabyLM Challenge: Exploring the Effect of Variation Sets on Language Model Training Efficiency" Dr. Bisazza currently leads a Vidi project funded by the Dutch Research Council (NWO) on improving low-resource language modeling through child language acquisition insights. She is also part of two national consortium projects funded by NWA-ORC initiatives: InDeep (Interpreting deep learning models for language, speech & music) and LESSEN (Low Resource Chat-based Conversational Intelligence). She supervises multiple PhD students, including two China Scholarship Council (CSC)-funded researchers working on simulating human patterns of language learning and change. Her earlier research was supported by a Veni grant (2017-2021) focused on understanding and improving the encoding of linguistic structure in Neural Machine Translation models. As head of the InClow research group, Dr. Bisazza oversees a team investigating interpretable, cognitively inspired approaches to low-resource language modeling. The group's work combines insights from cognitive science and linguistics with cutting-edge NLP techniques to develop language models that better reflect human language processing capabilities, particularly in resource-constrained settings.
Yoann Altmann is Professor in the School of Engineering & Physical Sciences at Heriot-Watt University and a member of the Institute of Sensors, Signals & Systems. Since 2024 he holds the Chair in Electrical, Electronic & Computer Engineering (EECE), directing a research programme that bridges statistical signal processing, computational imaging and quantum & neuromorphic sensing. Education & career: 2010 – Eng. degree (Electrical Engineering), ENSEEIHT, Toulouse, France 2010 – M.Sc. (Signal Processing), National Polytechnic Institute of Toulouse 2013 – Ph.D. (Signal & Communications), IRIT Laboratory, Toulouse 2014-2017 – Post-doctoral Research Fellow, Heriot-Watt University 2017 – Royal Academy of Engineering Research Fellow & Assistant Professor, HWU 2024 – promoted to Professor, School of Engineering & Physical Sciences, HWU Research interests: Prof. Altmann develops mathematical and algorithmic tools for Bayesian inverse problems, with emphasis on single-photon LiDAR, low-illumination imaging, neuromorphic computational sensing, variational inference and sparse reconstruction. His work combines principled statistical modelling with efficient computational schemes to enable imaging in extreme scenarios such as underwater scattering, photon-starved environments, quantum metrology and real-time 3-D scene reconstruction. Publication trends: Across 160 outputs (2011-2025) his recent articles reveal a clear trajectory toward integrating modern machine-learning paradigms—variational autoencoders, diffusion generative models, spiking neural networks—with rigorous physics-based forward models. Applications span quantum parameter estimation, multimode-fiber endoscopy, hyperspectral & Compton imaging, nuclear safeguards and cultural-heritage spectroscopy, demonstrating both methodological breadth and high-impact interdisciplinary deployment. Honours & recognition: Royal Academy of Engineering Research Fellowship – competitively awarded (2017) Grants & datasets: He has generated four open datasets supporting reproducible research in quantum sensing, variational autoencoders, underwater single-photon LiDAR and multispectral fluorescence imaging, reflecting sustained funding and commitment to open science. Continuous peer-review service for IEEE and Elsevier journals since 2013 underlines his standing within the signal-processing community. Labs & teams: He leads the Bayesian Imaging & Sensing Computing (BISC) group ( https://bisc.site.hw.ac.uk ) which hosts post-docs, PhD researchers and international visitors working on statistical machine-learning for imaging, sensing and quantum technologies.
Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.
