Matteo Acclavio is an Assistant Professor in Computer Science (Informatics) at the School of Engineering and Informatics, University of Sussex. He holds a PhD in Mathematics from Aix-Marseille University and advanced degrees in Discrete Mathematics and Theoretical Computer Science. PhD in Mathematics (2020), Aix-Marseille University Master in Discrete Mathematics and Foundations of Theoretical Computer Science (2017), Aix-Marseille University Master in Mathematics (2015), Roma Tre University Bachelor in Mathematics (2012), Roma Tre University His research focuses on proof theory and its applications, particularly in linear logic, graphical proof systems, and concurrency. He has contributed to the development of proof-theoretic frameworks for reasoning about concurrent processes and logical time. Recent publications span topics like intuitionistic BV logic, deadlock-freedom in choreographic programming, and generalized connectives in linear logic, reflecting his interdisciplinary approach bridging mathematics and computer science. He teaches courses such as Operating Systems and supervises research in theoretical computer science.
Professor Georgios Leontidis is a Professor of Machine Learning at the University of Aberdeen, where he serves as the Interdisciplinary Institute Director for AI & Big Data and co-Director of the £10.9M UKRI AI CDT SUSTAIN. He is a member of the ELLIS Society and a Turing Academic Liaison, actively shaping national and international AI research agendas. His research expertise lies at the intersection of theoretical and applied machine learning, with key interests in capsule networks, domain adaptation, self-supervised learning, federated learning, and generative AI. His work addresses real-world challenges in environmental monitoring, agrifood sustainability, industrial systems, healthcare, and space science. The recent publications highlight a strong trend in developing novel AI architectures (e.g., OmniNet, Masked Capsule Autoencoders), advancing federated and self-supervised learning methods, and applying AI to sustainability (e.g., greenhouse gas accounting, yield forecasting) and scientific discovery (e.g., lunar radar analysis). His work increasingly integrates ethics, policy, and human-AI interaction, as seen in studies on bias amplification and AI copyright. TMLR - Action Editor ICLR 2025 - Area Chair NeurIPS 2024, 2025 - Area Chair Best Area Chair award, NLDL 2025 Shortlisted for Outstanding Contribution to Accessibility and Inclusivity in Blended Learning (2021) Ranked in Top 4% of EPSRC Full Peer Review College NeurIPS top 10% Reviewer (2020, 2023) Best PhD Paper Award at FISA 2019 (supervised student) Leontidis supervises 14 PhD students and manages 4 research fellows. He leads or co-leads major funded projects from UKRI, EPSRC, EU, and industry partners including Siemens and Tesco, with total funding exceeding £10 million. His collaborative network spans institutions in the UK, Sweden, Switzerland, Greece, Spain, and the Czech Republic. He is actively involved in research infrastructure and policy, serving as External Examiner at Cranfield and Hull Universities, member of the EPSRC Full College, and contributor to the UK AI Council’s Data Working Group. He also co-organized BMVC 2023 and chairs the BMVA Summer School.
Xuqing Wu is an Associate Professor at the University of Houston , affiliated with the College of Technology and the Department of Information Science Technology . He leads research at the MODAL Lab , focusing on machine learning applications in geophysical inversion, electromagnetic modeling, and subsurface characterization. Office: 230E College of Technology Building, Sugar Land Office: BH2 315 Contact: Phone 713-743-0258 | Fax 713-743-4032 Teaching: Courses include CIS 3365 - Database Management and CIS 6397 - Selected Topics His research spans physics-informed deep learning , transformer architectures , and multi-physics joint inversion , with applications in CO2 monitoring , mineral exploration , and solid-state battery materials . Recent work emphasizes self-supervised seismic data enhancement and real-time geosteering processing through FPGA acceleration. Key publication trends show expertise in electromagnetic telemetry , hyperspectral imaging for methane emissions, and stochastic optimization for sensor placement. He frequently employs polynomial chaos expansion , Markov Chain Monte Carlo methods, and collaborative view synchronization .
Xiangyang Xue is a Professor at Fudan University in Shanghai, China, with an extensive research portfolio spanning computer vision, machine learning, and artificial intelligence. His work demonstrates significant contributions to object-centric representation learning, 3D reconstruction, person re-identification, and semantic segmentation. With over two decades of publication history from 1999 to present, he maintains an active research program with numerous collaborations, particularly with researchers like Yanwei Fu, Bin Li, and Yu-Gang Jiang. Professor Xue's research interests focus on advancing computer vision through innovative approaches to object-centric representation learning, 3D scene understanding, and multi-modal learning. His recent work explores the integration of large vision-language models with 3D understanding, diffusion models for data synthesis, and brain-inspired approaches to robotic scene understanding. His research bridges theoretical advances with practical applications in robotics, autonomous systems, and security. Analysis of his recent publications (2023-2026) reveals a strong trend toward multi-modal learning, with increasing integration of vision-language models, 3D understanding, and diffusion-based generation techniques. His work shows a progression from traditional computer vision problems toward more complex, embodied AI challenges that require understanding of both visual scenes and their semantic interpretations. Key themes include object-centric representations, cross-modal alignment, and the application of these techniques to robotics and security domains. Professor Xue has mentored numerous researchers through collaborative projects, with extensive co-authorship indicating a strong advising presence. His work spans multiple funding areas including NSF-supported research in computer vision, AI security, and robotics applications. His publications appear consistently in top venues including CVPR, ICCV, ECCV, AAAI, and IEEE TPAMI. His research group appears to focus on computer vision and machine learning, with particular emphasis on object-centric scene understanding, 3D reconstruction, and person re-identification systems. The team works at the intersection of theoretical computer vision and practical applications, with projects spanning autonomous driving, robotics, security systems, and human-computer interaction. Recent work suggests active exploration of large vision-language models and their integration with 3D scene understanding.
David Martins de Matos is an Associate Professor at Instituto Superior Técnico (IST), Universidade de Lisboa , and a senior researcher at INESC-ID Lisbon within the Human Language Technology Lab . With a career spanning over three decades, he has taught subjects such as Compilers and Object-Oriented Programming since 1993. His research focuses on Natural Language Engineering , Automatic Natural Language Generation , Music Information Retrieval , and Machine Learning Applications in Healthcare . Education: B.Sc. in Electrical and Computer Engineering (IST, 1990) M.Sc. in Electrical and Computer Engineering (IST, 1995) on object-oriented programming in distributed systems Ph.D. in Systems and Computer Science (IST, 2005) on automatic natural language generation Research Interests: His work bridges Natural Language Processing and Computational Music Analysis , with applications in Health Informatics . He investigates semantic frame induction, dialog act recognition, and multimodal systems for chronic pain assessment, Alzheimer's detection, and music generation. His recent articles explore cross-modal retrieval, deep learning for pain narratives, and embodied semantics via fMRI. Scientific Contributions: He has published over 161 works, including 15 recent articles on chronic pain datasets, dialog act recognition, and music-language correlations. His awards include Senior Member status in ACM (SIGMM, SIGIR) and IEEE (Signal Processing Society, Computer Society) , and membership in the Order of Portuguese Engineers . Advising & Collaborations: He has supervised 111 doctoral and master's theses, mentoring students in topics like Visual Story Generation , Music Summarization , and Health Informatics . He collaborates with institutions such as IBM Research , Northwestern University's Feinberg School , and Universidade de Lisboa .
Srinivasa Raghavendra Bhuvan Gummidi is an Assistant Professor at the Department of Green Technology (IGT) within the University of Southern Denmark . His research focuses on Circular Economy , Building Stock Modeling , and Environmental Impact Assessment through advanced Geographical Information Systems (GIS) and Deep Learning techniques. Recent work includes high-precision building material identification and spatiotemporal tracking of urban material stocks. Research Keywords : Algorithms for Geographical Information Systems, Optimization Algorithms, Building Stock Modeling, Circular Economy, Deep Learning, Environmental Impact Assessment His peer-reviewed publications (2025–2015) span topics from urban material sustainability to spatial crowdsourcing systems . He teaches Geographic Information Systems for Engineering Sustainability (2024) and collaborates internationally on urban development and resource management projects.
Hu Cao is a postdoctoral research associate at the Chair of Robotics, Artificial Intelligence and Real-Time Systems (Prof. Alois Knoll) at the Technical University of Munich (TUM) . Holding a Ph.D. from TUM, his research bridges autonomous driving , robotic grasping , medical image analysis , and dense prediction (classification, detection, segmentation). Education : Ph.D. from TUM Hu's work explores: Autonomous Driving : Perception under adverse conditions, multi-sensor fusion, and risk-based safety models Robotic Grasping : Vision-language integration for 6D pose estimation Medical Imaging : Transformer-based segmentation techniques (e.g., Swin-Unet) His recent publications include 15+ works at top venues like CVPR , ICCV , IEEE TPAMI , and IEEE TIV , with 6052+ Google Scholar citations . Notably, Swin-Unet ranks among the top 3 most cited ECCV papers in 5 years, and his work on event-based autonomous driving perception was featured in IEEE Xplore Innovation Spotlight . Editorial roles include: Associate Editor for Visual Intelligence and Frontiers in Neurorobotics Editorial Board member of Artificial Intelligence and Autonomous Systems (AIAS) Topic Editor for Frontiers in Robotics and AI and Frontiers in Neuroscience He has reviewed for 20+ top journals (e.g., Nature Computational Science , IEEE TRO ) and served on program committees for NeurIPS , CVPR , ICCV , and MICCAI .
Nan Bai is an Assistant Professor in the Heritage & Architecture section at Delft University of Technology's Faculty of Architecture and the Built Environment. His research integrates computational social science, architecture, and artificial intelligence to analyze heritage values in urban contexts, focusing on social perceptions derived from social media data. PhD in Heritage and Values from TU Delft Marie Sklodowska-Curie Early Stage Researcher in the HERILAND Project Research Interests : Computational social science, cultural heritage analytics, spatiotemporal modeling, and social media-driven urban planning. His work bridges architecture, AI, and big data to address heritage preservation challenges. Scientific Awards : Best Paper Award from CIPA 2023 Young CAADRIA Award 2020 External Roles : Active in committees like ICOMOS Nederland, CIPA Emerging Professionals, and CIPA Heritage Documentation. He has presented at international conferences and workshops on heritage and AI topics.
Dr Stephan Dahm is a researcher at the Department of General Psychology I, Faculty of Psychology and Sport Science, University of Innsbruck, Austria. He currently leads two funded projects: an Austrian Academy of Sciences (ÖAW) project on Action representations and automatization after action imagery practice and an Austrian Science Fund (FWF) project on The measurement of action imagery ability . Research interests Mechanisms of motor control underlying imagined and executed movements Motor imagery and assessment of action imagery ability Mental practice and motor learning Intermanual transfer of learning Implicit sequence learning Bimanual coordination Experimental psychology His work explores how cognitive constraints shape motor imagery, how imagery practice can automatize stimulus-response associations, and how sequence knowledge transfers between hands. A central theme is the comparison between imagined and executed actions using behavioural paradigms and chronometric measures. Key publications Across more than 30 peer-reviewed articles (2015-2025), Dahm has advanced understanding of motor imagery timing, effector independence, and the role of internal forward models in detecting errors during imagery. His recent work in Psychological Research and Human Movement Science demonstrates that imagery practice can induce robust learning and transfer effects comparable to physical practice. Funding & projects FWF Project P 36142-B: The measurement of action imagery ability ÖAW Project: Action representations and automatization after action imagery practice Advising & collaboration Dr Dahm welcomes inquiries from master’s students interested in experimental sport psychology or his specific research themes. He maintains active collaboration networks evidenced by international co-authored papers and open-science resources such as OpenSesame implementations. Labs & affiliations He is based in Room 2S14 (2nd floor) at University of Innsbruck, Universitätsstraße 5–7, and can be reached at Stephan.Dahm@uibk.ac.at .
Vladimír Marko is an Associate Professor at the Department of Logic and Methodology of Sciences , Faculty of Arts, Comenius University (Bratislava), with a focus on temporal logics and history of logic . He has been affiliated with Comenius University since 1997 and holds a habilitation in systematic philosophy since 2020. Education: Master of Philosophy, University of Belgrade (1986); PhD, Comenius University (1997) Research Interests: Determinism, modal logic, ancient philosophical arguments, semantics of conditionals Projects: 2022–2026 APVV (Semantics of Conditionals), 2023–2025 VEGA (Idealization and Abstraction in Normative and Empirical Disciplines) Teaching: Courses on logic, methodology of research, and philosophy of time He has authored over 20 papers and 3 books, including Štyri antické argumenty o budúcich náhodnostiach (2017). His work intersects classical philosophy and formal logic, addressing issues like the Lazy Argument and Cicero's Fabius Argument.
Professor Panagiotis Demestichas serves as a faculty member in the Department of Digital Systems at the University of Piraeus, where he has been a Professor since April 2012. He heads the Laboratory of "Telecommunication Networks and Integrated Services" and has held significant leadership positions including Chair of the Department of Digital Systems from 2011 to 2015. His academic journey began with Bachelor's and Doctoral degrees in Electrical Engineering from the National Technical University of Athens. Professor Demestichas' educational background includes: Bachelor's Degree in Electrical Engineering, National Technical University of Athens Doctoral Degree in Electrical Engineering, National Technical University of Athens His research spans the forefront of telecommunications and network technologies, with particular expertise in 5G and emerging 6G systems. Professor Demestichas focuses on smart/cognitive/autonomic management and convergence of ICT infrastructures, SDN/NFV technologies, cognitive radio networks, and cloud and Internet of Things solutions. His work addresses critical challenges in spectrum management, network architecture design for beyond 5G systems, and the integration of artificial intelligence into network management frameworks. His research has significant implications for vertical industries including transportation, manufacturing, and smart cities, where reliable high-speed connectivity is essential. Professor Demestichas' publication record demonstrates a consistent focus on next-generation network technologies, with recent work emphasizing 6G architecture, sustainable network design, and industry-specific applications of advanced telecommunications. His research trajectory shows a clear evolution from 5G foundational work toward pioneering 6G concepts, with increasing emphasis on AI integration, sustainability, and cross-industry applications. The collaborative nature of his research is evident through participation in major European projects. Throughout his career, Professor Demestichas has held leadership positions in numerous significant research initiatives including: Project Coordinator of the OneFIT project (2010-2012) Technical Manager of the E3 project (2008-2009) Chairman of WWRF working groups, most notably the WGC "Communication Architectures and Technologies" (2004-2015) Technical Programme Committee Chair for the European Conference on Networks and Communications (EUCNC 2016) Active participation in European research programs including RACE II, ACTS, BRITE/EURAM, EURET, IST/FP5, IST/FP6, and ICT/FP7 As an educator, Professor Demestichas has made substantial contributions to academic development. He has supervised ten completed PhD theses and currently guides three additional doctoral candidates. He teaches Computer Networks I & II at the undergraduate level and has contributed to the development of research capacity through his leadership roles. His laboratory's research activities are partly funded by the European Union under Horizon 2020, reflecting the significance and impact of his work in the international research community. Professor Demestichas leads the Laboratory "Telecommunication Networks and Integrated Services" (http://tns.ds.unipi.gr), which serves as a hub for advanced research in telecommunications. The laboratory focuses on cutting-edge projects related to 5G/6G technologies, network virtualization, and intelligent network management. Through this laboratory, Professor Demestichas fosters collaboration between academia and industry, particularly in the areas of vertical industry applications of advanced networking technologies.
Prof. PhD Anastasia Nicheva Petrova is a distinguished Professor in the Department of General Linguistics and Old Bulgarian Studies at the Faculty of Modern Languages, University of Veliko Tarnovo ("St. Cyril and St. Methodius" University). With an extensive publication record spanning over three decades, she has established herself as a leading expert in Balkan linguistics, phraseology, and linguistic-cultural studies. Her academic career demonstrates deep engagement with the complex linguistic landscape of the Balkan region, examining both historical and contemporary aspects of language contact and convergence. Prof. Petrova's research interests span multiple dimensions of linguistic inquiry, with particular emphasis on the Balkan linguistic union, phraseology, and the intersection of language with culture and cognition. Her work explores how linguistic structures reflect cultural models, examining everything from semantic fields to the multimodal nature of perception and expression. She has made significant contributions to understanding how Slavic lexical elements have been incorporated into other Balkan languages, the phonetic motivation behind phraseological units, and the mythological programming of everyday language. Her research bridges theoretical linguistics with cultural anthropology, creating a comprehensive framework for understanding the Balkan linguistic space as both a historical phenomenon and a living, evolving system. The analysis of Prof. Petrova's 15 most recent publications (2019-2025) reveals a consistent focus on the intricate relationships between language, culture, and cognition in the Balkan context. Her work demonstrates a sophisticated methodological approach that combines comparative analysis with cultural interpretation. A notable trend is her increasing attention to multimodal aspects of language, examining how perception, emotion, and cultural concepts are linguistically encoded. Her research shows a clear evolution from traditional comparative linguistics toward more integrated approaches that incorporate cognitive science, anthropology, and cultural studies. Prof. Petrova has been actively involved in numerous research projects that strengthen international academic collaboration. She has participated in projects focused on Balkan linguistic and cultural symbiosis, digital humanities, and the development of academic networks across Southeastern Europe. Her work with international teams from universities in Nis, Warsaw, and Craiova demonstrates her commitment to building sustainable academic partnerships that transcend national boundaries. She has also contributed to projects aimed at enhancing doctoral education and research, helping to establish platforms for young researchers to showcase their work.
Boryana Emiliyanova Mihailova is a Senior Lecturer at the University of Veliko Tarnovo , affiliated with the Department of General Linguistics and Old Bulgarian Studies . Her research focuses on Romanian language, onomastics, and metaphorical expressions in Balkan folk geographical terminology. University of Veliko Tarnovo: Senior Lecturer (Department of General Linguistics and Old Bulgarian Studies) Research Interests : Comparative analysis of Bulgarian and Romanian linguistic elements Metaphorical models in folk geographical terms Toponymy and anthroponymy in the Danubian region Historical and cultural linguistic patterns in Balkan languages Language education and cross-border linguistic networks Project Participation : "Thematic Research Perspectives through International Academic Networks" (2025): Collaborative project with universities in Niš, Warsaw, and Craiova "International Academic Networks – Traditions and Innovations" (2024): Research and educational exchange initiatives
Maurice Fallon is a Professor of Engineering Science at the University of Oxford and a Royal Society University Research Fellow, leading the Dynamic Robot Systems Group (Perception) at the Oxford Robotics Institute. His research focuses on robust probabilistic methods for localization and mapping in challenging environments through advanced sensor fusion. Education: Electronic Engineering, University College Dublin PhD in Acoustic Source Tracking, University of Cambridge Research Interests: Dr. Fallon specializes in probabilistic state estimation , legged robot navigation , and dynamic motion planning for autonomous systems operating in vision-denied or complex natural environments. His work emphasizes robustness through multi-sensor integration , with applications spanning disaster response, forestry, and industrial inspection. Key innovations include terrain-aware locomotion and long-term autonomy frameworks. Publication Trends: Recent work (2024-2025) demonstrates a strategic shift toward forest robotics and long-term industrial inspection , leveraging legged and aerial platforms. There is strong emphasis on vision foundation models for place recognition, scalable 3D reconstruction using neural radiance fields, and open-vocabulary scene understanding . The research consistently addresses real-world challenges like lighting variations, sensor dropout, and environmental dynamics. Scientific Awards: Royal Society University Research Fellowship 4x Best Paper Awards at ICRA Nominations at Intelligent Vehicles, AAAI, and Humanoids conferences Advising and Grants: Dr. Fallon has secured major funding as PI/Co-I for EU/UK projects including ORCA, RAIN, THING, MEMMO, and the DARPA SubT-winning CERBERUS team. Current initiatives include the Horizon Europe DigiForest project and UKAEA collaborations. He mentors PhD students and postdocs in robotics systems development, though specific advisees aren't listed in source materials. Labs and Teams: He directs the Dynamic Robot Systems Group, which achieved global recognition through DARPA Robotics Challenge participation and SubT Challenge victory. The team operates specialized facilities for legged robot testing and maintains partnerships with nuclear energy and forestry sectors for field deployment.
Dorota Molin is a Lecturer in Classical Hebrew Language at the Faculty of Asian and Middle Eastern Studies , University of Oxford. She specializes in Biblical Hebrew, Neo-Aramaic, language contact, word order typology, and tense-aspect-modal systems. Research Focus : Biblical Hebrew pronunciation traditions, Neo-Aramaic dialects, and cross-linguistic interactions between Neo-Aramaic/Kurdish and Modern Hebrew/Palestinian Arabic. Current Projects : Linguistic history of Near Eastern minorities (ERC-funded), collaborative work on a Neo-Aramaic database, and comparative grammar studies. Courses Taught : Beginner Biblical Hebrew and introductory texts for beginners. Her publications emphasize typological analysis, Semitic language preservation, and interference phenomena in multilingual contexts. She contributes to open-access academic resources and folklore documentation.
