Paweł Garbacz is an Assistant Professor at the Department of Computer Science Fundamentals within the Faculty of Philosophy at the Catholic University of Lublin. His work bridges formal logic, ontology, and their applications in computer science and philosophy. He has authored two books: Sentence Logic - One or Many and Logic and Artifacts . Research Focus: Formal logic, computational ontologies, philosophy of technical artifacts, and semantic interoperability. Publications: His recent work explores identity criteria, temporal logic, and the intersection of metaphysics with artificial intelligence. Collaborations: Active in interdisciplinary projects connecting philosophy with computer science, including contributions to the FOIS (Formal Ontology in Information Systems) conference series.
Dr. Boyu Kuang is a Research Fellow in Computer Vision and Artificial Intelligence at Cranfield University, affiliated with the Centre for Computational Engineering Sciences. His work bridges academic research with industrial applications in aviation, robotics, and energy systems. Current role: Research Fellow in Computer Vision and AI Key affiliations: Centre for Computational Engineering Sciences Research interests include semantic segmentation , object detection , weakly and self-supervised learning , multi-modal perception , and vision-language foundation models . His methodology emphasizes robust AI systems for low-resource industrial environments , with applications in aviation maintenance , robotics , and energy infrastructure monitoring . He leads the Artificial Intelligence and Machine Learning module at Cranfield. Key activities include the UKRI, ATI, and Airbus-funded ONEHeart project on autonomous systems and intelligent inspection. Collaborations span Stanford University , King’s College London , Civil Aviation University of China , and industry partners like Airbus and Leidos . Publications focus on vision-language models , multi-modal perception , and industrial AI . As an Editorial Board Member for Discover Artificial Intelligence (Springer Nature), he contributes to academic governance. Peer review experience includes 60+ journal manuscripts for venues like IEEE Transactions on Image Processing and Elsevier Neural Networks . Facilities utilized include DARTeC and AIRC .
Jonathan Sterling is an Associate Professor in Logical Foundations and Formal Methods at the University of Cambridge , and a Fellow of Clare College . His research focuses on programming languages and formal methods , integrating type theory , category theory , domain theory , and topos theory to explore semantics of computation. His recent contributions include papers at POPL 2024 ( The Essence of Generalized Algebraic Data Types , Decalf: A Directed, Effectful Cost-Aware Logical Framework ), POPL 2022, and ICFP 2019. These works span logical frameworks , cost-aware programming , and modal dependent type theory , reflecting his interest in bridging theoretical insights with practical programming language design. Jonathan maintains active involvement in academic service, serving on program committees for OOPSLA, ICFP, POPL, TyDe, and WITS. He advocates for sustainable intellectual infrastructure, emphasizing the need for long-term funding models for tools like proof assistants and formal verification systems. His personal website and GitHub repositories ( @jonsterling ) host projects such as Agda-CAFe and RedTT , which explore formal methods and type theory.
Xavier Puig is a Research Scientist at FAIR, focusing on Embodied AI and human-centered intelligent systems. Previously, he earned his Ph.D. from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), advised by Professor Antonio Torralba. He also holds a double degree in Computer Science and Telecommunications from UPC's CFIS program. Education Ph.D., MIT (CSAIL), 2022 B.S./B.S., UPC (CFIS program), 2016 Research Interests span building agents that collaborate with humans through goal anticipation, task coordination, and realistic behavior modeling in simulation environments. His work integrates Embodied AI , Multi-agent Systems , and 3D Scene Understanding with applications in Human-Robot Collaboration and Interactive Decision-Making . Recent Trends in his publications emphasize Planning in Multi-agent Environments , Language-guided Robotics , Procedural Scene Generation , and Human Foundation Models for embodied tasks. Scientific Awards Best Paper Award, NeurIPS Cooperative AI Workshop (2020) Spotlight Presentation, ICLR (2021)
Dr. Simon Wells is a Lecturer at the School of Computing Engineering and the Built Environment, Edinburgh Napier University. His research bridges Artificial Intelligence, Argumentative Dialogue, and Human-Computer Interaction, focusing on formal models of reasoning and communication. He has developed computational systems enabling argumentation between humans and machines, applied across scientific, educational, and policy domains. University of Dundee (2011) University of Aberdeen (2012) Edinburgh Napier University (2014–present) His work integrates Behaviour Change and Interaction Design through gamification and persuasive technology, particularly in sustainable transport systems. Projects like SUPERHUB and West of Scotland Herring Hunt highlight his interdisciplinary approach, combining AI with environmental and cultural studies. Recent publications analyze the synergy between formal dialogue models and Large Language Models, offering insights into argument visualization, socio-technical trust, and segmentation-based behavior interventions. Key research areas include argumentation theory, evolutionary robotics, and digital ethnography. PhD Supervisor: Yagmur Yigit, Dana Khartabil Co-Supervisor: Thomas Farrenkopf Funded by the William Grant Foundation and Natural Environment Research Council, his projects span from citizen-science tools in Brazil to historical socio-economic studies in Scotland. Collaborations include institutions like the Open Microscopy Environment and SICSA.
Fangfang Liu is an active researcher with an extensive publication record spanning from 2005 to 2025, demonstrating significant contributions across multiple domains in computer science and engineering. Their work shows consistent collaboration with researchers including Weimin Li, Caili Guo, Zhimin Zeng, and Chunyan Feng, suggesting strong institutional ties within their research community. Fangfang Liu's research spans several key areas including wireless communications, knowledge graph completion, semantic communications, and fake news detection. Their work demonstrates expertise in applying machine learning techniques to solve complex problems in network security, IoT systems, and multimedia analysis. The research portfolio shows a progression from foundational work in wireless communications and polarization techniques toward more recent applications in AI-driven security and knowledge representation. The publication trends reveal a strategic expansion from traditional communications engineering into cutting-edge AI applications. Early work focused on polarization techniques and wireless channel modeling, while recent publications emphasize knowledge graphs, multimodal fake news detection, and semantic communications. This evolution demonstrates adaptability to emerging research frontiers while maintaining technical depth in signal processing and network analysis. Fangfang Liu has made substantial contributions through numerous publications in prestigious venues including IEEE Transactions, Expert Systems with Applications, and Knowledge-Based Systems. Their collaborative approach is evident through extensive co-author networks across academia and research institutions. While specific advising information isn't detailed in the publication record, Fangfang Liu appears to lead research groups working on precision assembly, knowledge graph applications, and IoT security. The research program demonstrates strong connections between theoretical foundations and practical implementations in communication systems and AI applications.
Prof. Jérôme Schmid is a Full Professor (Professeur HES ordinaire) at the Haute école de santé - Genève, part of the Faculty of Health. His expertise spans medical image processing, artificial intelligence (AI), and their clinical applications. He leads innovative projects addressing challenges in diagnostics, surgery planning, and medical education. Key roles include Principal Investigator in grants funded by Swiss National Science Foundation, Swiss Innovation Agency, and others. Research Focus: Combines AI with medical imaging for applications such as Parkinson’s disease detection via SPECT, breast lesion analysis using ultrafast MRI, and AI-driven radiography training tools. Projects emphasize interdisciplinary collaboration with hospitals and industry partners. Projects: DeepDAT (2022–2025): AI for Parkinson’s diagnosis via SPECT imaging. SUBREAM (2022–2025): Rapid breast MRI protocols with AI integration. AIRx (2019–2020): AI-based radiography simulation for student training. MyHip (2012–2014): Patient-specific hip arthroplasty planning. Publications: Focus on AI-driven diagnostics, imaging techniques, and medical education. Recent works address drowning detection via post-mortem CT, multimodal AI fusion for breast cancer, and serious games in radiology training. Grants & Partnerships: Swiss National Science Foundation: FAI analysis via multi-modal imaging. Innosuisse: Low-cost X-ray detectors for developing countries (GlobalDiagnostiX). Swiss Cancer Research Foundation: Breast MRI advancements (SUBREAM). Labs/Teams: Collaborates with the Geneva University Hospitals, EPFL, and industry (e.g., Medacta International SA) on hardware and clinical AI solutions.
Elmar Rückert is a Professor at the Chair of Cyber Physical Systems at Karlsruhe Institute of Technology. His work focuses on robotics, machine learning, and industrial automation with applications in environmental science and data-driven systems. Recent activities include invited talks on topics like Bayesian Optimization for control systems and deep learning in robotics. Research interests span tactile robot learning, privacy-aware AI, and sensor fusion for autonomous systems. He has published extensively in venues like IEEE International Conference on Robotics and AAAI Conference on Artificial Intelligence, with a strong emphasis on practical applications in industrial processes and environmental monitoring. Key contributions include the EnvoDat dataset for robotic spatial reasoning and methods for skill disentanglement in RKHS. His collaborative work extends to material science and environmental chemistry through partnerships on technology-critical element analysis.
Renjie Liao is an Assistant Professor (tenure-track) in the Department of Electrical and Computer Engineering (ECE) at the University of British Columbia (UBC), with an associated appointment in the Department of Computer Science. He is also a Faculty Member at the Vector Institute and a Canada CIFAR AI Chair. Prior to UBC, Dr. Liao was a Visiting Faculty Researcher at Google Brain and held a Senior Research Scientist position at Uber Advanced Technologies Group during his PhD. He earned his B.Eng. (Automation) from Beihang University, M.Phil. (CS) from the Chinese University of Hong Kong, and PhD (CS) from the University of Toronto. His research focuses on probabilistic and geometric deep learning , with key contributions in deep generative models, geometric deep learning, neural algorithmic reasoning, and generalization bounds. Notable areas include 3D point cloud analysis, self-driving systems, and healthcare applications using graph neural networks. His work bridges theoretical foundations (e.g., PAC-Bayes bounds) with practical applications like motion forecasting and medical imaging. Education: B.Eng. in Automation, Beihang University (2011) M.Phil. in Computer Science, CUHK (2015) PhD in Computer Science, UofT (2021) Dr. Liao has received awards such as the RBC Graduate Fellowship and Connaught International Scholarship. His lab (Deep Structured Learning Lab) emphasizes principled mathematical approaches to solving complex problems. He advises students in machine learning, computer vision, and robotics, encouraging applications from those with strong coding/mathematical backgrounds. Labs/Teams: Deep Structured Learning Lab (UBC) Vector Institute Collaboration
Dr Fernando Alvarez Borges is a Senior Research Fellow at the University of Southampton, specializing in X-ray and neutron computed tomography applications for geomaterials, particulates, and porous media research. His work integrates Deep Learning methods with geomechanics, particularly focusing on offshore renewable energy infrastructure. With over eight years of experience in non-destructive analysis, he contributes to academic and enterprise projects across material sciences, palaeontology, and conservation. Research Interests: 3D imaging technologies, geotechnical engineering, renewable energy systems, and AI-driven image analysis Methodologies: Synchrotron X-ray tomography, neutron imaging, computational modeling Recent publications highlight his work on hydrogen storage in geological formations, methane hydrate dynamics, and advanced composite manufacturing. He actively collaborates with interdisciplinary teams across geosciences, mechanical engineering, and biomedical applications. External Engagement: Invited speaker at the 6th Annual Workshop on Advances in X-ray Imaging (2023)
Dr. Vasha DuTell is a Postdoctoral Research Associate at the Laboratory for Atmospheric Experiments (LAE) and Lecturing Faculty at UC Berkeley's School of Information. She holds a PhD in Vision Science from UC Berkeley, where she collaborated with Drs. Bruno Olshausen and Martin Banks on natural scene statistics and human vision modeling. Her postdoctoral work at MIT CSAIL focused on spatiotemporal and peripheral vision modeling under Drs. Ruth Rosenholtz and Bill Freeman. Her research combines computer vision and deep learning to address environmental challenges like contrail detection from satellite and ground-based imagery. She develops segmentation models for contrail tracking and studies their environmental impacts. Vasha also teaches computer vision at UC Berkeley, bridging academic instruction with cutting-edge research. Her recent publications span topics such as peripheral vision modeling, perceptual straightness in machine learning models, and high-fidelity tracking systems. These studies emphasize interdisciplinary approaches, merging computational methods with biological insights to advance vision science and environmental monitoring. Dr. DuTell’s work has contributed to foundational datasets like Coco-periph and frameworks like GramStatTexNet, which explore human-machine perception gaps and texture model statistics. Her efforts aim to enhance both theoretical understanding and practical applications in vision systems and climate research.
Armando Solar-Lezama is a Professor at the MIT Schwarzman College of Computing , Associate Director and COO of MIT CSAIL, and leads the Computer-Aided Programming Group . He earned his BS in Computer Science and Mathematics from Texas A&M University and PhD (2008) from UC Berkeley under Rastislav Bodik. His research focuses on program synthesis at the intersection of Programming Systems and Artificial Intelligence, with recent work on neurosymbolic programming. Education: BS (Texas A&M), PhD (UC Berkeley) Labs: CSAIL, Center for Deployable Machine Learning His research explores automated reasoning and learning to reduce programming effort, including the development of the Sketch programming language. Current neurosymbolic work combines deep learning with logical reasoning for applications in multi-agent systems, RNA splicing, and interpretable policy generation. Recent projects include VLMaterial for procedural asset generation and CRUXEval for code evaluation benchmarks. Scientific awards include: Robin Milner Young Researcher Award (2024) Best Paper (PLDI 2005, PPoPP 2016) Outstanding Paper (EMNLP 2023) He advises graduate students through MIT's EECS PhD program and has developed courses like 6.820 Foundations of Program Analysis and Program Synthesis . His work impacts software synthesis automation, programming language design, and deployable machine learning.
Roger Zimmermann is a Full Professor at the School of Computing, National University of Singapore (NUS), where he is also a Co-PI at the Grab-NUS AI Lab and leads the Location AI project. He previously served as Deputy Director of the NUS Smart Systems Institute (SSI) and Co-Director of the Centre of Social Media Innovations for Communities (COSMIC), both funded by Singapore’s National Research Foundation (NRF). Before joining NUS, he was a Research Area Director and Research Assistant Professor at the University of Southern California (USC). Ph.D. in Computer Science, University of Southern California (1998) M.S. in Computer Science, University of Southern California (1994) His research focuses on multimedia systems , spatio-temporal data management , streaming media architectures (especially DASH), machine learning applications , AR/VR , and location-based services . He leads the Media Management Research Lab (MMRL) at NUS, which conducts cutting-edge work in distributed multimedia and intelligent systems. His work combines theoretical depth with real-world applications in urban computing, smart mobility, and immersive media. The recent publications reflect a strong trend toward multimodal learning , spatio-temporal AI , adaptive streaming , and urban intelligence . His team explores zero-shot learning, 3D scene understanding, traffic forecasting, and open-vocabulary audio-visual segmentation, often leveraging foundational models and deep neural architectures. There is a clear emphasis on real-time, scalable systems for smart cities and immersive experiences. Dr. Zimmermann has received numerous accolades, including: DASH-IF Excellence in DASH Award (multiple years) Best Paper Awards at ACM SIGSPATIAL, IEEE ICME, and ACM MMSys Silver Award at ACM MMSys 2020 Grand Challenge IEEE Communications Society Best Editor Award (2017) ACM Distinguished Member (2017) Top 1% Publons Reviewer in Computer Science (2018) He has advised numerous students and led major research initiatives funded by MOE, NRF, A*STAR, NSF, and industry partners like Seagate, Intel, and HP. He has served as General Chair for IEEE MIPR 2023, ACM Multimedia 2020, and IEEE ISM 2015, and as TPC Co-Chair for several top-tier conferences. His editorial roles include Associate Editor for IEEE Transactions on Multimedia (TMM), ACM TOMM, and IEEE OJ-COMS. He leads the Media Management Research Lab (MMRL) , which focuses on intelligent multimedia systems, spatiotemporal data mining, and immersive media technologies. The lab develops scalable solutions for real-world challenges in urban computing, smart transportation, and interactive media.
Christopher McComb is an Associate Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering. He leads research in sociotechnical systems, machine learning for engineering design, and human-AI collaboration. He is affiliated with the Block Center for Technology and Society, Manufacturing Futures Institute, NextManufacturing Center, and Wilton E. Scott Institute for Energy Innovation. Previously, he was an assistant professor at Penn State, where he directed the Center for Research in Design and Innovation and led the Technology and Human Research in Engineering Design Group. Ph.D., Mechanical Engineering, Carnegie Mellon University M.S., Mechanical Engineering, Carnegie Mellon University B.S., Civil Engineering and Mechanical Engineering, California State University-Fresno His research centers on human-AI teaming , sociotechnical systems , and computational design , with applications in additive manufacturing, STEM education, and energy systems. He explores how machine learning can enhance engineering design processes, particularly through human-centered AI, generative design, and agent-based modeling. His work emphasizes the integration of human cognition and behavior into AI systems to improve collaboration and innovation. The 15 most recent publications (2025) demonstrate a strong trend in AI-driven design automation , neural surrogate modeling , human-AI interaction , and data generation for engineering simulations . Topics span from using large language models for material selection and design concept generation to developing datasets and benchmarks for advanced manufacturing and CAD systems. There is a clear emphasis on real-world applications in aerospace, finance, and global manufacturing, particularly in Africa. National Science Foundation Graduate Research Fellow McComb has received research funding from NSF, DARPA, and private corporations, and has collaborated with Boeing through their Visiting Professorship Program. He advises students in mechanical engineering and design, and leads the Human+AI Design Initiative and the Design Research Collective. His research has been applied in partnerships with NASA and in addressing manufacturing challenges in Africa. He leads or contributes to interdisciplinary research teams focused on AI in design, additive manufacturing, and energy systems. His labs and initiatives include the Human+AI Design Initiative and the Design Research Collective, which foster collaboration between human-centered design and artificial intelligence.
Prof Dirk Pattinson is a Professor in the School of Computing at Australian National University (ANU). His research focuses on modal logic, coalgebraic systems, automated reasoning, and formal methods. He holds a PhD in Computer Science and has supervised numerous research students. Research interests include coalgebraic logic, non-classical modal logics, automated theorem proving, and applications in computational social choice. His work bridges theoretical foundations with practical tools like the COOL reasoner for modal fixpoint logics. Notable contributions span over 70 peer-reviewed publications since 2008, with recent work on non-iterative modal resolution calculi (2024), Hennessy-Milner properties via topological methods (2022), and formal verification of voting systems (2021). His research often integrates algebraic, categorical, and coalgebraic perspectives.
