Dr. Paul Lerner is a researcher at the Institute for Intelligent Systems and Robotics (ISIR), affiliated with Sorbonne University (formerly Université Pierre et Marie Curie). His work focuses on machine translation, multimodal learning, and knowledge-based visual question answering systems. Research Interests: Machine translation for scientific neologisms and inclusive French Cross-modal retrieval in visual question answering Integration of knowledge bases into multimodal systems Development of NLP datasets for emerging tasks Publications: Active in top venues like COLING, ECIR, and SIGIR since 2019, with recent 2025 work on BPE segmentation limitations in LLMs and scientific translation challenges. Projects: Creator of datasets including ViQuAE (visual QA), Bazinga! (dialogue structuring), and INCLURE (inclusive translation toolkit).
Kun Gao is an Assistant Professor at the Department of Architecture and Civil Engineering at Chalmers University, leading the Urban Mobility Systems research group. His work bridges transportation engineering and data science to develop sustainable mobility solutions through electrification, shared systems, and connected infrastructure. Research Focus: Electric vehicle integration, charging infrastructure optimization, multimodal mobility systems Funding: Supported by JPI Urban Europe, FORMAS, Swedish Innovation Agency, Swedish Energy Agency, and Chalmers AoA Transport/Energy Methods: Machine learning, big data analytics, system optimization His recent publications emphasize autonomous vehicle safety , renewable energy integration , and equity in mobility systems . Current work explores AI-driven infrastructure planning and coupled transportation-energy systems.
Marcela Munera is an Associate Professor in Assistive Robotics at the University of the West of England (UWE Bristol). Her research focuses on robotic devices for rehabilitation, human-robot interaction, biomechanics, and movement analysis, with a particular emphasis on user-centered design approaches. Bioengineer, Universidad de Antioquia (Colombia) MSc in Mechanics and Materials, Ecole Nationale de Metz (France) PhD in Mechanics and Biomechanics, Université de Reims Champagne Ardenne (France) Key research areas include socially assistive robotics, rehabilitation robotics, and biomechanical modeling. She has led projects involving exoskeletons, smart walkers, and wearable sensors, often integrating participatory design and multimodal feedback mechanisms. Her publications highlight interdisciplinary applications in neurological rehabilitation (e.g., stroke, Parkinson's disease), autism therapy, and occupational health. Recent work explores smart upper-limb exoskeletons for construction workers, stress classification via novel sensors, and adaptive control systems for mobility assistance. FEDER, Region Champagne Ardenne Doctoral Grant Her doctoral research focused on industrial biomechanical assessments for sports performance and injury prevention, later expanding to human-centered rehabilitation robotics. She has collaborated on projects involving brain-computer interfaces, serious games, and cloud robotics frameworks like PoundCloud.
Emre Ugur is an Associate Professor in the Department of Computer Engineering at Bogazici University, where he serves as the head of the Cognition, Learning and Robotics (CoLoRs) laboratory. His research focuses on bridging the gap between continuous sensorimotor experiences and discrete symbolic representations in robotics. Funded by major international sources including the European Commission's Horizon 2020 program and TUBITAK, his work has significant implications for cognitive robotics and autonomous systems. Education: PhD in Computer Engineering from Middle East Technical University (METU, Turkey) Ugur's research interests center on cognitive and developmental approaches to robotics, with particular emphasis on neuro-symbolic integration, affordance learning, and symbol emergence. His work explores how robots can autonomously develop high-level cognitive capabilities through continuous interaction with their environment, similar to human cognitive development. His approach combines machine learning, cognitive science, and robotics to create systems that can learn, predict, and reason about their actions. His recent publications reveal a strong trajectory toward neuro-symbolic robotics, where he develops methods for extracting discrete symbolic representations from continuous sensorimotor experiences. This work enables robots to perform complex planning and reasoning tasks while maintaining connection to physical reality. There is also significant focus on social robotics, particularly in human-robot interaction, social navigation, and embodied cognition. Scientific Awards: The Young Scientist Award by the Science Academy (BAGEP) The Excellence in Teaching Award by the Faculty of Engineering (2023) As Principal Investigator of major projects including INVERSE (EU Horizon 2025), DEEPPLAN (TUBITAK), and previously DEEPSYM and IMAGINE, Ugur has established a robust research program that bridges theoretical advances with practical applications. He has supervised numerous PhD and Master's students who have made significant contributions to the field. His leadership extends to organizing major workshops at top robotics conferences including IROS, RSS, and ICRA. At the Cognition, Learning and Robotics (CoLoRs) lab, Ugur leads research on cognitive robotics, developmental robotics, and neuro-symbolic AI. The lab explores fundamental questions about how robots can develop understanding of their actions, learn from interaction, and form abstract representations necessary for high-level cognition. Current projects focus on symbolic reasoning, prediction, and planning in robotic systems.
Wei Liu is an Associate Professor in Machine Learning and Director of the Future Intelligence Research Lab at the University of Technology Sydney's School of Computer Science. He holds a PhD in Machine Learning from the University of Sydney and maintains active roles as a senior IEEE member and area chair for top AI conferences including KDD, AAAI, and ICDM. Education: PhD in Machine Learning, University of Sydney His research focuses on adversarial machine learning, generative AI, cybersecurity, and multimodal learning, with particular emphasis on AI security, robustness of algorithms, and model fairness. Liu's work addresses critical challenges in developing next-generation AI systems that can withstand cyber attacks while maintaining performance with multi-modal data and balanced outcomes despite data imbalances. Analysis of his recent publications reveals a strong trend toward securing large language models against novel attack vectors while advancing multimodal learning techniques. His work spans both theoretical contributions in adversarial frameworks and practical applications in cybersecurity, transportation, and industrial systems. Scientific Awards: 3 Best Paper Awards Most Influential Paper Award at PAKDD Nominee for NSW Premier's Prizes for Early Career Researcher (2017) Liu actively supervises numerous PhD students working on adversarial attacks, robust AI models, and agricultural applications. He has secured substantial funding including ARC Discovery Projects, government grants, and industry partnerships with organizations including Agriwebb, CSIRO Data61, and AVEVA. His Future Intelligence Research Lab specifically targets three emerging challenges: AI security against cyber attacks, robustness with multi-modal data, and model fairness with imbalanced datasets. The Future Intelligence Research Lab produces next-generation AI algorithms addressing AI security vulnerabilities, multi-modal robustness challenges, and fairness issues in real-world deployment scenarios, with multiple representative papers demonstrating practical applications in each domain.
Matthew F. Glasser, MD, PhD is an Assistant Professor of Radiology at Washington University School of Medicine in St. Louis, affiliated with the Mallinckrodt Institute of Radiology (MIR) and the Computational Imaging Research Center (CIRC). He co-directs a brain imaging laboratory with David Van Essen, PhD, and serves as co-leader of the Adult Aging Brain Connectome (AABC) project's Informatics, Data Analysis, and Statistics Core (IDASC). Education: Undergraduate: Emory University Doctorate: Washington University School of Medicine in St. Louis Medical Degree: Washington University School of Medicine in St. Louis Residency: Diagnostic Radiology, Mallinckrodt Institute of Radiology Fellowship: Neuroradiology, Mallinckrodt Institute of Radiology Dr. Glasser's research focuses on neuroanatomy, connectomics, and medical image analysis. He is best known for his work mapping 180 areas of each human cerebral cortical hemisphere using multiple MRI modalities as part of the Human Connectome Project (HCP). His work involves developing techniques for cortical myelin mapping to identify brain areas and align maps across individuals. He continues this research through the Adult Aging Brain Connectome (AABC) project, which aims to uncover factors underlying vulnerability and resilience to late-life dementia. Dr. Glasser's publication record demonstrates a consistent focus on advancing brain imaging techniques and understanding brain connectivity. His work spans from foundational methods in the Human Connectome Project to applications in aging and neurological disorders. Recent publications show an expansion into clinical applications like speech neuroprosthetics and depression treatment, indicating how his fundamental research in brain mapping is translating into therapeutic innovations. Scientific Awards: 2021–2022 Highly Cited Researcher, Clarivate 2022 Roentgen Resident/Fellow Research Award, Radiological Society of North America 2022 Alpha Omega Alpha, Washington University School of Medicine 2018-2021 Highly Cited Researcher, Clarivate 2017 Hugh M. Wilson Award in Radiology, Mallinckrodt Institute 2016 Dennis Hallahan Fellowship for Outstanding Research 2016 Olin Medical Scientist Fellowship 2009 Pittsburgh Brain Connectivity Competition Winner 2008 Distinguished Young Scholar Award Dr. Glasser has been instrumental in securing significant research funding, most notably as a key contributor to the $30 million Human Connectome Project funded by the NIH. He currently co-leads the AABC's Informatics, Data Analysis, and Statistics Core. While the text doesn't specify his current advisees, his position as Assistant Professor and research leadership suggests he mentors graduate students and postdoctoral researchers in neuroimaging and connectomics. Dr. Glasser co-directs a brain imaging laboratory with David Van Essen that is now part of the Computational Imaging Research Center (CIRC). This laboratory has been central to the Human Connectome Project and its successor, the Adult Aging Brain Connectome project. The lab brings together expertise in neuroscience, radiology, computer science, and statistics to advance brain imaging methodologies and applications.
Professor Yuan Miao is a distinguished academic at Victoria University (VU), serving as Professor in the College of Arts, Business, Law, Education & IT and Head of the Information Technology Program. With a PhD from Tsinghua University's Automation Department, his academic journey spans prestigious institutions including the University of Melbourne and Nanyang Technological University in Singapore before settling at VU where he has been Professor since January 2010, following his Associate Professorship from August 2004 to December 2009. Education: BSc, Shandong University, China MEng, Tsinghua University, China PhD, Tsinghua University, Automation Department, China Professor Miao's research centers on Large Language Models (LLMs) and Generative AI, where he has identified critical barriers in practical applications including limited memory length in systems like ChatGPT and Gemini, contradictory explanations, lack of local knowledge integration, and significant errors in text-data hybrid reasoning (up to 38%). His innovative solutions involve cognitive map graphs and rational intelligence models to create customized AI systems. His work spans diverse application areas including human knowledge modeling, multimodal interaction, healthcare analytics (particularly dementia detection), cybersecurity, and robotics powered by rational intelligence. Analysis of Professor Miao's recent publications reveals a strong focus on integrating LLMs with specialized knowledge domains across healthcare, cybersecurity, and social media analysis. His research consistently addresses practical limitations of current AI systems while developing novel frameworks for more reliable and context-aware applications. The interdisciplinary nature of his work is evident in publications spanning medical informatics, cybersecurity analytics, and educational technology. Scientific Recognition: Two articles in fuzzy cognitive map modeling ranked among top 10 most cited works since 2000 (Google Scholar 2000-2016) Development of adversarial dataset based on SQuAD 2.0 that reduced BERT and ELECTRA accuracy from ~90% to ORCID identifier 0000-0002-6712-3465 with 138 peer-reviewed publications Professor Miao actively supervises PhD and Master's students across diverse research topics including access control systems, healthcare analytics, cybersecurity, and social behavior analysis. His research has secured substantial funding from both industry giants (Microsoft, Amazon, Oracle, Google) and government bodies (Australia Research Council, Data61, Singapore's NRF), with recent projects including Digital Transformation for Construction Industry ($1.258 million), Western Health SharePoint Development ($68,000), and Big Data Analysis for Domestic Violence Research (US$100,000). His current grant portfolio demonstrates strong industry-academia collaboration addressing real-world challenges. Professor Miao leads research teams focused on rational intelligence systems that overcome current LLM limitations, with particular emphasis on creating practical AI solutions for healthcare, cybersecurity, and smart city applications. His work with Maribyrnong City Council on the Smart City at Footscray Park project ($850,000) exemplifies his commitment to applying advanced AI research to community-level challenges.
Gerardo Aragon Camarasa is a Senior Lecturer at the School of Computing Science, University of Glasgow, where he leads research in the Computer Vision and Autonomous Systems group. His work focuses on solving real-world challenges in robotic perception, manipulation, and grasping using advanced AI techniques. Research Interests: Robotics and AI for advanced manufacturing systems Perception and manipulation of deformable objects Autonomous robotic systems for chemical and domestic applications Robot behavior modeling and self-awareness His publications demonstrate strong emphasis on robotic vision (garment perception, hand-eye calibration), AI integration (multimodal LLMs, reinforcement learning), and industrial applications (chemical robotics, Industry 5.0 ethics). Recent work shows growing focus on foundation models for robotics and simulation frameworks. Research Leadership: He leads multiple grants including an EPSRC programme grant for chemical robotics and a Royal Society project on robotic teleoperation. Actively supervises 7+ PhD students and has graduated 8+ doctoral researchers in robotics and computer vision. Infrastructure: Leads research using dual-arm robots and maintains active GitHub repositories and YouTube channels demonstrating robotic manipulation systems. Regular contributor to top robotics conferences (ICRA, IROS) and journals.
Dr. Kenneth Marino is a Research Scientist at DeepMind , set to join the University of Utah as an Assistant Professor at the Kahlert School of Computing in Fall 2025. He earned his PhD in Machine Learning from Carnegie Mellon University (funded by NDSEG and NSF GRFP fellowships) and completed his undergraduate studies in Computer Engineering with a minor in Computer Science at Georgia Tech . Dr. Marino's research focuses on the intersection of Computer Vision , Natural Language Processing , and Reinforcement Learning , with emphasis on: Multimodal agents operating on the web, in simulation, and in real-world environments Evaluating AI systems and creating high-impact datasets (e.g., A-OKVQA , OK-VQA ) Incorporating semantic knowledge into end-to-end learning frameworks Embodied reasoning through language-guided planners and neural reporters Language model distillation for agent supervision Human-AI collaboration to improve RL generalization His work has been published in top venues including ICML , NeurIPS , ECCV , CVPR , and ICLR , with recent trends emphasizing: Relational reasoning in LLMs Object-centric world modeling Continual learning for embodied agents Reporter neural networks for agent control Legal AI benchmarking (e.g., BriefMe ) Knowledge graph integration for visual classification Scientific recognition includes: NDSEG Fellowship NSF GRFP Fellowship He has served on the MLD PhD Admissions Committee and as an Area Chair for ECCV 2024 , with prior teaching experience at Columbia University (COMS 6998) and Carnegie Mellon University (16-824, 10-401).
Marcus Specht is a Professor affiliated with Delft University of Technology and Leiden University, Netherlands. His research focuses on Educational Technology, Learning Analytics, and Artificial Intelligence in Education. He has contributed to projects involving agent-based social skills training, hybrid intelligence for cognitive process analysis, and computational thinking assessment in higher education. His work spans mobile learning, collaborative learning analytics, and gamification in MOOCs. He collaborates extensively with researchers like Marco Kalz, Roland Klemke, and Hendrik Drachsler. Notable contributions include the Presentation Trainer for public speaking feedback and the DojoIBL platform for inquiry-based learning. His research emphasizes multimodal learning systems, including AR/VR applications and sensor-based training tools. He explores the integration of AI into educational platforms, as seen in projects like JELAI and the ARTES architecture for social skills training.
Dr. Lesley Ross is a Professor and SmartLife Endowed Chair in Aging and Cognition at Clemson University's College of Behavioral, Social and Health Sciences, Department of Psychology. She also directs the Institute for Engaged Aging and co-directs the SHAARP lab. Her research focuses on cognitive aging, mobility, and interventions to maintain healthy aging, with funding from NIH, NHTSA, and the Department of Transportation. Education: Ph.D. in Lifespan Developmental Psychology, University of Alabama at Birmingham (2007) M.Ed. in Secondary Education, University of Montevallo (2003) M.A. in Lifespan Developmental Psychology, University of Alabama at Birmingham (2006) B.A. in Psychology and French, University of Montevallo Research Interests: Cognitive aging, mobility preservation, driving safety, behavioral interventions (cognitive, exercise, multimodal), and applied technologies to enhance aging outcomes. Her work emphasizes translating research into practical interventions like cognitive training programs. Key Awards: SmartLife Endowed Chair (2020) Evelyn R. Saubel Faculty Award (2017) Fellow of The Gerontological Society of America (2016) 2012 Nathalie Molton Gibbons Young Achiever’s Award Grants & Labs: Principal investigator on NIH-funded projects studying cognitive training mechanisms. Co-directs the SHAARP lab, focusing on healthy aging interventions. Collaborates with the Transportation Research Board on mobility research. Labs/Teams: SHAARP lab (Study of Healthy Aging & Applied Research Programs), Institute for Engaged Aging. Active in interdisciplinary collaborations across public health, transportation, and gerontology.
Lorraine Li is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh, affiliated with the Interdisciplinary Science Program (ISP) and the School of Computing and Information. She holds a PhD from the University of Massachusetts Amherst (2022) and conducted postdoctoral research at AI2's Mosaic team. Her work focuses on NLP, machine learning, and socially responsible AI systems. Education: PhD in Computer Science (UMass Amherst, 2022) Research explores evaluation frameworks for commonsense knowledge, model interpretability, and ethical AI applications in domains like education and law. Key interests include probabilistic models, long-tail reasoning, and geographic robustness in LLMs. Recent publications address confirmation bias in reasoning chains (ACL 2025), geographically diverse prompting (CVPR 2024), and uncommon scenario reasoning (NAACL 2024). She co-organized the AAAI 2024 Make symposium and serves on committees for ACL, EMNLP, and NAACL. Grants: Pitt Cyber funding (2024) Lab: Pitt NLP Seminar group
Pascal Knierim is a researcher in the Department of Computer Science at Ludwig Maximilian University of Munich, Germany. He completed his PhD at the same institution in 2020 with a dissertation titled "Enhancing interaction in mixed reality: the impact of modalities and interaction techniques on the user experience in augmented and virtual reality." His research focuses on human-computer interaction, particularly in virtual and augmented reality environments, with an ORCID identifier 0000-0001-9578-9953. Knierim's research interests span virtual reality, augmented reality, mixed reality, ubiquitous computing, and extended reality systems. He has made significant contributions to understanding user interaction in immersive environments, privacy considerations in VR/AR, biometric identification using thermal imaging, and universal interaction frameworks. His work often combines technical innovation with user-centered design principles, resulting in numerous publications at top-tier venues including CHI, MUM, UbiComp, and IEEE Pervasive Computing. Recent work has explored content blocking in extended reality, social anxiety in VR proxemics, user awareness of privacy permissions, and framework development for ubiquitous research preservation. Knierim has collaborated extensively with researchers such as Thomas Kosch, Florian Alt, and Albrecht Schmidt, forming a productive research group within LMU Munich's computer science department. He has also contributed to the academic community through editorial roles for major conferences including Mensch und Computer 2023 and the 22nd International Conference on Mobile and Ubiquitous Multimedia (MUM 2023), demonstrating leadership within his research community. His research demonstrates a strong commitment to both theoretical advancement and practical applications of immersive technologies, with particular attention to user experience, privacy, and accessibility considerations.
Francesco Ragusa is a Research Fellow at the University of Catania, Italy, holding an Industrial Doctorate in Computer Science (2021). He spent part of his PhD at the University of Hertfordshire, UK. His work focuses on First Person (Egocentric) Vision, including Human-Object Interaction, Industrial Applications, and Augmented Reality. He co-founded NEXT VISION s.r.l., an academic spin-off from the University of Catania since 2021. Key projects include the MECCANO dataset for industrial human-object interactions and the EGO-EXO4D initiative analyzing skilled human activity from multi-perspective views. Research interests span Computer Vision, Pattern Recognition, and Machine Learning. He contributed to seminal datasets like MECCANO and ENIGMA-51, advancing understanding of human behavior in industrial settings. His work emphasizes practical applications, such as wearable assistive systems and visual navigation solutions. Ragusa has delivered tutorials at major conferences (e.g., ICIAP, VISIGRAPP) on First Person Vision’s history, challenges, and trends. He actively promotes industrial collaborations and has secured grants including PNRR MUR (Code E63C22001940006) and EU funding under Next Generation EU. His teaching and professional activities include organizing workshops on egocentric vision for AI-driven assistants and industrial safety systems. Current research directions involve multimodal synthetic data utilization, gaze-based interaction analysis, and cross-modal fusion for human-robot collaboration.
Georg Groh is an Adjunct Professor at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology . His research focuses on modeling social context, social interaction mediated by IT systems, and ML-based natural language processing. He holds a doctorate (2005) and habilitation (2012) from TUM, with prior studies in physics and computer science. Key research areas include social signal processing, network analysis, and bias detection in AI systems. Notable awards include the 2019 Supervisory Award and 2016 Honorary Teaching Certificate. His work bridges computational methods with societal impacts, particularly in health informatics and ethical AI. Recent projects explore LLM hallucination detection, bias profiling, and cross-lingual text classification. Education: PhD in Computer Science (2005), TUM Habilitation in Computer Science (2012), TUM Studies in Physics (University of Kaiserslautern) and Computer Science (Universities of Hamburg, Kaiserslautern, TUM) Research Interests: Groh’s work spans social computing, NLP, and ethical AI . Current projects address bias in language models, hate speech detection, and data-driven health interventions. His methodologies emphasize contextual analysis of social interactions, leveraging ML and graph-based techniques. Awards: 2nd place Supervisory Award (2019) Best Paper Awards (2016, 2008) Advising & Grants: Advised on projects like Nutrilize (nutrition recommender system) and contributed to EU-funded initiatives on mHealth systems. Active in designing AI systems for dietary logging and stress management.
