Huijuan Xu is an Assistant Professor in the Department of Computer Science and Engineering. Her research spans artificial intelligence, computer vision, and knowledge representation, with a focus on temporal modeling, semantic reasoning, and multimodal learning. She has contributed to advancements in virtual reality streaming, knowledge graph completion, and weakly-supervised video analysis. Research output: 32 publications (2015-2025), including 15 peer-reviewed articles and conference contributions Core research areas: Representation Learning (100% match), Knowledge Graph (100% match), Temporal Action Detection (86% match), and Motion Feature Learning (73% match) Her recent work explores: 2025 : Bandwidth-optimized VR streaming for edge devices 2024 : Neural concept reasoning for image retrieval and avatar generation from sparse data 2023 : Zero-shot scene graph generation and bias mitigation in visual QA
Petros Maragos is a Professor at the National Technical University of Athens (NTUA) in the School of Electrical and Computer Engineering, where he directs the Division of Signals, Control and Robotics. He founded the Computer Vision, Speech Communication & Signal Processing Lab (1999) and the Hellenic Robotics Center of Excellence (2025). His research spans signal processing, computer vision, robotics, and machine learning, with 450+ publications and leadership in 50+ EU/Greek/US projects. Education includes a Dipl.Ing. from NTUA (1980), M.Sc./Ph.D. from Georgia Tech (1982/1985), and faculty positions at Harvard University (1985-1993) and Georgia Tech (1993-1998). Research Focus: Multimodal perception, nonlinear systems, assistive robotics, and deep learning. Recent work integrates tropical geometry with neural networks, robotic healthcare applications, and sign language technologies. Articles emphasize neural architectures, real-world robotics, and AI for social good. Awards: IEEE Fellow (1995), EURASIP Fellow (2010) IEEE W.R.G. Baker Prize (1995) NSF Presidential Young Investigator Award (1987-1992) CVPR/PETRA Best Paper Awards (2022-2025) Advising & Grants: Supervised 30+ PhDs and 130+ Master's students. Secured funding from EU Horizon 2020, NSF, and Greek national programs for projects like i-Walk (robotic mobility) and e-Prevention (mental health monitoring). Labs: Leads NTUA's Intelligent Robotics Lab and co-founded the Robotics Institute at Athena Research Center, focusing on human-robot interaction and perception systems.
Mengshan Xu is an Assistant Professor of Applied Econometrics at the University of Mannheim's Department of Economics since August 2021. His academic journey includes an M.Sc. from Humboldt University of Berlin (2015) and a Ph.D. from the London School of Economics and Political Science (2021). Education: M.Sc., Humboldt University of Berlin (2015) Ph.D., London School of Economics and Political Science (2021) His research focuses on Econometrics, Semi-nonparametric Econometrics, and Statistical Learning. Despite his primary affiliation with economics, his recent publications suggest interdisciplinary work spanning Artificial Intelligence, Robotics, and Computer Vision , including human-aware navigation frameworks, attention mechanisms for LLMs, and skeleton-based action recognition systems. Article trends reveal expertise in vision-and-language navigation , anomaly detection , and multi-modal learning , blending econometric theory with computational methods. Notable subfields include dynamic human interactions, deep invertible networks, and hypergraph transformers. His professional contact details include a direct email ( mengshan.xu@uni-mannheim.de ) and office location in Mannheim. No scientific awards or student advisement details are publicly listed in the provided materials.
Niranjan Balasubramanian is an Assistant Professor in the Department of Computer Science at Stony Brook University with additional affiliations in the Department of Biomedical Informatics and the Center of Excellence in Wireless & Information Technology (CEWIT). His research focuses on Natural Language Processing and Information Retrieval systems that extract, understand, and reason over textual information. Education: PhD, University of Massachusetts Amherst (Center for Intelligent Information Retrieval) MS, Computer Science, University at Buffalo (2003) His research spans question answering for elementary education, event schema generation from news, machine learning for information retrieval, energy-efficient mobile search, and automatic Wikipedia content generation. Recent work explores causal reasoning in event extraction, multimodal claim verification, authorship fairness, and secure coding with large language models. Analysis of his 15 most recent publications (2024-2025) reveals intensive focus on advancing NLP through causal/temporal reasoning, multimodal verification, and LLM optimization. Key trends include psychological modeling of human language, energy-efficient architectures, and addressing misattribution in authorship analysis. Scientific Awards: No awards mentioned in provided text Advising and grants information was not provided in the source materials. His postdoctoral background at the University of Washington's Turing Center and industry experience at Syracuse University's Center for Natural Language Processing inform his applied research approach. Labs and Teams: Affiliated with Stony Brook's Center of Excellence in Wireless & Information Technology (CEWIT), contributing to interdisciplinary AI initiatives while maintaining primary focus in the Computer Science department.
Michael Ryoo serves as a SUNY Empire Innovation Associate Professor in the Department of Computer Science at Stony Brook University while concurrently working as a Research Scientist with Google Brain's "Robotics at Google" team. Previously, he held positions as an Assistant Professor at Indiana University Bloomington and a Staff Researcher at NASA's Jet Propulsion Laboratory. His academic background includes a Ph.D. from the University of Texas at Austin (2008) and a B.S. from Korea Advanced Institute of Science and Technology (KAIST) in 2004. Ryoo's research centers on deep learning and computer vision with specific focus on convolutional neural network (CNN) models for video semantic understanding. His work bridges visual perception and robotic action through applications in robot perception, robot learning, and human-robot interaction. Key innovations involve developing efficient architectures for processing multimodal data and translating visual understanding into robotic control systems. Analysis of his 15 most recent publications reveals strong emphasis on multimodal AI integration, particularly vision-language models applied to robotics. His 2025 work shows significant advancement in token-efficient video representation, motion-controllable diffusion models, and zero-shot learning frameworks specifically designed for robotic control systems. The research trajectory demonstrates consistent focus on making video understanding more accessible and applicable to real-world robotic scenarios. Scientific awards: No awards were mentioned in the provided source material. Regarding academic advising, the source text does not list any students or mentoring activities. Similarly, no grant funding information is provided, though his dual academic-industry role suggests substantial research support. His Google Brain affiliation likely involves industry-sponsored research initiatives. Ryoo maintains active laboratory affiliations through Stony Brook's AI Innovation Institute and Google's Robotics team. His prior work at NASA JPL indicates experience with space robotics systems, while his current Google role focuses on large-scale robot learning infrastructure. The LAM SIMULATOR project represents his current focus on advancing data generation techniques for training large action models.
Adnan Akhunzada is a prolific researcher with extensive contributions to computer science, particularly in artificial intelligence, cybersecurity, and internet of things. His work spans deep learning architectures, software defined networks, and security frameworks for emerging technologies. Research Interests include: Deep learning for micro-expression and image analysis Quantum control systems with reinforcement learning AI-based phishing and malware detection Federated learning for drone services Cryptographic protocols for UAV communications Publication Trends show expertise in: Hybrid neural network architectures Cybersecurity for industrial IoT Privacy-preserving crowdsourcing Sign language recognition datasets 5G-assisted cognitive communication
Jan-Matthias Braun is an Associate Professor at the Maersk Mc-Kinney Moller Institute, University of Southern Denmark, specializing in Applied AI and Data Science. His research bridges artificial intelligence, robotics, and medical device engineering. Current projects focus on explainable AI integration in colon capsule endoscopy Development of real-time FPGA-based systems for colorectal diagnostics Biomechanical modeling for adaptive orthotic devices His work emphasizes cross-disciplinary applications of machine learning in healthcare, particularly for gastrointestinal disease detection and assistive robotics. Publications demonstrate expertise in deep neural networks, hardware acceleration, and smart environment control systems. Teaching responsibilities include: Advanced cybersecurity courses Deep learning applications in epilepsy detection Mentorship in capsule endoscopy image analysis
Dr. Jing Jiang is an Associate Professor in the School of Computer Science and a core member of the Australian Artificial Intelligence Institute (AAII) at the University of Technology Sydney (UTS). As an ARC DECRA Fellow, she has secured over AU$2 million in research funding through multiple ARC grants, CSIRO/Data61 projects, and industry collaborations. Her work bridges theoretical advances in machine learning with practical applications across various domains. Dr. Jiang's research focuses on machine learning, particularly federated learning, reinforcement learning, and foundation models. She explores how to make these technologies work effectively in heterogeneous environments, addressing challenges like data privacy, non-IID data distributions, and efficient communication. Her work spans both theoretical foundations and practical implementations for real-world applications. Her publications demonstrate a strong trend toward personalized federated learning approaches, with significant contributions to recommender systems, time series analysis, and weather forecasting. She has developed novel techniques like variational autoencoder approaches for federated collaborative filtering and adaptive prompt learning for foundation models on devices. Dr. Jiang has received several notable recognitions: ARC DECRA Fellow Awardee of the Australian International Postgraduate Research Scholarship (IPRS) Dr. Jiang has successfully led multiple major research projects, including two ARC Discovery Projects, one ARC Linkage Project, and a CSIRO/Data61 CRP project where she served as lead Chief Investigator. She has supervised numerous PhD and Master's students and actively collaborates with industry partners on applied research. As a core member of the Australian Artificial Intelligence Institute (AAII) at UTS, Dr. Jiang contributes to a vibrant research ecosystem focused on cutting-edge AI research. She collaborates closely with Professor Guodong Long and other researchers on various machine learning projects, and serves in leadership roles including program co-chair for major conferences like ADMA2023.
Miroslav Janík, PhD, serves as Research Assistant at the University of Trier's Department of German Studies and Research Associate at Masaryk University's Faculty of Education, Research Institute for School Education. He advises German as a Second Language (DaZ) and German as a Foreign Language (DaF) degree programs while teaching core methodology courses on German language didactics and integrative language mediation. His academic profile bridges empirical research on multilingual education with practical teacher training initiatives across Central European contexts. His educational foundation includes: PhD in Education (Foreign Language Didactics) from Masaryk University (2011-2016) Master's degree in Teaching German as a Foreign Language and Social Studies (2009-2011) Bachelor's degree in Teaching German as a Foreign Language and Social Studies (2005-2009) Dr. Janík's research investigates the intersection of language policy, multilingual school realities, and teacher professionalization. He examines how language regimes operate within inclusive frameworks in Czech and Austrian urban schools, revealing tensions between institutional homogenization and linguistic diversity. His work employs innovative video-based methodologies and eye-tracking to analyze teacher cognition and classroom dynamics, contributing to evidence-based approaches for multilingual education. Analysis of his 2014-2024 publications shows consistent focus on Central European educational contexts, with recent work emphasizing practical implications of language policy implementation. His scholarship demonstrates growing engagement with teacher professional vision development and equity issues in linguistically diverse classrooms, reflecting evolving research priorities toward actionable interventions. Dr. Janík actively secures competitive research funding, currently leading the Nikolaus Koch Stiftung project (2024) supporting linguistically diverse students. His grant portfolio includes multiple Czech Science Foundation projects examining multilingual school spaces (2019-2022), teacher professional vision (2017-2019), and novice teacher socialization (2015-2017), demonstrating sustained research productivity and institutional trust. As an educator, he guides DaZ/DaF students through curriculum design and teaching methodologies while conducting classroom-based research that directly informs his instructional practice. His dual institutional appointments facilitate cross-pollination between theoretical research at Masaryk University and practical application at the University of Trier, creating a robust cycle of research-informed teaching and practice-based inquiry.
Bastien Perroy is an Industrial Cognitive Science Researcher based in Paris, France, leading the Passenger Cognition Lab at the RATP Group – the operator of 10 million daily public transport trips in Paris. He holds a Ph.D. in cognitive science from École des Hautes Études en Sciences Sociales (EHESS), completed at ENS Ulm's Institut Jean-Nicod Department of Cognitive Studies, where he continues to collaborate. Education: Ph.D. in Cognitive Science from EHESS His research bridges cognitive science and urban mobility, focusing on passenger perception, temporal disorientation, and crisis-induced cognitive disruptions. Key projects include: Developing a zero-cost, real-time social media bot questionnaire system for passenger sentiment analysis Engineering a 30 million tweet dataset using Spacy, CamemBERT, and GPT-4 for NLP analysis Conducting distress analysis during the Line 4 incident (2023) to improve service recovery Creating a psychometric instrument mapping 63 key messages across 9 clarity dimensions Investigating non-spatial disorientation during the pandemic through multi-method research His work has resulted in publications across diverse journals including Nature Scientific Reports , PLOS One , and the British Journal of Psychology . He emphasizes actionable insights for customer service, operational audits, and policy development, particularly through: Statistical models doubling disruption recovery time estimation precision Real-time KPI dashboards for executive decision-making Policy recommendations focused on social disorientation mitigation Currently seeking collaborations, conferences, and new projects in cognitive science and transportation psychology.
Dr. Saritha Unnikrishnan serves as a Lecturer in Computing and Principal Investigator in AI-driven Computer Vision at Atlantic Technological University (ATU) Sligo, Ireland. She maintains multiple research affiliations across the institution, including the Health and Biomedical Research Centre (HEAL) , the Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE) , and the Precision Engineering Materials and Manufacturing Research Centre (PEM Research Centre) . Dr. Unnikrishnan's research spans computer vision , medical imaging , and artificial intelligence applications with significant focus on healthcare and industrial quality assessment. Her work demonstrates strong interdisciplinary connections between computer science, biomedical engineering, and pharmaceutical sciences, particularly in the areas of micrograph analysis , brain tumor characterization , and AI-driven diagnostic solutions . Analysis of her recent publications reveals a clear trajectory toward applying AI techniques to solve complex problems in medical imaging and industrial applications. Her work increasingly focuses on deep learning approaches for image analysis, with notable contributions in glioma characterization , emulsion stability assessment , and educational technology solutions . Ireland's National AI Challenge 2024 award recipient Dr. Unnikrishnan has demonstrated exceptional grant acquisition capabilities, securing over €2 million in research funding to lead multiple national and EU projects. She has led major enterprise-funded AI research initiatives, including an AI-enabled computer vision solution licensed to GSK . Her collaborative work extends to European COST Actions and cross-border AI initiatives, highlighting her significant impact in the European research landscape. As Principal Investigator across multiple research centers at ATU Sligo, Dr. Unnikrishnan directs work in the Health and Biomedical Research Centre, the Mathematical Modelling and Intelligent Systems for Health and Environment initiative, and the Precision Engineering Materials and Manufacturing Research Centre, where she bridges computer science with practical healthcare and industrial applications.
Markku Kilpeläinen is a University Lecturer at the Department of Psychology, University of Helsinki, affiliated with the Faculty of Medicine. His work focuses on cognitive psychology, perception, and neuroscience, with particular emphasis on visual system research and human-computer interaction. Dr. Kilpeläinen's research explores: Cognitive abilities and their impact on computer task performance Retinal processing mechanisms in primate and human vision Sound-action and sound-space symbolic associations Comparative studies of human and AI visual recognition capabilities His publications since 2025 demonstrate active engagement in interdisciplinary research spanning psychology, neuroscience, and technology design. Recent work includes studies on parenting interventions in perinatal care and novel methods for XR device legibility testing. In collaborative projects, Dr. Kilpeläinen has contributed to: Human Optimized XR (Multisensory Signals and Meanings) International collaborations with institutions like UC Berkeley Business Finland-funded research initiatives
Roberto Vezzani is an Associate Professor at the University of Modena and Reggio Emilia's Enzo Ferrari Department of Engineering, specializing in information processing systems (ING-INF/05). Previously Director of the Artificial Intelligence Research and Innovation Center (2018-2021), he holds a PhD in Information Engineering from the same institution. As senior member of AimageLab, he coordinates research on human-computer interaction using multi-sensor systems. His research spans: Computer vision for IoT and video surveillance Motion detection and action classification 3D vision with depth/thermal/event cameras Sensor fusion and automatic video annotation He leads competitive projects funded by Toyota, Ferrari, and EU programs, focusing on industrial applications of computer vision. Recent publications (2024-2025) demonstrate strong focus on 3D pose estimation, robot perception, and efficient embedded vision systems, with applications in automotive, robotics, and UAVs. Awards include: Best Paper - ICPR 2020 (IAPR) Best Paper - VISAPP 2020 Best Paper - THEMIS'2008 Industrial collaborations feature multi-year projects with Ferrari (RedVision lab), Toyota Europe, and Tetra Pak. Teaching includes courses on Computer Architecture, IoT systems, and industrial AI.
Stuart Jonathan Russell is a Distinguished Professor of Computer Science, Cognitive Science, and Computational Precision Health at the University of California, Berkeley. He holds the Smith-Zadeh Chair in Engineering and is also a Professor of Computational Precision Health at the University of California, San Francisco. Russell is the founder and leader of the Center for Human-Compatible Artificial Intelligence (CHAI) at UC Berkeley and serves as an Honorary Fellow of Wadham College, Oxford. His academic journey began with a B.A. in Physics from the University of Oxford, followed by a Ph.D. in Computer Science from Stanford University. Throughout his distinguished career, Russell has received numerous prestigious honors including the IJCAI Computers and Thought Award (1995), IJCAI Award for Research Excellence (2022), Fellow of the Royal Society (2025), and Member of the National Academy of Engineering (2025). Russell's research spans multiple domains within artificial intelligence, with a recent focus on ensuring AI systems remain beneficial to humanity. His work includes significant contributions to machine learning, probabilistic reasoning, knowledge representation, planning, real-time decision making, and inverse reinforcement learning. In recent years, his research has increasingly focused on AI safety, value alignment, and developing frameworks for human-compatible AI systems that maintain human control as AI capabilities advance. His publication record shows a clear trend toward addressing the long-term challenges of AI development, particularly the control problem and value alignment. The research spans theoretical foundations of AI, practical applications in robotics and decision making, and critical examinations of the societal implications of increasingly capable AI systems. Russell's work has evolved from foundational AI research to increasingly focus on the alignment problem and mechanisms for ensuring AI systems remain beneficial. Russell has received numerous scientific awards recognizing his contributions to the field: IJCAI Computers and Thought Award (1995) AAAI Fellow (1997) ACM Fellow (2003) AAAS Fellow (2011) Blaise Pascal Chair (2012) Reith Lectures (2021) Officer of the Order of the British Empire (OBE) (2021) Fellow of the Royal Society (2025) Member of the National Academy of Engineering (2025) Russell has advised numerous doctoral students including Marie desJardins, Eric Xing, and Shlomo Zilberstein, and has mentored many postdoctoral researchers who have become leaders in the field. His research has been supported by various grants from organizations including the National Science Foundation, Defense Advanced Research Projects Agency, and other funding bodies focused on advancing AI research with careful consideration of safety and societal impact. He has been particularly active in securing funding for research on human-compatible AI and value alignment. He founded and leads the Center for Human-Compatible Artificial Intelligence (CHAI), which brings together researchers from multiple disciplines to address the challenge of creating AI systems that reliably do what humans want them to do. The center collaborates with other research groups including the Berkeley Artificial Intelligence Research (BAIR) lab, the Kavli Center for Ethics, Science, and the Public (KCESP), and the Institute for Cognitive and Brain Sciences (ICBS), creating a rich interdisciplinary environment for addressing the challenges of AI safety and human compatibility.
Paul Udoh serves as a Lecturer in Project Management at Aberdeen Business School, Robert Gordon University (RGU). With industry experience spanning healthcare and law sectors, he applies pragmatic project management expertise to translate business requirements into actionable plans through cross-functional team leadership and strategic risk mitigation. His academic credentials include: LLM MSc MAPM (Member of the Association for Project Management) Research centers on Risk Management and Change Management within project frameworks, with emerging focus on Artificial Intelligence integration in construction. His work investigates how AI-driven solutions enhance efficiency, reduce costs, and optimize decision-making in complex project environments through advanced predictive modeling and adaptive systems. His 2025 literature review analyzes AI's evolutionary trajectory in construction project management, identifying critical trends including machine learning for risk forecasting, computer vision for site monitoring, and natural language processing for document analysis. The research highlights growing industry adoption of digital twins and blockchain for transparency, while forecasting increased AI-human collaboration in future project ecosystems. No scientific awards or honors were documented in available materials. Professional activities include teaching Project Management Fundamentals (BSM084) and Personal/Professional Skills development (BSM260), though specific student supervision or grant funding details remain unreported in current sources. He contributes to RGU's Project Management Research Group, collaborating on industry-focused initiatives that bridge academic theory with practical construction management challenges through interdisciplinary partnerships.
