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Matthew F. GlasserView profile

Assistant Professor

• Neuroanatomy
• Connectomics
• Medical Image Analysis
• +5 more

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.

• Large Language Models
• Generative AI
• Fuzzy Cognitive Maps
• +7 more

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 CamarasaView profile

Senior Lecturer

• Robotics
• Artificial Intelligence
• Computer Vision
• +5 more

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.

• Cognitive Aging
• Gerontology
• Human Factors
• +5 more

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 LiView profile

Assistant Professor

• Natural Language Processing
• Machine Learning
• Commonsense Reasoning
• +4 more

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

Kenneth MarinoView profile

Assistant Professor

• Computer Vision
• Natural Language Processing
• Reinforcement Learning
• +4 more

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).

Francesco RagusaView profile

Research Fellow

• Computer Vision
• Pattern Recognition
• Machine Learning
• +5 more

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.

• Educational Technology
• Learning Analytics
• Artificial Intelligence in Education
• +3 more

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.

• Robotics
• Precision Agriculture
• Cyber-Physical Systems
• +5 more

Upinder Kaur is an Assistant Professor in the Department of Agricultural & Biological Engineering at Purdue University , part of the College of Engineering. Her research focuses on robotics, precision agriculture, and cyber-physical systems, with a strong emphasis on integrating data science and digital technologies into agricultural practices. Her work spans areas such as agricultural robotics , cybersecurity for robotic systems , and animal health monitoring . Notable projects include developing multimodal datasets for animal-robot interaction and creating self-powered in-vivo sensing systems for precision dairy farming. She also explores cybersecurity solutions to protect robotic networks against emerging threats. Her articles highlight a trend toward cross-disciplinary innovation , blending robotics with agriculture, healthcare, and cybersecurity. Recent work demonstrates advancements in soft robotics design, zero-day malware detection frameworks, and traceability systems for commodity supply chains. No scientific awards are listed, though her contributions to precision agriculture and robotic cybersecurity are significant. Advising and grants details are currently unavailable, but her research aligns with Purdue's focus on sustainable and technology-driven solutions for modern agricultural challenges.

• Interaction Design
• Music Technology
• Live Coding
• +3 more

Professor Palle Dahlstedt is affiliated with the University of Gothenburg's Interaction Design department. His work bridges music technology, live coding, and interdisciplinary performance. He specializes in gestural interactions, algorithmic creativity, and systems for collaborative improvisation. Key projects include the Bucket System, OtoKin, and research on live coding frameworks. Research interests focus on creative technologies in music and performance, with emphasis on real-time systems, human-computer interaction, and artistic collaboration. Dahlstedt has published extensively in venues like NIME, ICLC, and Evolutionary Intelligence, addressing topics ranging from hybrid piano design to generative storytelling. Notable contributions include the Biosphere Code Manifesto (2015), exploring algorithms in environmental contexts, and the Electroacoustic Modular Ecosystem (2020). His work often involves cross-disciplinary collaborations with dancers, musicians, and technologists. Performance highlights include jury-selected NIME performances (2015) and collaborations with artists like Gino Robair and Tim Perkis. Dahlstedt actively participates in international festivals and conferences, advancing the field of computational creativity and artistic research.

Sandra OkitaView profile

Associate Professor

• Educational Technology
• Human-Robot Interaction
• Virtual Reality for Learning
• +3 more

Sandra Okita is an Associate Professor at Teachers College, Columbia University , where she serves as Program Director in the Communication, Media, and Learning Technologies Design program within the Department of Mathematics, Science and Technology . She holds dual PhDs from Stanford University (Learning Sciences) and Keio University (Human-Computer Interaction). Research Focus : Developing pedagogical agents and virtual environments that enhance learning through social interaction Designing sociable robots as peer learners to study cognition and collaborative learning Exploring virtual reality and mixed reality for science education Theoretical work on self-other monitoring , learning by teaching , and recursive feedback Publication Trends : 15+ publications (2004-2022) on robotics in education, virtual learning environments, and social learning theories Key areas: Human-Robot Interaction , STEM Education , Metacognition , and Technology-Enhanced Learning Notable works: Learning by Teaching , Recursive Feedback Mechanisms , and Therapeutic Robots Awards & Grants : 15+ research grants (2001-2026) from NSF , Google VR Research Program , and Honda Research Institute Recipient of Dean's Faculty Diversity Research Award (2011-2012) 2008 Best Paper Award at International Conference of the Learning Sciences

Kim BarakaView profile

Assistant Professor

• Artificial Intelligence
• Human-Robot Interaction
• Reinforcement Learning
• +3 more

Kim Baraka is an Assistant Professor at the Faculty of Science, Vrije Universiteit Amsterdam, affiliated with the Artificial Intelligence department, Network Institute, and Social AI division. His research focuses on Human-Robot Interaction, Reinforcement Learning, and socially intelligent systems. He co-developed frameworks like SHARPIE for Human-AI collaboration and explores ethical aspects of AI teamwork. Baraka teaches courses on Human-interactive Agent Learning, Robotics, and Socially Intelligent Robotics. He actively contributes to conferences like HRI and ROMAN, publishing on topics ranging from robot curriculum learning to empathetic AI design. Ancillary roles include director of Bara-kadance and board member of Stichting Triplets. Research interests include: Human-AI co-creativity and collaboration Robot learning through human demonstrations Emotional expression in embodied AI Ethical frameworks for human-agent teams Recent work highlights multimodal interaction (e.g., audio-visual speech recognition), prosody-based teaching signals, and proxemics-aware navigation. His 2025 publications emphasize iterative algorithm design for fairness in human-agent teams and systematic reviews of collaborative AI creativity. This research bridges technical advancements with socially responsible AI deployment.

Timo GerkmannView profile

Professor

• Statistical Signal Processing
• Machine Learning
• Speech and Audio Processing
• +3 more

Prof. Timo Gerkmann is a Professor at the University of Hamburg's Department of Informatics, leading the Signal Processing Research Group. His research focuses on statistical signal processing and machine learning for speech and audio applications, including communication devices, hearing aids, audiovisual media, and human-machine interfaces. He previously held roles at Technicolor Research & Innovation, KTH Royal Institute of Technology, and Siemens Corporate Research. His work emphasizes generative models, diffusion-based approaches, and acoustic signal enhancement. He currently serves as Senior Area Editor of the IEEE/ACM Transactions on Audio, Speech, and Language Processing. Research Interests: Statistical Signal Processing for Speech and Audio Machine Learning Applications in Acoustic Environments Diffusion Models for Audio Restoration Audio-Visual Speech Enhancement Human-Machine Interaction Systems Acoustic Scene Analysis Publications Highlight Trends: Recent works focus on diffusion models for speech enhancement, generative approaches to dereverberation, and audiovisual multimodal analysis. He has pioneered frameworks like ReverbFX datasets and FlowDec codecs, emphasizing perceptual quality and unsupervised domain adaptation. Advising & Grants: While no specific students or grants are listed, his research group actively publishes in top venues, indicating sustained academic contributions. His work bridges theoretical signal processing with applied systems engineering. Labs/Teams: Leads the Signal Processing (SP) Research Group at UHH, specializing in cutting-edge audio technologies and human-centric signal processing solutions.

Michelle AldridgeView profile

Teaching Professor

• Autism Spectrum Disorders
• Pediatric Motor Speech Disorders
• Speech Sound Disorders
• +4 more

Michelle Aldridge serves as Professor of Instruction and Program Head for the Speech-Language Pathology MS and Speech, Language, and Hearing Sciences BS programs within the Department of Speech, Language, and Hearing at the School of Behavioral and Brain Sciences, University of Texas at Dallas. Her academic credentials include: Ph.D. in Human Development and Communication Sciences, University of Texas at Dallas (1997) M.S. in Communication Disorders, University of Texas at Dallas (1995) B.S. in Psychology, Texas Woman's University (1992) Dr. Aldridge's research focuses on early communication development in typical and atypical populations, with dual emphases on infant speech perception (vowel categorization, intermodal representation) and autism-related language/cognitive development . Her work bridges foundational neuroscience with clinical applications for pediatric speech disorders including motor speech impairments and speech sound disorders, informing early intervention strategies for autism spectrum disorders. Publication analysis reveals an evolutionary trajectory from basic infant speech processing research (1994-1999) toward applied autism studies (2000-2001), demonstrating how newborn perception mechanisms inform understanding of communication breakdowns in autism. Recognition includes: Callier Excellence in Education award (4K, 1999) She directs the Follow-Up study of Children in the Preverbal Program and oversees academic operations for two degree programs. While not currently accepting undergraduate researchers, her Program Head role involves extensive graduate mentorship in speech-language pathology. Professional engagement spans the American Speech-Language Hearing Association, Texas Speech-Language Hearing Association, Autism Society of America, and International Society for Infant Studies.

• Supply Chain Management
• Sustainable Operations
• Logistics Innovation
• +3 more

Dr Michael Wang is Senior Lecturer in Sustainable Operations & Supply Chain Management and Course Director for MSc Logistics & Supply Chain Management at Kingston University. His research explores human-centered operations, supply chain digitalization, and sustainability, with recent work addressing Industry 5.0, logistics resilience, and low-carbon transitions. Research Trends: Recent publications emphasize the intersection of human factors and technology in supply chains, with consistent focus on sustainability metrics and risk mitigation. Articles frequently analyze how workforce engagement, digital tools, and agile methodologies enhance operational resilience amid disruptions like extreme weather and geopolitical shifts. Professional Credentials: Chartered Member (CILT) in UK/AU/NZ/UAE Fellow of Higher Education Academy Member of Royal Society NZ