جست‌وجوی استاد و پژوهشگر

Jiebo Luoمشاهده پروفایل

استاد

• Computational social science
• Natural language processing
• Digital health
• +۴ مورد دیگر

Jiebo Luo is a Professor of Computer Science at the University of Rochester, where he has been since 2014. Prior to this, he spent over 15 years at Kodak Research Laboratories, rising to Senior Principal Scientist. His research spans computer vision, natural language processing, machine learning, and computational social science. BS and MS in Electrical Engineering from University of Science and Technology of China (USTC), 1989 and 1992 PhD in Electrical Engineering from University of Rochester, 1995 Dr. Luo's work focuses on bridging computer vision with social science through multimedia analysis, social media modeling, and digital health. His research includes innovative approaches to unsupervised learning, event recognition in videos, and multi-label classification. His publications highlight trends in deep learning for medical imaging, social media as sensors, and video analytics. Key subfields include semantic understanding, attention mechanisms, and multimodal fusion. 2021 ACM SIGMM Technical Achievement Award 2018 IEEE Region 1 Technological Innovation in Academic Award 2004 Eastman Innovation Award Fellowships: ACM, AAAI, IEEE, IAPR, SPIE Dr. Luo serves as Editor-in-Chief for IEEE Transactions on Multimedia (2020-2022) and has held leadership roles in multiple IEEE Technical Committees. He is a Data Science CoE Distinguished Researcher at the Goergen Institute for Data Science and a Board Member of the Greater Rochester Data Science Industry Consortium.

Paul Sowmanمشاهده پروفایل

استاد

• Neuroscience
• Psychology
• Cognitive Science
• +۳ مورد دیگر

Paul Sowman is a Professor and Honorary Professor at Macquarie University's School of Psychological Sciences. His affiliations include the Hearing Research Centre, Perception in Action Research Centre, and Centre for Language Sciences (CLaS). He holds an ORCID ID (0000-0002-3863-6675) and specializes in neuroscience, psychology, and cognitive science. His research focuses on neuroimaging techniques like magnetoencephalography (MEG), auditory perception, and brain mechanisms underlying speech disorders, sensory processing, and psychedelic effects. He leads projects on microdosing psilocybin for depression, interoceptive abilities in aesthetic appreciation, and computational neurosurgery. Key research interests include auditory environments' impact on autism traits, prefrontal cortex roles in learning acoustics, and hallucinogen-induced perceptual disorders. He has been awarded the Henry Guze Award (2018) for hypnosis-related research. His work spans collaborations in Australia, Europe, and beyond, addressing topics like sport-related concussion assessment, neurosurgical innovations, and language processing during natural conversations. Recent projects include MQRC Data Research Centre collaborations and studies on brain activity in rugby players. He has supervised 1 student and contributed over 119 research outputs, including peer-reviewed articles and datasets on auditory surprise decoding and room acoustics learning.

Hannah Hobsonمشاهده پروفایل

مدرس ارشد

• Psychology
• Developmental Disorders
• Neuroscience
• +۳ مورد دیگر

Dr. Hannah Hobson is a Senior Lecturer in the Department of Psychology at the University of York. Her research focuses on language and communication challenges in developmental and acquired conditions, including autism spectrum disorder, developmental language disorder, and stroke survivors. She has a particular interest in how language interacts with emotional processing and the neural systems underlying social cognition. She also advocates for reproducible and ethical research practices in psychology. Education: Bachelor of Arts in Experimental Psychology, University of Oxford (2009-2012) Doctor of Philosophy (DPhil) in Experimental Psychology, University of Oxford (2012-2016) Research Interests: Language disorders and their impact on emotional and social functioning Neural mechanisms of imitation and social interaction Alexithymia and its relationship to language Improving reproducibility in psychological research Recent Articles Focus: Her work examines intersections between language impairment, neurodiversity, and social cognition, with recent studies addressing topics like mask-wearing effects on autistic individuals, developmental language disorder in legal contexts, and participatory autism research practices. Teaching & Service: Leads the Clinical Research Methods module in the Developmental Disorders and Clinical Practice MSc. Actively involved in editorial roles promoting open science. Collaborations: Works with researchers at City University, University of Bath, and University of Warwick, focusing on developmental and clinical psychology.

Michaela Regneriمشاهده پروفایل

پژوهشگر

• Machine Learning
• Natural Language Processing
• AI Ethics
• +۳ مورد دیگر

Dr. Michaela Regneri is a Senior Researcher at the Department of Informatics, University of Hamburg, affiliated with the Machine Learning group. She holds a PhD in Computer Science and focuses on interdisciplinary research at the intersection of artificial intelligence, computational linguistics, and ethical data practices. Her work emphasizes data minimalism, sustainable AI development, and clinical NLP applications for neurodiversity analysis. Research interests include: AI ethics and societal impact Resource-conscious machine learning Natural language processing for clinical narratives Knowledge graph construction Investigative data journalism tools Her recent articles explore topics like violence detection in ancient texts, conceptual abstraction in LLMs, and ethical frameworks for data usage. Notable projects include the new/s/leak visualization tool for journalists and the Seedling corpus development initiative. She actively contributes to low-resource language technologies and autism spectrum disorder discourse analysis. Professional activities include supervision of student projects and participation in interdisciplinary collaborations. Her work bridges technical innovation with critical societal discourse, particularly addressing German cultural skepticism towards AI development.

• Speech Processing
• Machine Learning
• Affective Computing
• +۶ مورد دیگر

Professor Julien Epps is the Dean of the Faculty of Engineering at the University of New South Wales (UNSW). He holds a Professorship and previously served as Head of the School of Electrical Engineering and Telecommunications. His academic journey includes roles as Senior Lecturer (2007), Associate Professor (2014), and Head of School (2019). Epps earned his BE (Hons I) and PhD in Electrical Engineering from UNSW in 1997 and 2001, respectively. His research focuses on speech processing, wearable sensor technology, affective computing, and mental health applications. Notable contributions include work on depression detection via speech analysis, multimodal emotion recognition, and the development of tools like the IREye4Task dataset for wearable mental state analysis. He also investigates human-computer interaction through eye-tracking and biometric sensors. Epps' publications (2021–2025) emphasize speech-based health screening, adaptive signal processing for cybersecurity, and the integration of large language models with acoustic landmarks. His work spans engineering education, including a pioneering undergraduate quantum engineering degree program introduced in 2023. His lab and team collaborations include projects on smartphone-based depression screening, real-time task load estimation, and the design of robust spoofing attack detection systems. Epps has led interdisciplinary efforts in healthcare technology and pedagogical innovation in engineering education.

• Information Retrieval
• Machine Learning
• Big Data Stream Processing
• +۶ مورد دیگر

Dr. Richard McCreadie is a Senior Lecturer in Computing Science at the University of Glasgow, specializing in Information Retrieval, Machine Learning, and Digital Finance. His research focuses on real-time data processing, financial asset recommendation systems, and crisis informatics. He leads the Information Retrieval Group and co-chairs the TREC Incident Streams initiative, advancing methods for actionable information extraction from social media during emergencies. Education: PhD in News Vertical Search using User-Generated Content (2012). Active collaborations include the FAR-AI platform for investment recommendations and the Event Tracker system for disaster response. Research Interests: His work bridges academia and industry, emphasizing practical applications in fintech, social media analysis, and event detection. Recent trends include leveraging large language models (LLMs) for personalized financial advising and robust vision transformers for crisis image classification. Grants & Projects: Key contributions include the TREC-IS track evaluations, Agile IR experimentation frameworks with Terrier, and the SUPER project for emergency management using social sensors. He has led over 75 publications in top venues like SIGIR and ECIR. Labs/Teams: Part of the Information, Data and Analytics section and collaborates internationally on AI-driven financial systems and disaster informatics platforms.

Linda Petzoldمشاهده پروفایل

استاد

• Computational Science and Engineering
• Systems Biology
• Bioengineering
• +۷ مورد دیگر

Linda Petzold is the Mehrabian Distinguished Professor in Computer Science and Mechanical Engineering at the University of California, Santa Barbara (UCSB). Her roles span academic leadership, research, and interdisciplinary collaboration. She holds affiliations with the National Academy of Engineering, National Academy of Sciences, and multiple UCSB centers including the Institute for Collaborative Biotechnologies, Center for Bioengineering, and Center for Control, Dynamical Systems & Computation. Dr. Petzold earned her PhD and BA in Computer Science and Mathematics from the University of Illinois. Her research focuses on modeling, simulation, and software development for multiscale systems in biology, materials, and social networks. Notable projects include the Stochastic Simulation Service (StochSS), a cloud-based platform for biological modeling, and collaborations on topics like circadian rhythms, coagulopathy, and social sentiment analysis. She also leads work in computational neuroscience, including studies of neuronal activity in brain organoids. Her scientific contributions are recognized through prestigious awards, including the Sidney Fernbach Award and ACM Prize in Computational Science. Petzold’s research emphasizes bridging computational methods with experimental data, fostering interdisciplinary partnerships to address complex challenges in healthcare, ecology, and materials science. Her lab develops algorithms for stochastic systems, with applications ranging from trauma patient outcomes to fibrinolytic mechanisms. Publications highlight advancements in numerical methods, machine learning, and systems biology. Key themes include neural ordinary differential equations, prompt tuning for LLMs, and multimodal scientific datasets. Her work integrates computational tools with real-world biomedical and ecological problems, reflecting a commitment to translational research. Dr. Petzold advises students and collaborates extensively, with grants supporting projects like Bayesian neural networks for parameter inference and GAN-based imputation for medical data. Her leadership in academic and professional societies underscores her role as a visionary in computational science and engineering.

Brendon Clarkمشاهده پروفایل

دانشیار

• Design Anthropology
• Participatory Design
• Decolonial Design
• +۳ مورد دیگر

Brendon Clark is an Associate Professor in design anthropology at the Umeå Institute of Design (UID), part of Umeå University in Sweden. His work bridges design practice, anthropology, and participatory methodologies to address complex societal challenges. Prior to UID, he spent eight years as a senior researcher at the Interactive Institute (now RISE), leading design teams in Stockholm. Clark holds a PhD in User-Centered Design from the University of Southern Denmark, following an MA in Anthropology and BA in Social Sciences. His research focuses on how designers collaboratively engage people and materials to reshape daily life through improvisational and relational practices. Key areas include decolonial design ethics, language learning pedagogies, and participatory frameworks that dismantle oppressive systems. Notable projects include the Walking Decolonially through Design for Sustainabilities initiative (2017-2022), exploring anti-oppressive design directions. Publications highlight his exploration of hybrid design education models, narrative-based collaborative practices, and tangible future scenario-building. His work frequently intersects with performance theory and critical design activism, advocating for more equitable and contextually responsive design processes. Clark’s career reflects a commitment to applied research with societal impact, spanning academic, corporate, and community development contexts. His current role at UID emphasizes fostering innovative design education that integrates interdisciplinary methods and ethical considerations.

Wai Lok Wooمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +۷ مورد دیگر

Prof Wai Lok Woo is a Chair in Machine Learning at Northumbria University's Computer and Information Sciences Department. He serves as Faculty Postgraduate Research Director (Engineering and Environment) and Head of the Artificial Intelligence and Digital Technology Research Cluster. With a PhD in Statistics and Machine Learning from Newcastle University, he has over 450 publications and holds IET and IEEE Fellowships. Education PhD in Statistics and Machine Learning, Newcastle University, UK MSc in Communications Engineering, Newcastle University, UK BEng (Hons) in Electrical and Electronic Engineering, Newcastle University, UK Research Interests His work focuses on machine learning foundations, signal/image processing, and smart sensing for applications like non-destructive testing, energy systems, and mental health monitoring. Core themes include: AI & Machine Learning: Deep neural networks, ethical AI, reinforcement learning Data Science: Latent variable analysis, blind signal separation Sustainable Development: Smart grids, precision farming, disaster resilience Awards Fellow of IET and IEEE IEE Prize British Commonwealth Scholarship Grants & Projects Leads the EU-funded CO VER project (€6M) on UAV-based emergency response systems. Active in UK/EU grant evaluations and has supervised 8 PhD students. Labs & Teams Directs the AI & Digital Technology Research Cluster, collaborating globally on UN SDGs like affordable energy and climate action.

Luo Jieboمشاهده پروفایل

استاد

• Computer Vision
• Natural Language Processing
• Machine Learning
• +۴ مورد دیگر

Jiebo Luo is a Professor of Computer Science at the University of Rochester, where he has held this position since 2014. He earned his BS and MS in Electrical Engineering from the University of Science and Technology of China (1989 and 1992) and a PhD in Electrical Engineering from the University of Rochester (1995). Prior to academia, he spent 15+ years at Kodak Research Laboratories as a Senior Principal Scientist. His research focuses on computational social science, natural language processing, digital health, computer vision, data mining, and multimedia. Dr. Luo’s work has been recognized through numerous awards, including the ACM SIGMM Technical Achievement Award (2021), Fellowships from ACM, AAAI, IEEE, IAPR, and SPIE. He has authored over 500 peer-reviewed papers, holds 90+ patents, and serves as Editor-in-Chief of the IEEE Transactions on Multimedia. He actively contributes to conference organization (e.g., ACM Multimedia, CVPR) and editorial roles for top journals. Key contributions include pioneering work in social media analytics, sentiment analysis, and digital health, as well as foundational research in multi-label classification and action recognition datasets like UCF 101. His labs and collaborations span the Goergen Institute for Data Science and the Greater Rochester Data Science Industry Consortium.

Ifeoma Nwoguمشاهده پروفایل

دانشیار

• Human Behavior Modeling
• Sign Language Understanding
• Probabilistic Modeling
• +۵ مورد دیگر

Ifeoma Nwogu is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo. She holds an appointment in the School of Engineering and Applied Sciences, specializing in research areas including human behavior modeling, sign language understanding, and probabilistic modeling. Her work spans computer vision, robotics, and AI with applications in healthcare, education, and social signal processing. Her research focuses on advancing technologies for sign language interpretation, infant vocal analysis, and human-robot interaction. Key projects include developing automated code generation frameworks for social robots, 3D motion synthesis systems for sign language avatars, and methodologies for emotion recognition in facial interactions. She teaches courses such as CSE 455/555 (Pattern Recognition), CSE 610 (Special Topics), and CSE 706 (Seminars), reflecting her expertise in AI and machine learning. Nwogu's recent publications emphasize cross-disciplinary applications of machine learning, including medical imaging anonymization, multi-agent systems, and ethical considerations in data privacy. Her work bridges computational methods with societal needs, particularly in education accessibility for deaf communities and stroke rehabilitation technologies.

Yezhou Yangمشاهده پروفایل

دانشیار

• Computer Vision
• Artificial Intelligence
• Robotics
• +۴ مورد دیگر

Dr. Yezhou (YZ) Yang is a tenured Associate Professor in the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU). He leads the ASU Active Perception Group , focusing on Cognitive Robotics, Computer Vision, and Generative AI . His work integrates vision, deep learning, and AI to interpret human actions and environmental geometry. Education: Ph.D. Computer Science, University of Maryland, College Park (2016) M.S. Computer Science, University of Maryland, College Park (2013) B.E. Computer Science and Engineering, Zhejiang University, China (2010) Research Interests: Dr. Yang’s research emphasizes Visual Recognition with Knowledge (VR-K) , Autonomous Vehicles , and Multimodal Learning . Key projects include: - Developing Secure and Decentralized Generative Models (NSF SaTC Grant) - Enhancing Robustness in Vision-Language Tasks (NSF Robust Intelligence Grant) - Traffic Safety Assessment via Infrastructure Cameras (Institute of Automated Mobility) Publications: Recent work spans Text-to-Image Generation (e.g., RefEdit), Event-based Vision (e.g., SEVD Dataset), and Connected Autonomous Vehicles (e.g., CAROM). His team’s contributions to benchmarks like VL-GLUE and ConceptBed advance multimodal reasoning and model evaluation. Awards & Recognition: NSF CAREER Award (2020) Amazon AWS ML Research Award (2019) Co-founder of ARGOS Vision Inc. Lab & Teams: The ASU Active Perception Group collaborates with industry (e.g., Arizona DOT) and academic partners. Ongoing projects include Robust Multimodal Foundational Models and Socially-Adept Autonomous Vehicles .

• Machine Learning
• Deep Learning
• Neural Memory Networks
• +۳ مورد دیگر

Dr. Tharindu Fernando Warnakulasuriya is a Research Fellow at Queensland University of Technology (QUT), affiliated with the School of Electrical Engineering & Robotics. He holds a PhD from QUT and a BSc (Special Degree in Computer Science) from the University of Peradeniya, Sri Lanka. His primary research focuses on Machine Learning, Deep Learning, Neural Memory Networks, Computer Vision, and Biomedical Signal Processing. He has received several awards, including the QUT Outstanding Doctoral Thesis Award (2019), QUT Early Career Researcher Award (2022), and EER Rising Star Award (2023). His work bridges interdisciplinary fields such as healthcare, neuroscience, and autonomous systems, with notable contributions to medical anomaly detection, autonomous vehicle behavior prediction, and surveillance systems. Teaching roles include coordinating EGH404 and lecturing in EGB103. He supervises PhD students in areas like deep learning for biosignal analysis and multimodal perception. His research outputs span over 50 publications in top-tier journals like IEEE Transactions and ACM Computing Surveys. Key collaborations include projects on neural memory plasticity, autonomous steering, and stress detection using wearable devices. He actively reviews for journals like IEEE Transactions on Neural Networks and Learning Systems, and is part of professional networks like IEEE.

Olov Anderssonمشاهده پروفایل

استادیار

• Autonomous Systems
• Robotics
• Artificial Intelligence
• +۵ مورد دیگر

Olov Andersson is an Assistant Professor and WASP Fellow in AI for Autonomous Systems at KTH Royal Institute of Technology, leading the Division of Robotics, Perception and Learning. His research focuses on Embodied AI for autonomous robots and vehicles, combining advancements in Vision-Language Models (VLM), Large Language Models (LLM), and real-world navigation challenges. Key projects include the DARPA SubT Challenge-winning team CERBERUS and the EU H2020 Heron project for robotic road repair. He supervises multiple PhD students and postdocs, including Timon Homberger, Finn Lukas Busch, and Jesper Eriksson. Research interests emphasize full-stack autonomy in dynamic environments, including planning, mapping, and navigation. Notable contributions include the OneMap real-time open-vocabulary mapping system and self-supervised scene flow methods like Seflow. He has been recognized for technical leadership in autonomous systems through awards like the WASP Fellowship. Professional activities include co-chairing the 2024 IROS workshop on robot perception in dynamic environments and advising the Swedish Prime Minister’s AI initiative. Teaching roles span multiple graduate courses in machine learning, robotics, and systems engineering at KTH.

David Clausiمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Pattern Recognition
• +۲ مورد دیگر

David Clausi is a Professor in the Department of Systems Design Engineering at the University of Waterloo. His research focuses on computer vision, image processing, and pattern recognition, with applications in SAR imagery and biomedical imaging. He leads the Vision and Image Processing (VIP) Lab, which explores AI, transparent AI systems, and scalable solutions. He has supervised numerous graduate and undergraduate students, contributing to advancements in medical imaging, SAR sea ice classification, and sports analytics. Awards include Teaching Excellence recognitions and a Research Excellence Award. His work spans publications in remote sensing, medical imaging, and computer vision, with a focus on real-world applications like environmental monitoring and healthcare. Education: Ph.D., P.Eng. (details not explicitly provided, inferred from titles). Research Interests include automated image interpretation, SAR and biomedical imagery analysis, and AI ethics. His lab emphasizes scalable AI and transparent AI explanations. Recent articles address sea ice mapping, hockey analytics, and wildlife detection using advanced machine learning techniques. Awards highlight contributions to research and teaching excellence. Advising spans over 30 students, with notable theses in SAR segmentation, medical image analysis, and computer vision. The VIP Lab collaborates on projects like the AI4Arctic initiative and hockey analytics tools like PuckNet and GoalieNet.