جست‌وجوی استاد و پژوهشگر

Hanseok Koمشاهده پروفایل

استاد

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

Hanseok Ko is a Professor and Newton-Bennett Endowed Chair of Engineering at The Catholic University of America's College of Engineering, Physics, and Computing. Internationally recognized for his expertise in artificial intelligence, signal processing, and intelligent systems, he leads the Multimodal AI Laboratory and has authored over 700 publications with 83 patents. His work bridges acoustics, speech, and image processing with human-machine interaction for robotics and healthcare. Ph.D. in Electrical Engineering, The Catholic University of America M.S. in Systems Engineering, University of Maryland, College Park M.S. in Electrical and Computer Engineering, The Johns Hopkins University B.S. in Electrical Engineering, Carnegie-Mellon University Dr. Ko's research emphasizes secure and efficient AI systems, including innovations in red-teaming large language models and streamlined scene graph generation architectures. His recent work aligns with Catholic University's strategic focus on principled artificial intelligence development. At ICASSP 2025, he presented two groundbreaking papers while serving as a member of the IEEE SPS Conference Board and General Chair for IEEE ICASSP 2024. His leadership extends to establishing an interdisciplinary AI research center at Catholic University. Fellow of the International Speech Communication Association (2023) Fellow of the Institution of Engineering and Technology (2014) IEEE Signal Processing Society Distinguished Lecturer (2025–2026) Former President of the Acoustical Society of Korea Over his career, Dr. Ko has supervised 43 Ph.D. and 110 M.S. graduates. His lab's research is funded by national agencies and industry partners, focusing on multimodal signal processing, generative-AI, and applications in robotics and healthcare.

Andrei C. Şerbanمشاهده پروفایل

مدرس

• Theatre Studies
• Literary Analysis
• Contemporary European Society
• +۳ مورد دیگر

Andrei C. Şerban serves as a Lecturer at the Lucian Blaga University of Sibiu , affiliated with the Faculty of Letters and Arts , specifically within the Department of Theatre Arts . His academic work bridges theatre studies with literary and cultural analysis, focusing on intermediality, medieval symbolism, and contemporary European drama. He has contributed extensively to understanding the intersections of poetry, drama, and visual aesthetics, particularly in Romanian and Francophone contexts. Şerban's research interests span the reception of classical texts in modern theatre , political allegory , and transnational cultural dialogues . His publications frequently analyze adaptations of Shakespeare, Dante, Ibsen, and Molière, while also exploring feminist and postcolonial theatre trends. He has curated anthologies of contemporary poetry and drama, emphasizing experimental forms and cross-cultural influences. His recent academic output highlights the integration of medieval imagery in modern performance , interdisciplinary approaches to theatrical space , and critical interpretations of European cinema . While no formal awards or students are mentioned in the provided materials, his editorial work on Francophone and African theatre underscores his commitment to global cultural exchange. Şerban also contributes to documenting major theatre festivals and avant-garde movements.

• Mixed Reality
• Context-aware Computing
• Human-Computer Interaction
• +۷ مورد دیگر

Kimberly Garcia is a researcher at the University of St. Gallen, focusing on mixed reality systems, contextual understanding, and explainable AI frameworks. Her work bridges human-computer interaction with industrial applications through projects like FoodCoach (automated diet counseling), GEAR (gaze-enabled AR feedback), and BLEARVIS (object identification in ubiquitous environments). Research Focus: Context-aware mixed reality interfaces Knowledge graph construction for environmental and health domains Explainable AI for cyber-physical systems Temporal scene understanding with contextual identifiers Recent Publications Highlight: Cross-domain applications of machine learning in AR systems, sensor fusion for object identification, and semantic tools for greenhouse gas accounting. All publications demonstrate interdisciplinary collaboration with computer scientists and domain experts. Key Collaborations: Regular co-authorship with Simon Mayer and Jannis Rene Strecker on human-AI interaction frameworks and sensor-based systems.

• Conversational AI
• Natural Language Processing (NLP)
• Human-Robot Interaction (HRI)
• +۵ مورد دیگر

Dr Yanchao Yu is a Lecturer at the School of Computing Engineering and the Built Environment of Edinburgh Napier University. He earned a BSc from Beijing Information Science and Technology University, an MSc in Software Engineering (Distinction), and a PhD in Artificial Intelligence from Heriot-Watt University (UK). PhD in Artificial Intelligence (Heriot-Watt University) MSc in Software Engineering (Distinction) BSc in Computer Science His research focuses on building smart agents and robots that assist humans in daily tasks through natural conversations. Key areas include: Natural Language Understanding (NLU), Generation (NLG), and Dialogue Management (DM) Visually grounded language learning Human-Robot Interaction (HRI) Machine learning for interactive systems Recent publications emphasize context-aware dialogue systems, visual scene understanding, and ethical challenges in HRI. He leads the EPSRC-funded project Learning from Collaborative Storytelling (2025-2028, £439,581) to enhance robot perception through interactive storytelling. Director of Studies for Lewis Watson's PhD on AI-Driven Conversational Storytelling Co-supervisor for multiple PhD projects including medical training robotics and machine unlearning Previously worked at Heriot-Watt's Interaction Lab (postdoc) and industry roles at Voysis (acquired by Apple) and Alana AI.

• Machine Learning
• Computer Vision
• Signal Processing
• +۳ مورد دیگر

Chenguang Liu is a researcher at Institut Polytechnique de Paris , France, with a focus on computational methods, machine learning, and control systems. His work bridges theoretical and applied research across multiple domains including computer vision, signal processing, and operations research. Key research areas: Machine Learning, Computer Vision, Computational Physics, Control Theory Notable publication trends: Develops novel algorithms for parallel computing, peridynamic modeling, and real-time systems Recent contributions include Bayesian neural networks for gas-bearing prediction, multi-agent reinforcement learning for UAV swarms, and domain adaptation techniques in object detection. He collaborates extensively with researchers in electrical engineering and applied mathematics disciplines.

Bernhard U. Seeberمشاهده پروفایل

استاد

• Auditory perception
• Computational models
• Binaural hearing
• +۵ مورد دیگر

Prof. Dr. Bernhard U. Seeber is a faculty member at the Technical University of Munich (TUM) in the Department of Electrical Engineering and Information Technology. He specializes in audio information processing, focusing on auditory perception, computational modeling, and hearing device technologies. Academic Rank: Professor Primary Affiliation: TUM Department of Electrical Engineering and Information Technology Research Interests: Auditory scene analysis, cochlear implants, binaural hearing, room acoustics, and signal processing for hearing aids His recent publications demonstrate expertise in auditory nerve modeling, spatial hearing optimization, and audio-visual simulation environments. While no formal awards or student advisement information are available in the provided texts, his work has significant implications for improving hearing technologies and understanding auditory perception mechanisms.

• Artificial Intelligence
• Multimedia Systems
• Robotic Interaction
• +۵ مورد دیگر

Wang Yufei is affiliated with Xiamen University, China, holding positions across the School of Informatics, Institute of Artificial Intelligence, Center for Digital Media Computing, and School of Film. The research spans interdisciplinary domains, integrating artificial intelligence, robotics, and multimedia systems for real-world applications. Wang Yufei's research primarily focuses on artificial intelligence , computer vision , robotic interaction , and multimedia systems . Key areas include 3D scene modeling using Gaussian splatting, diffusion models for storytelling and image generation, reinforcement learning for interactive instruction, and AI applications in healthcare, agriculture, and urban planning. The work emphasizes robustness, real-time feedback, and human-centered design. The recent publications demonstrate a strong trend in deep learning applied to vision and multimodal systems , with increasing focus on interactive AI , embodied environments , and domain adaptation . Topics such as differentiable trajectory optimization, backdoor attacks in compression models, and context-aware assistance systems reflect both technical depth and societal relevance. Wang Yufei has not been mentioned in relation to any scientific awards in the provided text. No information is available regarding student advising or research grants. However, the breadth and volume of publications suggest active research leadership and likely involvement in funded projects. Research is conducted in collaboration with teams at Xiamen University, particularly within the Institute of Artificial Intelligence and the Center for Digital Media Computing, focusing on intelligent multimedia and robotic systems. The integration of AI with cultural and educational applications, such as piano instruction and heritage preservation, highlights a unique interdisciplinary lab environment.

Alexander Hermansمشاهده پروفایل

پژوهشگر

• Computer Vision
• Deep Learning
• Robotics
• +۷ مورد دیگر

Dr. Alexander Hermans is a researcher at the Institute for Vision and Graphics, Faculty of Electrical Engineering and Information Technology, RWTH Aachen University. He is actively engaged in cutting-edge research at the intersection of computer vision, deep learning, and robotics, with a focus on 3D perception, segmentation, and anomaly detection. Research Interests: His primary research areas include Computer Vision , Deep Learning , Robotics , 3D Scene Understanding , Semantic and Instance Segmentation , and LiDAR-based Perception . His work often addresses the practical challenges of deploying vision systems in real-world robotic applications. Publication Trends: Dr. Hermans' recent publications demonstrate a strong trend towards leveraging transformer architectures for 3D and video understanding, developing robust methods for anomaly detection, and creating unified frameworks for diverse vision tasks. His research on diffusion models and self-supervised learning also highlights his engagement with the latest advancements in AI. Scientific Awards: Best Vision Paper Award at ICRA 2014 Advising and Grants: While no specific students or grants are listed, his role as a senior author on numerous publications and his leadership in creating benchmarks (like OoDIS) and software indicate a significant mentoring and project leadership role. His work is often supported by large-scale datasets and collaborations, suggesting involvement in substantial research grants and projects (e.g., the STRANDS project). Labs and Teams: He is a core member of the research group led by Prof. Bastian Leibe at RWTH Aachen, a group renowned for its work in computer vision and robotics. His research is closely tied to this lab, which focuses on developing robust, real-world applicable vision systems for autonomous agents.

• Urban and Social Geography
• Sociology of Space
• Spatial and Regional Planning
• +۷ مورد دیگر

X. Gao is a Post-doctoral Researcher at the Leibniz Institute of Ecological Urban and Regional Development (IOER) in Dresden, Germany, where he works on the project Circular Spaces of the Built Environment in Cities and Regions . He previously served as Assistant Professor of Urban Sociology at the Institute of Sociology, Chinese Academy of Social Sciences, and as a Post-doctoral Researcher at the Habitat Unit, Technical University of Berlin. His academic journey reflects strong international engagement in urban and social geography, with regional expertise in China, Thailand, and Cambodia. Education: Doctor of Philosophy (Dr. Phil.) in Urban and Architectural Sociology, Technical University of Berlin (2014–2019) Master of Science (M.Sc.) in Urban and Regional Planning, Peking University (2011–2014) Bachelor of Engineering (B.Eng.) in Urban Planning, Tianjin University (2006–2011) His research focuses on the sociology of space, urban transformation, and spatial planning, with particular interest in relational spatial theory, gated communities, intercity cooperation, and nighttime identity in historical cities. He applies critical realism and participatory methods to understand urban dynamics in Asian megacities. The recent publications reflect a strong trend in urban sociology and spatial planning, especially in Chinese and Southeast Asian contexts. Topics include gated communities, intercity governance, circular urban spaces, and art-world spatiality, indicating an interdisciplinary approach combining sociology, architecture, and policy analysis. Scientific Projects and Grants: Urban-Rural Assembly (URA): Managing inclusive transformation-to-sustainability processes at the urban-rural interface of the Huangyan-Taizhou region in China (BMBF funded, 01LE1804A) Housing Regeneration and Management in Beijing (funded by National Government Offices Administration, PRC) Place-Making in Megacity Chengdu (funded by Chengdu Institute of Social Sciences) A Participatory Approach to Developing Nighttime Identity for Historical Cities in Thailand (collaboration with King Mongkut’s University of Technology Thonburi) Safeguarding Ta Keo Temple in Angkor Wat (funded by Chinese Academy of Cultural Heritage) He has supervised bachelor’s and master’s theses and taught courses such as Urban and Community Studies at the University of Chinese Academy of Social Sciences and Communicative Construction of Space at Technical University of Berlin. His work is supported by national and international funding agencies, reflecting strong research impact and collaboration. He is currently involved in the ZirkuläR project at IOER, focusing on circularity in urban built environments, indicating a forward-looking research agenda on sustainable urban development and spatial transformation.

• Environmental Humanities
• Energy Cultures
• Literature and Energy Transition
• +۳ مورد دیگر

Prof. Dr. Ingo Uhlig is an Extraordinary Professor at the Institute of German Studies, Faculty of Philosophy II, Martin Luther University Halle-Wittenberg. He also serves as a Research Associate at the Institute for Climate Protection, Energy and Mobility (IKEM) in Berlin and holds a Department Affiliate position at the Institute for Philosophy, Literature, Science and Technology History at the Technical University of Berlin. Since 2023, he has held the 50Hertz Guest Professorship 'New Energy Systems and Energy Cultures' at TU Berlin. His educational background includes: MA in Philosophy, Sociology, English Studies, and Ethnology from Dresden and Bayreuth (2001) PhD in Media Culture from Bauhaus-Universität Weimar (2005), with dissertation 'Poetologien des Ereignisses bei Gilles Deleuze' Habilitation (2013) with work 'Traum und Poiesis. Produktive Schlafzustände 1641-1810' (published 2015) Prof. Uhlig's research centers on Environmental Humanities with a focus on energy cultures, renewable energies, and the energy transition as reflected in contemporary literature and art. His work bridges literary studies, philosophy, and energy studies, examining how cultural narratives shape and are shaped by energy systems. He has previously explored theories of contingency, the philosophy and literary history of dreams and sleep, and global research perspectives at Halle University since 1780. His recent projects include the SuzAnNa magazine on sustainability culture, the 'Strom, Netz, Fluss' atlas of energy systems, and research for IKEM and the WindNODE energy project. His publications reveal a consistent engagement with the intersection of literature, philosophy, and energy systems. Recent works focus on how contemporary literature and art narrate the energy transition, examining texts by authors like Juli Zeh, Alina Herbing, and Burkhard Spinnen. His research shows how cultural forms engage with energy infrastructure, sustainability, and resource cultures, creating a bridge between technical energy systems and cultural understanding. His scientific contributions include: Monographs: 'Energiewende erzählen. Literatur, Kunst, Ressourcen' (2023), 'Traum und Poiesis' (2015), 'Poetologien des Ereignisses bei Gilles Deleuze' (2008) Edited volumes: 'SuzAnNa' (2024), 'Strom, Netz, Fluss' (2023), 'Weltensammeln' (2020) Numerous articles on energy culture in literature, dream theory, and aesthetics Prof. Uhlig has been actively involved in public discourse on energy transition through media appearances, including interviews with MDR Kultur and contributions to publications like FAZ. His teaching at MLU Halle-Wittenberg covers topics such as 'Poisons' and 'Near and Far / Local and Global in Literature and Film,' connecting historical literary perspectives with contemporary environmental concerns. He leads the SuzAnNa magazine project, which explores the culture and aesthetics of sufficiency, and has developed web content through platforms like stromnetzfluss.de that visualize energy systems and their cultural dimensions.

• Image Compression
• Machine Learning
• Visual Media Processing
• +۷ مورد دیگر

Jona Ballé is an Associate Professor in the Electrical and Computer Engineering department at New York University's Tandon School of Engineering. His research focuses on developing efficient representations of visual media through machine learning and end-to-end optimization techniques. Dr. Ballé's research interests center on visual media compression, spanning still images, video, augmented reality, virtual reality, plenoptic imaging, and holographic imaging. His work bridges information theory, computer vision, and machine learning to develop perceptually optimized compression algorithms. He has made significant contributions to understanding the relationship between human visual perception and image statistics, which has led to improved compression results and ultimately contributed to the JPEG AI standard finalized in 2025. His recent publications demonstrate a strong trend toward Wasserstein distortion metrics, neural compression architectures, and rate-distortion optimization. These works span computer vision, information theory, and signal processing domains, with applications in both traditional and emerging visual media formats. His research shows consistent innovation in developing perceptually relevant metrics that balance fidelity and realism in compressed media. Contributed to JPEG AI standard (2025) Co-organizer of Challenge on Learned Image Compression (CLIC) since 2018 Program committee member of Data Compression Conference (DCC) since 2022 Reviewer for top-tier publications including NeurIPS, ICLR, ICML, and IEEE Transactions journals Dr. Ballé has advised numerous graduate students who have co-authored significant publications with him, particularly in the areas of neural compression and perceptual metrics. His research has been supported by institutions including the Simons Foundation. He maintains active collaborations across academia and industry, with his work at Google (2017-2024) directly informing his current academic research. His laboratory focuses on developing open-source implementations of advanced compression techniques, with notable GitHub repositories including Wasserstein Distortion implementation in PyTorch and CoDeX (Learned data compression in JAX), demonstrating his commitment to reproducible research and community engagement.

Jonathon Shlensمشاهده پروفایل

پژوهشگر

• Machine Learning
• Computer Vision
• Neuroscience
• +۳ مورد دیگر

Jonathon Shlens is a principal scientist and research director at Google DeepMind, focusing on vision, language, and learning. He has led teams in deploying production systems, invented TensorFlow, and collaborated with Waymo. His work spans machine learning, computer vision, basic science, and autonomous driving. Research interests include: Machine learning with applications in multimodal and transformer models Computer vision, particularly robustness and 3D object detection Neuroscience, analyzing neural computations in the primate retina Autonomous driving systems and motion forecasting Recent articles highlight trends in: Vision-language models and semantic guidance Transformer architectures for scene flow and calibration Adversarial robustness across human and machine perception Scalable datasets and model architectures Scientific awards include: Best Paper Award at CVPR 2013 Former advisees include notable researchers now at institutions like Stanford, MIT, and OpenAI.

Zhong Chenمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Data Mining
• +۷ مورد دیگر

Zhong Chen is an Assistant Professor in Data Science and Machine Learning at the School of Computing, Southern Illinois University (SIU), where he serves as Director of the Learning, Optimization, and Analysis from Data Lab (LOAD Lab). He holds a Ph.D. in Computer Science from Wuhan University of Technology and has previously worked as a Research Assistant Professor at the University of Kansas Medical Center and as a Computational Scientist at Xavier University of Louisiana. His research focuses on data-centric AI, Large Language Models, machine learning, deep learning, big data mining, online optimization, and anomaly detection, with applications in healthcare and medical physics. His work addresses fundamental challenges in handling streaming data with varying feature spaces, imbalanced classification problems, and developing interpretable AI systems for medical applications. Chen's recent publications demonstrate expertise in online learning frameworks, sparse representation techniques, and applications in healthcare domains including cancer treatment, patient outcome prediction, and medical imaging. His research combines theoretical innovation with practical applications in medical physics and bioinformatics. Excellence Reviewer Award of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'23) Outstanding Reviewer Award (top 10% of reviewers) of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'25) Chen serves as Associate Editor for Medical Physics and Editorial Member of Computational Biology and Bioinformatics. He is actively involved in the academic community as Program Committee member for major AI conferences including AAAI, IJCAI, KDD, and ECML-PKDD. He has advised numerous research projects and served on thesis committees at SIU, with a focus on developing the next generation of data scientists and AI researchers. His LOAD Lab at SIU focuses on foundational innovation in artificial intelligence and machine learning with emphasis on online optimization, machine learning techniques, and applications in big streaming data, bioinformatics, and medical physics.

Katherine Dauge-Rothمشاهده پروفایل

دانشیار

• Early Modern French Literature
• Francophone Studies
• History of the Body
• +۷ مورد دیگر

Katherine Dauge-Roth is an Associate Professor of Romance Languages and Literatures at Bowdoin College, specializing in the literature, history, and culture of the early modern Francophone world with a focus on the history of the body and material culture. She is a core faculty member in both the Romance Languages and Literatures department and the Francophone Studies program, teaching across the curriculum from language instruction to advanced seminars. Professor Dauge-Roth earned her PhD and MA in French Literature from the University of Michigan, a DEUG in Lettres Modernes from Université de Caen, and an AB in French and History from Colby College. Her educational background reflects her dual expertise in French language and historical studies. Her research examines how the body served as a privileged surface for displaying authority and identity during periods of increased global circulation in the early modern period. She has made significant contributions to understanding skin marking practices including tattooing, branding, stigmata, and other corporeal inscriptions, with particular attention to their intersections with writing and print culture. Her work spans Native American tattooing practices, criminal branding, devotional stigmata, and the representation of race in early modern France. Professor Dauge-Roth's scholarly impact is evident in her monograph Signing the Body: Marks on Skin in Early Modern France (Routledge, 2020) and the co-edited volume Stigma: Marking Skin in the Early Modern World (Penn State University Press, 2023), which brings together international scholars to examine body marking from a global perspective. Karofsky Teaching Prize (2005) National Endowment for the Humanities grant (summer 2023) Executive Director of the Society for Interdisciplinary French Seventeenth-Century Studies President of Bowdoin's Alpha of Maine chapter of Phi Beta Kappa Professor Dauge-Roth is deeply committed to inclusive pedagogy and developing students' linguistic and cultural fluency. She has co-authored several pedagogical articles on teaching the early modern period and contemporary France. Her teaching portfolio includes innovative courses such as 'Spoken Word and Written Text,' which examines oral and written sources from the Middle Ages through the abolition of enslavement in 1848, and 'Contemporary France through the Media,' where students analyze current French social and political issues. She is an active member of the Early Modern Race reading group and the Skin Studies reading group, contributing to scholarly conversations that bridge historical inquiry with contemporary concerns about race, embodiment, and cultural representation.

• Auditory Working Memory
• Neural Mechanisms of Imagination
• Neuroscience of Music
• +۵ مورد دیگر

David Ricardo Quiroga Martinez is a Postdoctoral Researcher in the Department of Psychology at the University of Copenhagen's Faculty of Social Sciences. His research focuses on the neural mechanisms underlying auditory working memory and musical imagination using intracranial EEG and MEG techniques. He investigates how the brain represents and manipulates sounds during perception and imagination, with applications for understanding psychiatric disorders and brain-computer interfaces. His research interests span Auditory Working Memory , Neural Mechanisms of Imagination , Neuroscience of Music , and Reward Coding . He employs invasive and non-invasive neuroimaging to study how musical sequences are processed in the brain, examining temporal hierarchies in predictive processing from pure tones to complex songs. His work bridges cognitive neuroscience, computational modeling, and clinical applications. Key publications reveal trends in neural representation of musical thoughts (2024), asymmetric reward prediction coding (2023), and temporal hierarchies in melody processing (2023). His research consistently explores how musical structure, predictability, and expertise shape neural responses, with implications for understanding disorders involving auditory imagery abnormalities. Reintegration Fellowship from the Carlsberg Foundation: The representation of musical thoughts in neurons of the human brain International Postdoc from DFF: The neural basis of musical imagination His collaborative network spans international institutions, with significant work on congenital amusia, atonal music processing, and neural gain modulation. Current projects involve decoding imagined musical sounds and investigating reward prediction errors in cortical networks, supported by major Danish research foundations.