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Daniel RothView profile

Professor

• Artificial Intelligence
• Virtual Reality
• Augmented Reality
• +6 more

Prof. Dr. Daniel Roth is a Professor of Machine Intelligence in Orthopedics at the TUM School of Medicine and Health at Technical University of Munich (TUM), appointed in September 2023. His research focuses on human-machine interfaces in medicine, including AI, virtual/augmented reality (XR) technologies for surgical assistance systems, disease diagnosis, and rehabilitation. Previously, he held a junior professorship in Human-Centered Computing and Extended Reality at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). Education: Bachelor's/Master's in Media and Imaging Technology (TH Köln) PhD in Computer Science (Julius-Maximilians-Universität Würzburg) Research Interests: Roth’s work integrates AI and extended reality to solve healthcare challenges. Key areas include: XR-based surgical training and teleconsultation systems AI-driven analysis of surgical workflows Embodiment in virtual environments for medical applications Accessibility technologies for visually impaired users Publications: Recent work emphasizes immersive medical visualization, telemedicine systems, and user embodiment in VR. Key themes include: XR applications in surgery and patient care 3D teleconsultation for emergency scenarios AI-enhanced anatomy visualization Awards: Best Demo Honorable Mention (IEEE VR, 2021) Best Poster Award (ISMAR, 2020) IEEE TVCG Best Journal Paper (2018) Grants & Teams: Active in interdisciplinary projects at Klinikum rechts der Isar. Collaborates with industry partners on AR/VR healthcare solutions. No formal advisee list is provided, but his work involves multi-disciplinary teams. Labs/Teams: Leads machine intelligence initiatives in TUM’s medical school, focusing on translational research between engineering and clinical practice.

Leonardo BanhView profile

Researcher

• Generative AI
• Artificial Intelligence
• Machine Learning
• +6 more

Leonardo Banh is a Researcher at the University of Duisburg-Essen within the Faculty of Computer Science and its Chair of Business Information Systems and Software Engineering . M.Sc. in Business Information Systems (University of Duisburg-Essen, 2022) B.Sc. in Business Information Systems (University of Duisburg-Essen, 2020) Semester abroad at Instituto Superior Técnico, Lisbon (2021) His research focuses on Generative AI and its socio-technical implications, particularly in Machine Learning and Deep Learning applications. He explores intersections with NeuroIS , Smart Tourism , and E-commerce Ecosystems , emphasizing sustainability and digital transformation. Recent publications analyze Generative AI in Software Engineering , AI in Music Sentiment Analysis , and AI-Based Sign Language Translation . His work often involves design science research and grounded theory frameworks. Best Paper in Track Award (ICIS 2024) Nominated for Best Paper Award (ICIS 2024) Outstanding Reviewer (ICIS 2024) As advisor, he supervises theses on topics including AI-Based Mental Health Chatbots , Generative AI in HR , and Smart Tourism Applications . He also contributes to the Institute of Computer Science and Information Systems and serves on appointment/habilitation committees.

Hasan KivrakView profile

Assistant Professor

• Cyber-Physical Systems
• Autonomous Robots
• Indoor Wireless Positioning
• +3 more

Dr. Hasan Kivrak is an Assistant Professor in the Department of Computer and Information Science at Northumbria University, UK, since 2023. He holds a B.Sc. in Computer Engineering from Selcuk University, Turkey, and M.Sc. and Ph.D. degrees from Istanbul Technical University. Before joining Northumbria, he served as a Research Associate at the University of Manchester's Department of Electrical and Electronic Engineering (2021–2023). His research focuses on cyber-physical systems, autonomous robotics for extreme environments, and indoor wireless positioning. Key areas include developing socially-aware robots, digital twin approaches for industrial inspection, and navigation frameworks for human-robot interaction. His work addresses challenges in nuclear environments, assistive robotics, and 3D environmental modeling. Dr. Kivrak’s interdisciplinary projects bridge robotics, AI, and wireless networks. Notable contributions include the Cyber-WISE positioning system, symbiotic autonomous ecosystems for nuclear applications, and adaptive social navigation models. His research emphasizes practical solutions for industrial automation, human-robot collaboration, and extreme environment robotics. He currently resides at Northumbria University’s Ellison Building, Room ELB120, contributing to academic and applied research in computer science and robotics.

Helmi MarttilaView profile

Researcher

• Intralingual Subtitling
• Accessible Communication
• Language Technologies
• +3 more

Helmi Marttila is a Doctoral Researcher in the Doctoral Programme in Language Studies at Tampere University. Their research focuses on intralingual subtitling processes in Finnish public sectors, examining paraprofessional subtitlers utilizing language technologies like ASR. They are affiliated with Plural, a multidisciplinary research centre for languages and cultures. Education: PhD candidate (2023–present), MA in Finnish Language (2020–2022), BA in Sign Language Interpreting (2012–2016). Research interests include accessible communication methods, interfaces of speech and text, and the role of paraprofessionals in language work. Their work bridges translation studies and accessibility, particularly in digital environments. Award: AFinlandia Vuoden gradu (2023). Active in academic communities, organizing and participating in conferences such as the Digitalization Transforming Language Work conference (2025) and the Language in the Human-Machine Era Training School (2024). Member of Plural's management group since 2025.

Pablo RivasView profile

Assistant Professor

• Artificial Intelligence
• Responsible AI
• AI Ethics
• +7 more

Pablo Rivas is an Assistant Professor in the Department of Computer Science at Marist College, where he contributes to teaching and research in artificial intelligence and machine learning. He is the Director of the Center for Standards and Ethics in Artificial Intelligence, an NSF IUCRC planning grant initiative, emphasizing his leadership in responsible AI development and ethical frameworks. Education: PhD in Electrical and Computer Engineering, The University of Texas at El Paso (2011) MS in Electrical Engineering, Chihuahua Institute of Technology (2006) BS in Computer Science, Nogales Institute of Technology (2004) Dr. Rivas's research spans Artificial Intelligence , with a strong emphasis on Responsible AI , AI Ethics , Deep Learning , and Fuzzy Systems . His work integrates machine learning with applications in cybersecurity, healthcare diagnostics, natural language processing, and environmental monitoring. He explores ethical dimensions in technology, particularly under societal and organizational pressures. The analysis of his recent publications reveals a consistent focus on applying AI to real-world problems—from detecting eye diseases in children using smartphone photos to classifying DDoS attacks and modeling sentence quality through deep learning. His interdisciplinary work bridges computer science, medicine, ethics, and environmental science, showcasing a versatile and impactful research agenda. Scientific Awards: No awards listed in the provided text. Dr. Rivas has been involved in significant research projects, including postdoctoral work at Baylor University and a graduate internship at NASA Goddard Space Flight Center. While no formal grants are listed, his leadership in the NSF IUCRC planning grant for AI ethics indicates active grant-seeking and collaborative research. He has mentored research but no formal students are listed. He has not held administrative roles beyond research leadership. He is associated with the Center for Standards and Ethics in Artificial Intelligence, where he leads efforts to develop ethical guidelines and standards for AI deployment. This center serves as a hub for interdisciplinary collaboration on trustworthy AI systems.

Anne GöhringView profile

Researcher

• Computational Linguistics
• Natural Language Processing
• Machine Translation
• +5 more

Anne Göhring is an Academic Associate at the University of Zurich, working within the Language, Technology and Accessibility research team at the Institute of Computational Linguistics. With over a decade of experience in computational linguistics and natural language processing, she has established herself as a specialist in machine translation, sign language processing, and German language analysis. Her work bridges theoretical linguistics with practical applications, particularly focusing on accessibility technologies and resource-poor languages. Her educational background includes: 2001-2010: Studies in Spanish Linguistics and Literature, Computational Linguistics 2006-2007: Erasmus study year at Universidad Complutense Madrid Dr. Göhring's research spans multiple domains of computational linguistics with a strong emphasis on practical applications. She has made significant contributions to machine translation systems, particularly for hybrid approaches and low-resource languages like Quechua. Her recent work has increasingly focused on sign language processing, developing corpora and translation systems to improve accessibility. She also conducts important research in semantic role labeling, sentiment analysis, and German language processing, with applications ranging from social media analysis to veterinary text mining. Analysis of her recent publications reveals a clear trajectory toward accessibility-focused NLP research, particularly in sign language technologies. While maintaining expertise in German language processing and machine translation, her 2023-2024 work shows a strong emphasis on sign language corpora development (SwissSLi) and pose-based identification systems. Earlier work focused more on semantic analysis, sentiment detection, and cross-lingual processing, demonstrating remarkable versatility across NLP subfields. Dr. Göhring has been actively involved in numerous research projects: 2011-2014: SNSF project SQUOIA on Hybrid Machine Translation 2015: CTI project SentiSpider on expectation-based sentiment analysis 2016-2018: What's up, Switzerland? corpus project 2018-2022: Sentiment inference research 2021-2024: NCCR Evolving Language Project: IMAGINE 2022-2026: Flagship IICT Throughout her career, Dr. Göhring has supervised numerous student projects and taught courses ranging from introductory computational linguistics to specialized seminars on machine translation and NLP for medicine. Her teaching portfolio demonstrates commitment to both foundational knowledge and cutting-edge applications in the field. She is an active member of the Language, Technology and Accessibility research group at the University of Zurich, collaborating extensively with researchers like Manfred Klenner, Sarah Ebling, and Martin Volk on diverse NLP challenges.

• Computer Engineering
• Artificial Intelligence
• Software Engineering
• +2 more

Gizem Gümüşçekiçci is a Research Assistant at Işık University , affiliated with the Faculty of Engineering and Natural Sciences in the Department of Computer Engineering . Her work focuses on Artificial Intelligence , Software Engineering , and Computer Networks . Education : Master's in Computer Engineering (Full Scholarship, ongoing since 2021), Bachelor's in Computer Engineering (75% Scholarship, 2016-2021) Her research integrates Natural Language Processing and Deep Learning , covering areas like Turkish Embedding Models , Sarcasm Detection , and Sign Language Accessibility . Recent publications highlight applications in sentiment analysis, diffusion models, and social network security. No scientific awards are explicitly mentioned in the provided texts.

Lennart EingView profile

Researcher

• Multimodal Neural Networks
• Self-Supervised Learning
• Deep Learning
• +4 more

Lennart Eing is a Researcher at the Chair for Human-Centered Artificial Intelligence at the University of Augsburg . He contributes to projects focusing on multimodal neural networks, self-supervised learning, and sign language recognition systems. Research Interests: Multimodal neural networks, self-supervised learning, deep learning, few-shot learning, sign language recognition, 3D hand mesh reconstruction, physiological constraints in machine learning. He actively engages in GitHub projects, particularly related to self-supervised learning applications for sign language translation and dataset management. His work often involves refining models like HaMeR and MANO for improved 3D reconstruction and implementing attentive probing techniques with Sapiens features. Contact: lennart.eing@uni-a.de

• Accessibility Research
• Sign Language Recognition
• Human-Computer Interaction
• +4 more

Danielle Bragg is an Assistant Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, specializing in accessibility research with a focus on technologies for deaf and hard-of-hearing communities. Her work bridges human-computer interaction, computer vision, and inclusive design to create practical solutions for sign language recognition, translation, and communication accessibility. Her research interests center on making technology accessible through innovative approaches to sign language processing, assistive communication systems, and inclusive design methodologies. She investigates how to build robust sign language recognition systems that work in real-world settings, addresses bias in AI models for sign language understanding, and develops community-centered approaches to dataset collection. Her work particularly emphasizes involving the Deaf community in the research and development process to ensure technologies meet actual needs. Dr. Bragg's publication record shows a consistent trajectory from her PhD work at the University of Washington (2018) through to current research in 2025, with increasing leadership in the field of accessibility technology. Her recent work explores cutting-edge applications of AI for sign language processing, gaze-based interfaces, and systematic analysis of how large language models are transforming human-computer interaction research. Her collaborative approach is evident in her extensive co-authorship network, working with both technical researchers and community stakeholders to advance the field of accessibility technology through interdisciplinary partnerships.

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +3 more

Amal Al-Shargabi is a Senior Lecturer in Data Analytics and Artificial Intelligence at the School of Computer Science and Informatics, part of the Faculty of Computing, Engineering, and Media at De Montfort University (UK). Previously, she served as an Associate Professor at Qassim University (Saudi Arabia). She holds a PhD in Information Technology and Quantitative Sciences from Universiti Teknologi MARA (UiTM), Malaysia, along with a Master's in Computer Science (awarded excellence) and a Bachelor's in Computer Science. Education: PhD in Information Technology and Quantitative Sciences, UiTM, Malaysia Master's Degree in Computer Science, UiTM, Malaysia Bachelor Degree in Computer Science Research Interests: Machine Learning, Artificial Intelligence (including Generative AI), Deep Learning, and their applications in real-world problems such as healthcare, education, and environmental management. She also explores empirical software engineering and data analytics. Research Trends in Publications: Her work focuses on AI-driven solutions for healthcare monitoring (e.g., elderly health), energy efficiency in arid climates, GAN-based generative models, and ethical AI in academia. Recent studies also address assistive technologies like Saudi Sign Language translation and Quranic speech recognition. Awards: Invention, Innovation and Design Exhibition (IIDX’16) Award, Malaysia (2016) Three Minute Thesis (3MT’16) Award, Malaysia (2016) Student-led projects under her supervision have won awards in Saudi Arabia, including Takaful and AI Challenge prizes. Advising & Grants: Research grants include projects on health monitoring via IoT, ethical considerations of ChatGPT/BARD, and solar panel energy optimization. Internally funded projects at Qassim University addressed noise pollution, COVID-19 diagnosis via AI, and job recommendation systems. Labs/Teams: Affiliated with the Institute of Artificial Intelligence (IAI) at DMU. Collaborates with institutions like Qassim University on interdisciplinary projects.

Diane BrentariView profile

Professor

• Sign Languages
• Phonology
• Morphology
• +7 more

Dr. Diane Brentari is the Mary K. Werkman Professor of Linguistics at the University of Chicago and Co-Director of the Center for Gesture, Sign, and Language. Her research focuses on sign language grammars, phonology, and the emergence of protactile systems in DeafBlind communities. She holds a Ph.D. from the University of Chicago (1990) and has taught there since 2011. Key interests include modality effects on language structure, language variation, and typological comparisons. Her work has been supported by the National Science Foundation, a Guggenheim Fellowship, and the Neubauer Collegium. Notable contributions include books like Sign Language Phonology (2019) and edited volumes on sign language creation and phonological theory. Awards include Fellow of the Linguistic Society of America and membership in the American Academy of Arts and Sciences. Research Highlights: Analysis of Nicaraguan Sign Language emergence, tactile phonology in protactile communities, and cross-modal comparisons of gesture and sign. Courses Taught: Phonological Analysis, American Deaf Community Studies, and Seminars on Multiple Verb Predicates. Dr. Brentari's lab investigates sign language morpho-phonology and its sociocultural dimensions. Recent projects explore how tactile languages utilize touch and proprioception as linguistic resources. Her work bridges theoretical linguistics with empirical studies of language emergence and variation.

Marc Van HulleView profile

Professor

• Computational Neuroscience
• Brain-Computer Interfacing
• Artificial Intelligence
• +4 more

Marc Van Hulle is a full Professor at the Laboratory for Neuro- and Psychophysiology , KU Leuven Faculty of Medicine, Department of Neurosciences. He serves as promotor for multiple long-term projects (2022-2028) at the intersection of biomedical engineering and neuroscience, including Alzheimer's early detection, Parkinson's brainstem-basal ganglia interactions, and advanced brain-computer interfaces (BCIs). His affiliations include the KU Leuven Brain Institute (LBI) and Leuven.AI artificial intelligence institute. Research Focus: Specializing in computational neuroscience, Marc Van Hulle pioneers innovative BCI systems, neurophysiological modeling, and biomedical signal processing. His work spans cognitive modeling, neural network analysis, and translational applications for neurological disorders through advanced signal decoding techniques. Publication Trends: Recent articles emphasize decoding speech and motor activities from brain signals (ECoG/EEG), Alzheimer's detection via graph network analysis, and VR-integrated SSVEP systems. Key methodologies include tensor regression, multiway modeling, and phase synchrony analysis, with consistent application of AI to biomedical challenges. Academic Contributions: He supervises multiple researchers including Aurelie de Borman, Axel Faes, and Qiang Sun. His projects as promotor include European Doctoral Network for Neural Prostheses , Adaptive multistate decoding , and Decoding imagined speech , demonstrating sustained leadership in neurotechnology research. Laboratory Context: The Laboratory for Neuro- and Psychophysiology includes PhD members working on neural computing and BCI systems, with former members having completed significant contributions in similar domains.

• Automatic Speech Recognition
• Machine Learning
• Acoustic Phonetics
• +6 more

Mark Hasegawa-Johnson is a Professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, where he has been faculty since 1999. He holds affiliations with the College of Engineering and leads the Statistical Speech Technology Group. His academic roles include serving as Editor-in-Chief of the IEEE Transactions on Audio, Speech and Language, and membership in the ISCA Diversity Committee. Education: PhD in Electrical Engineering and Computer Science from MIT (1996), postdoctoral research at UCLA (1996-1999). Research interests span automatic speech recognition, machine learning applied to phonetics and prosody, and accessibility technologies for under-resourced languages and speech disorders. Key projects include the Speech Accessibility Project, which improves speech recognition for individuals with dysarthria, and international competition successes in audio event detection and multilingual broadcast retrieval. Scientific achievements include Fellowships from the IEEE (2020), Acoustical Society of America (2011), and ISCA. Awards also include NIH’s National Research Service Award (1998-1999) and the Frederic Vinton Hunt Post-Doctoral Fellowship (1996-1997). Teaching focuses on courses like Artificial Intelligence, Multimedia Signal Processing, and Speech Processing. Research supervision emphasizes undergraduate projects in signal processing and speech recognition, with notable student contributions to prosody-dependent speech recognition and audio source separation. Labs/Teams: Leads the Speech Accessibility Project and collaborates with interdisciplinary teams on projects like Mandarin language education tools and audio-visual speech models. Current work explores unsupervised learning, cross-lingual speech recognition, and AI-driven accessibility solutions.

• Computer Vision
• Vision and Language
• 3D Human Motion Synthesis
• +5 more

Gül Varol is an Associate Professor (permanent researcher) in the IMAGINE group at École des Ponts ParisTech. She holds an ELLIS Scholar distinction and collaborates with MPI. Previously, she was a postdoc at the University of Oxford’s Visual Geometry Group (VGG). Her PhD from Inria Paris/ENS under Ivan Laptev and Cordelia Schmid earned her ELLIS and AFRIF awards. She earned BS/MS degrees from Boğaziçi University. Research Focus: Her work bridges computer vision and language, emphasizing video understanding, human motion synthesis, and sign language technologies. Notable contributions include the BOBSL dataset (BSL), SLRTP challenge, and methods for sign language translation/contextual retrieval. She also pioneers text-driven 3D motion editing (MotionFix) and video chaptering with LLMs (Chapter-Llama). Awards & Service: Received Google Research Scholar Award (2023), ECCV 2024 Program Chair, IJCV associate editor, and leadership in workshops like NeurIPS’20 Preregistration and CVPR’25 Sign Language. Organized over 15 conferences/workshops since 2017. Labs & Teams: Core member of IMAGINE (LIGM lab), collaborates with MPI and industry (Google/Adobe). Active in outreach initiatives like Women in CV and Girls Who Code.

Ivan LaptevView profile

Professor

• Computer Vision
• Robotics
• Human Action Recognition
• +3 more

Ivan Laptev is a Professor at MBZUAI on leave from INRIA Paris, and serves as Head of Research at VisionLabs. He holds a PhD from the Royal Institute of Technology (2004) and a Habilitation from École Normale Supérieure (2013). His research focuses on computer vision, robotics, and action recognition, with over 150 publications in top-tier venues. He has served as Program Chair for ICCV'23 and CVPR'18, and organized events like the INRIA summer schools and Machines Can See summits. Key contributions include work on human action recognition, 3D reconstruction, and multimodal learning. His recent research emphasizes robotics manipulation, sim-to-real transfer, and large-scale video understanding. Awards include the ERC Starting Grant (2012) and the Helmholtz Prize (2017). Education: PhD (KTH, 2004), Habilitation (ENS, 2013) Grants: ERC Starting Grant (2012) Labs: Leads new lab at MBZUAI; Head of Research at VisionLabs