جستجوی استاد راهنمای دانشگاهی

• Arabic Natural Language Processing
• Machine Translation
• Dialect Identification
• +۷ مورد دیگر

Ahmed Abdelali is a prominent researcher at Hamad Bin Khalifa University's Qatar Computing Research Institute (QCRI), where he works in the Arabic Language Technologies department. With 97 publications spanning from 2004 to 2024, he has established himself as a leading figure in Arabic natural language processing research. His work has been presented at major conferences including ACL, EMNLP, EACL, and specialized Arabic NLP venues. Dr. Abdelali's research focuses on several key areas within Arabic computational linguistics, with particular emphasis on Arabic language technologies. His work spans Arabic dialect identification, diacritization, machine translation, offensive language detection, and part-of-speech tagging. In recent years, he has been at the forefront of adapting large language models for Arabic language processing, contributing significantly to benchmarking frameworks like LAraBench and LLMeBench that evaluate Arabic language models' performance. His publication record shows consistent high-impact contributions, with notable collaborations with researchers including Hamdy Mubarak (45 co-authored papers), Kareem Darwish (38 co-authored papers), and Nadir Durrani (26 co-authored papers). Recent work has focused on large language models for Arabic, cross-lingual spaces, and benchmarking frameworks to advance Arabic AI capabilities. His research bridges theoretical advances with practical applications, particularly in processing the linguistic complexities of Arabic and its dialects. Through his leadership in developing resources like the Farasa segmenter and contributions to major Arabic NLP conferences, Dr. Abdelali has played a pivotal role in establishing the infrastructure and community for Arabic language technology research.

Kristopher Kyleمشاهده پروفایل

دانشیار

• Corpus Linguistics
• Second Language Acquisition
• Natural Language Processing
• +۳ مورد دیگر

Kristopher Kyle is an Associate Professor in the Department of Linguistics at the University of Oregon , where he directs the Learner Corpus Research and Applied Data Science (LCR-ADS) lab . His research examines how readers/listeners assess language proficiency through lexical, lexicogrammatical, and syntactic features, employing computational methods and language corpora within a usage-based/emergentist framework. Research Interests Lexical and syntactic complexity in second language acquisition Development of text analysis tools (TAALED, TAALES, TAASSC, TAACO) Natural language processing for language assessment Dependency parsing and bigram analysis Corpus-based language teaching and learning Key Contributions Open-source Python packages: lxgrtgr , pylats , taaled , corpus-toolkit Annotated datasets: ASC-Treebank , SL2E-Dependency-Treebank His work bridges corpus linguistics , applied data science , and language teaching studies , with recent publications focusing on lexical diversity indices, argument structure analysis, and NLP applications in language assessment. While no explicit scientific awards are listed, his tool development and corpus creation efforts significantly impact second language research methodology. Outside academia, Kyle engages in outdoor activities, notably rock climbing, with his family.

• Artificial Intelligence
• Machine Learning
• Inverse Reinforcement Learning
• +۵ مورد دیگر

Aaron Snoswell is a Research Fellow at Queensland University of Technology (QUT), affiliated with the Faculty of Creative Industries, Education & Social Justice and the School of Communication. His work bridges technical AI development with social implications, focusing on ethical implementation of AI systems across multiple domains including healthcare, legal systems, and public policy. Snoswell's research interests span artificial intelligence, machine learning (particularly inverse reinforcement learning), AI ethics, automated decision-making systems, and the societal implications of AI technologies. His work demonstrates a consistent focus on making AI systems more transparent, accountable, and beneficial for society, with particular attention to healthcare applications, legal systems, and public understanding of AI technologies. Analysis of his most recent publications reveals a clear trajectory toward responsible AI deployment, with increasing focus on policy frameworks, public education about AI capabilities and limitations, and practical applications that address real-world problems in healthcare and justice systems. His work often combines technical AI development with critical social perspectives, reflecting an interdisciplinary approach that connects computer science with social sciences. Snoswell has been actively involved in policy submissions to governmental bodies including the Australian Senate Select Committee on Adopting Artificial Intelligence and responses to the G7 Guiding Principles for organizations developing advanced AI systems. His work with the ARC Centre of Excellence for Automated Decision-Making and Society (ADM+S) demonstrates his commitment to shaping responsible AI governance frameworks. He has established productive collaborations across multiple institutions and disciplines, working with researchers in communication studies, computer science, healthcare, and legal fields. His recent work on the GenAI Arcade project represents an innovative approach to public engagement with AI technologies through interactive learning environments.

Ana Puig Puigمشاهده پروفایل

استاد

• Gamification in Education
• Serious Games
• 3D Point Clouds
• +۹ مورد دیگر

Dr. Anna Puig Puig is a Professor at the University of Barcelona, affiliated with the Faculty of Mathematics and Computer Science within the Department of Mathematics and Computer Science. Her research focuses on gamification in education, 3D point cloud processing, data visualization, and biomedical applications of computer science. Key research areas include: Gamification and serious games for education 3D point cloud segmentation and analysis Machine learning applications in geospatial and biomedical domains Interactive visualization systems Virtual reality for archaeological and educational purposes Notable projects include: INDOMAIN - Pedagogical Module in Intelligent Tutoring Systems HORIZON 2020 European Cancer Image Platform GRAPES - Machine Learning for Shape Processing CLiC Research Group in Language and Computing Digital Reconstruction of Neolithic Social Life

Hanna Ehlertمشاهده پروفایل

پژوهشگر

• Artificial Intelligence in Speech Therapy
• Dynamic Assessment
• Language Development Disorders
• +۴ مورد دیگر

Hanna Ehlert is a researcher at Leibniz University Hannover, affiliated with both the Institute for Special Education (Faculty of Philosophy) and the Institute for Information Processing (TNT/Phonomatics spin-off). Her work bridges artificial intelligence with speech therapy, focusing on automated diagnostics and dynamic assessment for child language development. PhD in Special Education (2020, Leibniz University Hannover) M.Sc. & B.Sc. in Speech and Language Therapy/Logopedics (HAWK Hildesheim) State-certified speech therapist (Hannover Medical School) Her research integrates: Artificial Intelligence in speech therapy Dynamic Assessment for multilingual children Language development disorders Voice disorder prevention Technology for inclusive education Key research projects include: TALC (Tool for Analyzing Language and Communication) Phonomatics spin-off AI-LIT (AI-supported Literacy Development)

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۹ مورد دیگر

Professor Sule Yildirim Yayilgan is a distinguished academic at the Department of Information Security and Communication Technology (IIK) within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU) in Gjøvik. She has held the position of Professor since 2020, following her tenure as Associate Professor from 2016-2020. Dr. Yayilgan previously served as Head of Department between 2005-2009 at HIHM (now part of NTNU). Her academic journey spans over 30 years in teaching and research, with significant contributions to interdisciplinary fields bridging AI, cybersecurity, and privacy. Her educational background includes a MSc in Computer Engineering (1995) and a PhD in Artificial Intelligence and Computer Science (2002). Dr. Yayilgan has led and participated in numerous international research projects funded by EU Horizon 2020, Eurostars, Erasmus+, and various Norwegian research councils. She currently leads the MR PET (Multidisciplinary Research group on Privacy and data protEcTion) research group and serves on the scientific board of NTNU's strategic area in Data Science. Dr. Yayilgan's research spans multiple domains with a unifying focus on ethical, legal, and privacy-preserving AI systems. Her work addresses critical challenges in health, energy, education, and security sectors through advanced AI methodologies. She has published over 100 journal and conference papers, with recent work focusing on hate speech detection, border security technology acceptance, smart grid security, and explainable AI applications. Her publications demonstrate a strong emphasis on practical implementations that balance technical innovation with societal considerations. As an active research leader, she currently oversees several significant projects including VIPA-DELF (vineyard disease detection using federated learning), METICOS (border control technology monitoring), CINELDI (intelligent electricity distribution), and AQMA (air quality monitoring). She also serves on multiple ethics boards and research integrity committees, reflecting her commitment to responsible innovation. Dr. Yayilgan has supervised numerous graduate students throughout her career, advising 41+2 (in progress) MSc students and 3+2 (periods) +6 (in progress) PhD candidates. Her administrative contributions include membership in NTNU's Research Integrity Committee, the Trondheim ACM Women Chapter, and various project management boards for EU-funded initiatives. She maintains active professional affiliations with IEEE, the International Association for Pattern Recognition, and COST Actions focused on language technologies and security research.

Auxiliadora Sarmiento Vegaمشاهده پروفایل

استاد

• Audio Source Separation
• Biomedical Signal Processing
• Machine Learning
• +۵ مورد دیگر

Auxiliadora Sarmiento Vega is a full professor at the University of Seville 's School of Engineering within the Department of Signal Theory and Communications . With over two decades of research experience, her work bridges audio signal processing and biomedical applications, focusing on blind source separation, entropy-based methods, and machine learning for healthcare diagnostics. Research Pillars : Audio source separation, biomedical signal/image analysis, and virtual reality integration Key Projects : ACACIA (Signal Analysis), NEUBIAS (Bioimage Analysts Network), and multiple NIH-funded biomedical imaging initiatives Academic Contributions span 15+ years, with groundbreaking work in: Alpha-Beta divergence clustering algorithms EEG processing for motor imagery BCI systems Automated breast cancer grading from histological images Glaucoma and diabetic retinopathy diagnostics via retinal image analysis Virtual reality platforms for emotion analysis research She actively collaborates with institutions like the IEEE Women in Engineering (Spanish section secretary) and NEUBIAS network , while mentoring through outreach programs like g4g Day that empower young women in STEM.

Elizabete Munzlingerمشاهده پروفایل

پژوهشگر ارشد

• Human-Computer Interaction
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Elizabete Munzlinger is a Research Fellow (industrial PhD) at the IT University of Copenhagen specializing in Data Science, concurrently employed as an Industrial PhD candidate at GN Audio A/S (Jabra) since October 2023. Her work bridges academic research and industry applications in human-computer interaction and AI-driven interface design. Education: Master of Science in Computer Science and Electronic Engineering, Aeronautics Institute of Technology (ITA), completed May 2008. Thesis: "Multimodal and Multi-User Extensions of Graphical and Voice Interfaces based on Speech Technologies and Interactions Models." Bachelor in Computer Science, Universidade do Oeste de Santa Catarina (UNOESC), completed July 2005. Thesis: "The Use of Computers in the Domotic to Help Disabled People." Research Focus: Munzlinger pioneers cross-cultural interaction systems through multimodal interfaces, specializing in hand gesture recognition for virtual/hybrid meetings. Her work integrates machine learning with cultural anthropology to develop universally accessible communication tools, emphasizing accessibility for diverse user populations and voice interface optimization. Research Trends: Recent publications demonstrate a strategic shift toward globally applicable gesture vocabularies addressing cultural diversity in virtual collaboration. Her work synthesizes taxonomy classification, semantic similarity analysis, and foundation models to overcome cross-cultural communication barriers in distributed teams, with strong emphasis on practical Edge AI deployment. Scientific Recognition: Best Poster Extended Abstract Award (July 4, 2024) for gesture-based HCI research at international conference Grant Leadership: Principal Investigator for the Innovation Fund Denmark project "HAND: Identifying Universal Communal Hand Gestures for Virtual and Hybrid Meeting Interaction" (2023-2026; 36 months). This €1.2M initiative develops standardized gesture systems for global virtual collaboration, focusing on cultural adaptation and real-time implementation. Professional Engagement: Active contributor to CVPR 2024 tutorial on Edge AI deployment, demonstrating expertise in translating theoretical AI models into production-ready systems. International collaborations span Denmark, USA, and Brazil through the HAND project consortium.

Hernisa Kacorriمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Assistive Technologies
• Accessibility Research
• +۵ مورد دیگر

Hernisa Kacorri is an Associate Professor in the College of Information (iSchool) at the University of Maryland with an appointment in the University of Maryland Institute for Advanced Computer Studies. She is a member of the Human-Computer Interaction Lab (HCIL) and the Institute for Trustworthy AI in Law & Society. Her research focuses on building technologies that address real-world problems by integrating data-driven methods and human-computer interaction, with particular emphasis on assistive technologies for people with disabilities. Her work includes developing sign language avatars, technologies for independent mobility for the blind, and math-to-speech systems. Dr. Kacorri's research primarily addresses accessibility challenges through technological innovation. Her work spans multiple domains including American Sign Language animation where she develops systems for synthesizing syntactic facial expressions, technologies for people with visual impairments focusing on object recognition and navigation, and accessibility solutions for mathematical content. Her approach combines data-driven methods with rigorous user-centered evaluation involving disabled communities. Her publication record shows a consistent focus on accessibility technologies, particularly in American Sign Language processing and visual impairment solutions. Her work demonstrates strong interdisciplinary connections between computer science, linguistics, and human factors research, with a clear emphasis on practical applications that address real accessibility barriers. Best Paper Honorable Mention at CHI 2017 Best Paper Finalist at ASSETS 2016 NSF Award #1506786 for Syntactic Facial Expression Synthesis research Dr. Kacorri collaborates with researchers at Boston University and Rutgers University on sign language processing projects. Her research methodology emphasizes involving disabled users early in the design process and developing rigorous evaluation frameworks that account for demographic factors and user experience. She has contributed to the development of automatic metrics for evaluating sign language animations and has explored innovative approaches like gamification to improve video captioning through crowdsourcing. Her laboratory work focuses on the Human-Computer Interaction Lab at the University of Maryland, where she leads projects on assistive technologies, sign language animation, and accessibility solutions for mathematical content. Her research group develops technologies that model communication and behavior to benefit people with disabilities.

Mason Marksمشاهده پروفایل

استاد

• Drug Policy
• FDA Regulation
• Artificial Intelligence in Healthcare
• +۶ مورد دیگر

Mason Marks is the Florida Bar Health Law Section Professor at Florida State University College of Law. He concurrently serves as Senior Fellow and Project Lead of the Project on Psychedelics Law and Regulation (POPLAR) at Harvard Law School's Petrie-Flom Center and is an Affiliated Fellow at Yale Law School's Information Society Project. Previously, he held fellowships at Harvard's Edmond J. Safra Center for Ethics and NYU's Information Law Institute. His research examines drug policy, FDA regulation, artificial intelligence in healthcare, psychedelics law, and constitutional rights. Core interests include controlled substance regulation, AI's impact on medical decision-making, therapeutic applications of psychedelics, and freedom of thought protections under the First Amendment. Publications demonstrate strong interdisciplinary focus across law, medicine, and technology. Recent work explores AI governance in healthcare, psychedelic therapy frameworks, constitutional drug decriminalization, and emergent medical data privacy. Articles frequently appear in top-tier law reviews (Harvard, Yale, Columbia) and medical journals (JAMA, NEJM, Nature). Advises federal and state regulators including the FDA, NIH, and HHS on controlled substance policy and psychedelic therapy frameworks. Leads POPLAR's initiatives to develop evidence-based regulations for emerging psychedelic therapies. Holds a J.D. from Vanderbilt Law School (2015), M.D. from Tufts University School of Medicine (2011), and B.A. from Amherst College (2000). Teaches Constitutional Law, Administrative Law, Drug Law, and seminars on technology and civil liberties.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Tengda Han is a Research Scientist at Google DeepMind , specializing in neural networks for video understanding. He earned his PhD from the University of Oxford under Andrew Zisserman and a BEng in Mechanical & Material Engineering from the Australian National University . Prior to his PhD, he studied business administration and law at Renmin University of China for one year. PhD in Computer Science, University of Oxford (2022) BEng in Mechanical & Material Engineering, Australian National University (2016) One year of business administration and law at Renmin University of China His research focuses on video representation learning , self-supervised methods , multimodal learning , and efficient prompting techniques . He has pioneered training-free frameworks for audio description, zero-shot object counting, and hyper-realistic movie generation. Recent publications demonstrate trends in temporal alignment , discriminative prompting , 5D video modeling , and open-world counting . His work often bridges computer vision with natural language processing and speech recognition . Scientific Awards : Best Paper Award at ACCV2024 Best Poster Award at BMVC2023 BMVA Sullivan Doctoral Thesis Prize Runner-up He has co-organized workshops at ICCV25 , NeurIPS23 , and BMVA Symposium on Vision & Language . Collaborators include Andrew Zisserman, Dima Damen, and Flamingo project members at DeepMind.

• Text mining
• Intelligent information retrieval
• Natural language processing
• +۵ مورد دیگر

Efstathios Stamatatos is a Professor in the Department of Information and Communication Systems Engineering at the University of the Aegean, Greece. He has been a faculty member at the university since 2004, following previous research positions at the Wire Communications Lab of the University of Patras, the Polytechnic University of Madrid, the Austrian Research Institute for Artificial Intelligence, and the TEI of Ionian Islands. His primary research interests include Text mining, Intelligent information retrieval, Natural language processing, Machine learning, and Computer music, with a particular focus on authorship attribution, plagiarism detection, and web genre identification. His work bridges computational linguistics, digital forensics, and digital humanities, applying machine learning techniques to analyze textual patterns and stylistic features. Dr. Stamatatos has authored numerous publications on authorship attribution, plagiarism detection, and text classification, with a strong emphasis on developing robust methodologies that work across different domains and languages. His research has evolved from traditional n-gram approaches to more sophisticated techniques involving machine learning ensembles, topic modeling, and recently, addressing challenges posed by generative AI systems. He has served as Associate Editor for the International Journal of Digital Crime and Forensics, and as Editorial Board Member for Language Resources and Evaluation and Information Processing and Management. He has also been Guest Editor for a Special Issue on Plagiarism and Authorship Analysis in Language Resources and Evaluation. Dr. Stamatatos has been instrumental in organizing the PAN (Uncovering Plagiarism, Authorship, and Social Software Misuse) workshop series, serving as co-organizer and contributing numerous overview papers on authorship verification, plagiarism detection, and digital text forensics. He has also served on program committees for major conferences including ACL, SIGIR, ECIR, and AAAI.

• Speech Processing
• Automatic Speech Recognition
• Auditory Modeling
• +۳ مورد دیگر

Philip Neil Garner is a Researcher at the Idiap Research Institute (LIDIAP), affiliated with École Polytechnique Fédérale de Lausanne (EPFL). His current position is listed as "EPFL member Current" with email philip.garner@epfl.ch. He maintains an active research profile with publications spanning from 2013 to 2025. Garner's research focuses on the intersection of physiological auditory modeling and machine learning for speech processing. His work bridges cochlear physiology with automatic speech recognition systems, investigating how biological principles of hearing can inform and improve computational models. Key areas include modeling the cochlea as an active amplifier using Hopf oscillators, exploring neural oscillations in speech perception via spiking neural networks, and developing interpretable affective speech synthesis systems. His research demonstrates consistent interest in creating biologically plausible models that maintain compatibility with modern deep learning frameworks. Analysis of his recent publications reveals a clear trajectory toward integrating physiological auditory models with state-of-the-art speech recognition systems. His work increasingly focuses on creating hybrid models that maintain physiological plausibility while leveraging pre-trained acoustic models. The research shows particular attention to modular approaches that allow different components (cochlear models, neural networks) to interact meaningfully, with emphasis on understanding how end-to-end learning affects physiological interpretations of speech processing. Garner has supervised multiple doctoral students including Louise Coppieters De Gibson, Bastian Schnell, and Sibo Tong, whose theses address cochlear modeling, affective speech synthesis, and multilingual speech recognition respectively. His research has received funding from the Swiss National Science Foundation as indicated in two publications. While specific grants aren't detailed, his work demonstrates consistent collaboration with Hervé Bourlard and Alexandre Bittar across multiple projects. As a core researcher at Idiap Research Institute, Garner works within a multidisciplinary team focused on speech processing and artificial intelligence. His publications indicate collaboration across units including LIDIAP, EDEE, STI, IEL, and LCAV at EPFL, suggesting integration within both the Idiap institute and broader EPFL research ecosystem. His work connects computational neuroscience with practical speech technology applications, positioning him at the intersection of theoretical auditory modeling and applied speech processing.

Casey Lew-Williamsمشاهده پروفایل

استاد

• Developmental Psychology
• Language Acquisition
• Bilingualism
• +۴ مورد دیگر

Casey Lew-Williams is a Professor and Department Chair at Princeton University, where he leads the Princeton Baby Lab. His research focuses on how infants learn from dynamic communicative environments, integrating experimental, descriptive, computational, and social neuroscience approaches. He collaborates with institutions like Concordia University to study bilingual language acquisition and has developed tools like iCatcher+ for automated gaze analysis. His work spans typical development, adversity, and bilingual contexts. Research Interests: Language acquisition, bilingualism, infant cognition, caregiver-child interactions, neural synchrony, and computational modeling of learning processes. Recent Article Trends: His publications address sociodemographic reporting standards, emotion dynamics, caregiver speech variability, and open science methodologies in developmental research. Awards: Phi Beta Kappa Award President’s Award for Distinguished Teaching Excellence in Mentoring Graduate Students Advisees: Kennedy Casey Brooke Ryan Bianca Santi His lab emphasizes translating theoretical insights into community-focused applications to support child development.

Anton Ragniمشاهده پروفایل

مدرس ارشد

• Core automatic speech recognition
• Efficient and expressive speech synthesis
• Spoken Language Translation
• +۲ مورد دیگر

Dr. Anton Ragni is a Senior Lecturer in Speech and Language Technologies at the University of Sheffield's School of Computer Science, where he serves as Assessments Lead and contributes to the Speech and Hearing (SpandH) research group. His educational background includes: BEng in Information Technology from the University of Tartu (2005) MEng in Information Technology from the University of Tartu (2007) PhD from the University of Cambridge (2013) Ragni's research centers on machine learning approaches for speech and language processing, with core expertise in automatic speech recognition (ASR), expressive speech synthesis, spoken language translation, information retrieval, and conversation modeling. His work increasingly integrates self-supervised learning and foundation models to address challenges in speech technology and cross-domain applications like music processing. Analysis of his recent publications reveals a strong trend toward applying speech processing techniques to music understanding and developing robust ASR systems for specialized populations, including hearing-impaired users and children. His work demonstrates consistent innovation in leveraging contextual information and novel architectures like energy-based models. His scientific recognition includes: Best Student Paper Award at IEEE ASRU 2011 for 'Generative kernels for noise robust ASR' Ragni has secured significant research funding as Principal Investigator and Co-Principal Investigator: EPSRC grant 'Exemplar-based Expressive Speech Synthesis' (2021-2023, £218,290) as PI Innovate UK grant 'Automatic voice conversion for transforming professional adult voice actors to artificial child voice actors' (2021-2023, £173,605) as Co-PI He actively contributes to the Speech and Hearing research group, focusing on advancing speech technology through interdisciplinary collaboration and real-world applications.