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 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)
Norwegian University of Science And TechnologyNorway
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 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 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 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 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.
Swiss Federal Institute of Technology in LausanneSwitzerland
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.
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.
Swiss Federal Institute of Technology in LausanneSwitzerland
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-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.
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.
Joe F. Bozeman III is an Assistant Professor in the School of Civil and Environmental Engineering at the Georgia Institute of Technology's College of Engineering, with a courtesy appointment in the School of Public Policy. He serves as the SEI Lead for Ethics in Energy Transition and directs the Social Equity and Environmental Engineering Lab (SEEEL). His work bridges engineering practice with ethical considerations in climate change adaptation and mitigation strategies. Dr. Bozeman's educational background includes: Ph.D. in Industrial Ecology from University of Illinois at Chicago (2020) M.S. in Environmental Engineering from Wright State University (2010) B.S. in Environmental Engineering from Wright State University (2008) As an industrial ecologist, Dr. Bozeman focuses on developing ethical climate change adaptation and mitigation strategies. His research spans circularity analysis, food-energy-water nexus systems, sustainable urban systems, and life cycle assessment. His work uniquely integrates sociodemographic considerations into environmental engineering practice, examining how different demographic groups experience environmental impacts differently. This focus on systemic equity in industrial ecology applications has positioned him as a thought leader addressing the 'wicked' challenges of our time through cross-disciplinary collaboration with psychology, economic, and public health experts. Dr. Bozeman's scientific contributions have gained significant attention, with research featured in mainstream media outlets including NPR, the New York Post, Popular Science, and Free Speech TV. His work on U.S. food-consumption impacts across sociodemographic subgroups has particularly resonated with broader audiences. His publications demonstrate recognition through frequent citations and media coverage. With over a decade of experience in public and private sectors as an energy and environmental practitioner before joining Georgia Tech, Dr. Bozeman brings practical insights to his academic work. His SEEEL lab serves as a hub for integrating ethics, engineering, and community engagement, exploring innovative ways to merge technical findings with artistic expression for broader public accessibility. Dr. Bozeman maintains affiliations with multiple research centers at Georgia Tech, including Sustainable Systems, Renewable Bioproducts, Energy initiatives, and the Institute for Matter and Systems. His work on urban carbon management strategies, food-energy-water equity, and circular economy approaches demonstrates his commitment to creating more just and sustainable systems through engineering practice.
Dr Majid Zamani is a Lecturer in the School of Electronics & Computer Science at the University of Southampton. His research focuses on implantable and wearable biomedical devices, applied AI in biomedical engineering, and hardware-efficient processing frameworks. Member of: Digital Health and Biomedical Engineering Institute for Life Sciences Member of: Centre for Internet of Things and Pervasive Systems Member of: Centre for Health Technologies Member of: UKRI AI Centre for Doctoral Training in AI for Sustainability (SustAI) Current research addresses key challenges in scalability, signal processing, sensing, energy efficiency, and miniaturization for next-generation implantable brain-machine interfaces (iBMI). He explores hardware-efficient computational platforms for biomedical applications, including AI-driven algorithms for neural signal/image processing, augmented navigation in constrained anatomical spaces, and low-power real-time processors in 180/90/45 nm CMOS technologies. Recent publications focus on deep learning for spike sorting, binarized neural networks, noise-aware speech enhancement, and AI integration in surgical navigation. His work bridges biomedical engineering with AI and hardware optimization. Teaching: Digital system design (ELEC6236) 42+ publications in journals like IEEE Transactions on Medical Imaging, Journal of Neural Engineering, and IEEE Access
Marie Tahon is a Professor at Le Mans University and Director of the LST (Langage, Signal et Texte) team at LIUM (Laboratoire d'Informatique de l'Université du Maine). Her research spans expressive speech processing with applications in speech synthesis, emotion recognition, and speaker identification, complemented by expertise in musical acoustics for automatic song analysis and organology. Education : Engineering degree from École Centrale de Lyon (2007), M.S. in Acoustics from École Centrale & INSA Lyon (2007), and Ph.D. in Computer Science from University of Paris-Sud (Orsay, 2012). Postdoctoral positions at LIMSI-CNRS (affective computing), LMSSC CNAM (acoustics), and IRISA (Expression team). Research Focus : Tahon's work centers on developing interpretable systems for expressive speech processing. Key contributions include the ALLIES corpus for speech segmentation/diarization and AlloSat for call-center emotion analysis. Her recent publications demonstrate strong emphasis on low-resource speech translation (e.g., Kurdish), speaker verification after resynthesis, and turn-taking analysis in French media using explainable AI techniques. She integrates acoustic and linguistic features for continuous emotion prediction and develops noise-robust models for digital holography. Collaborations & Infrastructure : Leads the COMMUTE and ESPERANTO projects while directing LIUM's LST team. Her work leverages specialized resources like the ALLIES corpus (segmentation, diarization, recognition) and AlloSat (satisfaction/frustration analysis). Current efforts focus on lifelong learning for MOS prediction, multilingual speech translation, and perceptual evaluation of turn-taking phenomena in broadcast media.
