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Judita PreissView profile

Lecturer

• Natural Language Processing
• Biomedical Data Analysis
• Mental Health Analytics
• +3 more

Dr. Judita Preiss is a Lecturer in Data Science at the University of Sheffield's School of Information, Journalism and Communication. She holds a MA, MPhil, and PhD in Mathematics, Computer Speech and Language Processing, and Natural Language Processing (NLP) from the University of Cambridge. Her career includes postdoctoral research at Cambridge, a visiting professorship at Ohio State University (2008–2010), and a role as a lecturer in Data Science at the University of Salford (2017–2022). Education: MA Cantab (Mathematics), MPhil (Engineering), PhD (Computer Science) – all from Cambridge. Her research focuses on NLP with applications in biomedical domains, mental health analysis via social media, big data techniques, multilingual models, and knowledge organization. She explores text/speech analysis involving large datasets and industry knowledge transfer. Publications emphasize literature-based discovery, semantic analysis, and tools like HiDE for scientific knowledge extraction. She is certified by Databricks (Apache Spark 3.0 – Python) and AWS (SysOps Administrator, Academy Educator), and serves on ACL's rolling review panel. Teaching: Modules include Big Data (INF6032), Business Intelligence (INF6040), and Practical Programming for Data Science (INF111). Grants & Labs: Active in interdisciplinary projects linking data science to healthcare and social sciences. Current research spans automatic discovery in healthcare, multilingual NLP models, and ethical AI applications in data-driven decision-making.

• Artificial Intelligence
• Machine Learning
• Healthcare Technology
• +6 more

Alberto Nogales Moyano is a Lecturer at the Department of Computer Science at the University of Alcalá. He holds a PhD in Computer Science from the same institution, completed in 2018, with a thesis titled A structural and quantitative analysis of the web of linked data and its components to perform retrieval data , supervised by Dr. María Elena García Barriocanal and Dr. Miguel Ángel Sicilia Urbán. His research focuses on AI applications in healthcare, data science, and cultural heritage preservation, with notable work on deep learning models for medical imaging, EEG analysis, and architectural restoration. Key research interests include artificial intelligence techniques for mental health assessment (e.g., lockdown impact studies during the pandemic), medical diagnostics using hybrid AI models, and optimizing deep learning frameworks for diverse domains like food safety and noise suppression. He also contributes to open-source tools such as EEGraph (for electroencephalogram analysis) and explores GAN-based approaches for virtual reconstruction of historical structures. His publications span topics such as self-organizing maps for data imbalance, BERT-based EEG analysis for Parkinson’s disease, and systematic reviews of deep learning’s role in physiotherapy and neurology. He actively engages in interdisciplinary projects combining machine learning with healthcare, sociology, and architecture, demonstrating a commitment to both methodological innovation and practical societal impact.

• Speech Processing
• Machine Learning
• Natural Language Processing
• +5 more

Professor Xiang Zhang serves as President and Vice-Chancellor of The University of Hong Kong , combining academic leadership with active research in speech processing and machine learning. His work bridges theoretical and applied advancements in speech enhancement, depression detection, and multilingual speech recognition. Speech Enhancement : Comparative studies on long-context networks and selective state space models Acoustic Representation : Feature codecs, landmark extraction, and frame-rate analysis Mental Health Applications : Depression detection using large language models and speech timing Code-Switching Recognition : Interactive language biasing and alignment techniques Interdisciplinary Research : Unidirectional brain-computer interfaces and quantum language models Recent publications highlight trends in transformer alternatives like Mamba architectures, self-supervised learning for speech tasks, and efficient diffusion-based audio processing. His work spans technical reporting, open-source dataset development, and pedagogical innovations in computational linguistics.

• Music Information Retrieval
• Artificial Intelligence
• Audio Signal Processing
• +3 more

Gerhard Widmer is a Full Professor at the Institute of Computational Perception within the Faculty of Engineering and Natural Sciences at Johannes Kepler University Linz (JKU). He leads research at the LIT Artificial Intelligence Lab and serves as principal investigator for major projects including the FWF-funded Cluster of Excellence "Bilateral Artificial Intelligence" (2024-2029) and the EU project "Whither Music?" (2022-2026), focusing on AI applications in music cognition and performance analysis. His research spans Music Information Retrieval (MIR) with emphasis on computational modeling of musical expressivity, audio processing, and machine learning applications. Key areas include sound event detection, music transcription, language-based audio retrieval, and modeling of expressive musical performances using deep learning techniques. His work bridges artificial intelligence with musicology to develop systems capable of understanding and generating human-like musical expressions. Analysis of recent publications (2024-2025) reveals a strong trend toward diffusion models for audio generation/manipulation, robustness studies in music emotion recognition, and efficient architectures for sound event detection. There is significant focus on multimodal approaches integrating audio with textual descriptions, knowledge distillation for model compression, and real-time processing solutions for symbolic music analysis and score following. No specific scientific awards are documented in the provided materials. Professor Widmer has supervised 8 research works and manages multiple funded projects: Cluster of Excellence "Bilateral Artificial Intelligence" (FWF, 2024-2029): Investigating human-AI collaborative intelligence "Whither Music?" (EU, 2022-2026): Exploring AI's impact on musical creativity CoMIRVA: Developing music information retrieval visualization tools Sony-CSL project (2021-2024): Industry collaboration on music cognition He directs the Institute of Computational Perception at JKU and is central to the LIT Artificial Intelligence Lab, maintaining active collaborations with the International Society for Music Information Retrieval (ISMIR) community through conferences, shared datasets, and open-source tools like Partitura for symbolic music processing.

Visara UroviView profile

Associate Professor

• Data Sharing
• Blockchain
• Artificial Intelligence
• +5 more

Visara Urovi is an Associate Professor at Maastricht University's Data Science Institute within the Faculty of Science and Engineering. Her research focuses on blockchain applications, AI-driven healthcare solutions, and privacy-preserving data sharing frameworks. Education: PhD in Computer Science Key research areas include: Blockchain technology for secure health data exchange AI models in respiratory disease monitoring Regulatory compliance for digital health systems Speech analytics as diagnostic tools Recent publications highlight trends in: Blockchain-based accountability mechanisms Speech recognition for COPD and asthma tracking Responsible data science frameworks Scientific awards include the Grant for the Web (2020). She has supervised PhD candidate Yan Y. (2025 thesis) and organized academic events like the Women in Data Science Maastricht Datathon (2022).

Reemt HinrichsView profile

Researcher

• Signal Coding
• Cochlear Implants
• Digital Signal Processing
• +3 more

Reemt Hinrichs is a researcher at the Institute for Information Processing (TNT) at Leibniz University Hannover, where he focuses on signal processing applications across biomedical engineering, audio technology, and structural health monitoring. He completed his Dr.-Ing. (PhD) at TNT in 2023 after working as a research assistant since January 2018. Dr. Hinrichs earned his Master's Degree in Mechatronics from Leibniz University Hannover in May 2017, completing his thesis on "System-theoretical modeling of a structural sound signal path" at the Institute for Information Processing. His academic journey reflects a consistent focus on signal processing theory applied to practical engineering challenges. His primary research interests center around Signal Coding , particularly for Cochlear Implants , along with broader expertise in Digital Signal Processing and Nonlinear System Theory . His work spans multiple application domains including biomedical engineering (cochlear implants), audio processing (guitar effects modeling), and structural health monitoring (acoustic emissions analysis for infrastructure). Dr. Hinrichs' publication record demonstrates a strong focus on compression algorithms for cochlear implants, with numerous papers on neural network-based approaches for zero-delay compression of electrical stimulation patterns. He has also made significant contributions to guitar effects modeling using convolutional neural networks and structural health monitoring through acoustic emission analysis. His research bridges theoretical signal processing with practical applications across diverse domains, showing particular strength in applying deep learning techniques to specialized signal processing challenges. With approximately 60 theses supervised, Dr. Hinrichs has been actively involved in mentoring students across various research topics including cochlear implant technology, structural modeling, and audio signal processing. His supervision portfolio includes work on nonlinear prediction of electrode excitation patterns, geometry-dependent modeling of transfer functions, and automatic extraction of guitar effects. His current research focuses on "deep learning models for the compression of electrode excitation patterns of cochlear implants," continuing his long-standing expertise in this specialized area of biomedical signal processing while expanding into new applications of neural network architectures for real-time signal compression.

Maria Joaquina Valero GisbertView profile

Associate Professor

• Spanish phraseology
• Bilingual lexicography
• Audiovisual translation
• +3 more

María Joaquina Valero Gisbert is Associate Professor (Professore Associato, “II fascia”) of Spanish Language and Translation at the University of Parma , Italy, within the Department of Humanities, Social Sciences and Cultural Enterprises. Since 2016 she serves as President of the Master in Audiovisual Translation and sits on the PhD boards in “Philological-Literary Sciences, Historical-Philosophical and Artistic Sciences” and the National Doctorate in “Learning Sciences and Digital Technologies”. She is member of the Scientific Council of the Doctoral College and of the Executive Board of the Spanish Association of Italian Language and Translation (ASELIT). Education PhD in Linguistics, University of Valencia / University of Bologna Degree in Modern Foreign Languages and Literatures, University of Milan Research Qualification (Idoneità alla Ricerca), University of Valencia Pedagogical Aptitude Diploma, University of Valencia Degree in Hispanic Philology, University of Valencia Research interests centre on Spanish phraseology, bilingual Spanish-Italian lexicography, audiovisual translation with a special focus on accessibility through audio-description, and the application of digital technologies and game-based learning to translator and language training. Her work integrates linguistic, pedagogical and technological perspectives to enhance inclusive communication and cultural heritage accessibility. Her recent publications (2020-2023) reveal a clear trajectory: intensive investigation into inter-semiotic translation and audio-description for film, comics and cultural heritage; critical analysis of how cultural references and phraseological units are handled in bilingual dictionaries; and exploration of serious games and digital tools for developing transliteracy and language skills. Scientific evaluation and awards Registered expert reviewer for the Italian Ministry of University and Research (MIUR) since 2012 Member of REPRISE – Register of Expert Peer Reviewers for Italian Scientific Evaluation (2018) Referee for numerous Class-A journals and international conferences Doctoral supervision and teaching span BA, MA and PhD courses in Spanish language, translation, specialised languages, and audiovisual translation. She has supervised countless theses since 2002 and is actively involved in curriculum design for language teacher education programmes. Laboratory & projects : Coordinator of the local unit in the Horizon Europe project “TLIT4U – Improving Transliteracy Skills through Serious Games”; partner in the PRIN project “A new digital space for the lexicographical heritage: The Digital Treasury of Italian-Spanish Bilingual Lexicography”; member of the national Cluster TICHE on technological innovation in cultural heritage.

• Machine Learning
• Natural Language Processing
• Healthcare Applications
• +7 more

Frank Rudzicz is a Professor at the University of Toronto with appointments in the Faculty of Medicine and Department of Computer Science. His research bridges machine learning, natural language processing, and healthcare applications, with a particular focus on developing AI solutions for medical diagnostics, surgical assistance, and patient care. He leads several research initiatives in medical AI and collaborates extensively with clinicians and healthcare institutions. Rudzicz's research interests span multiple critical areas in healthcare AI. He has pioneered work in speech-based detection of neurological conditions including Alzheimer's and Parkinson's diseases, developing algorithms that analyze linguistic patterns to identify early signs of cognitive decline. His work in surgical AI has led to systems that provide real-time decision support during operations and analyze surgical performance. He also investigates ethical AI implementation in healthcare, addressing privacy concerns and developing frameworks for trustworthy clinical AI systems. More recently, his research has expanded into large language models for medical applications, focusing on privacy preservation, fairness, and explainability in clinical contexts. His publication record shows a clear evolution from foundational work in speech processing and dysarthria recognition toward increasingly sophisticated medical AI applications. Early work focused on improving speech recognition for impaired speakers, which naturally led to applications in neurological disorder detection. Over the past decade, his research has expanded to encompass broader healthcare applications, with recent work heavily featuring transformer models, multimodal data integration, and ethical considerations in medical AI deployment. His current research demonstrates strong emphasis on practical implementation challenges including model security, privacy preservation, and clinical integration of AI systems. Rudzicz has received recognition for his work, including best paper awards at major conferences like ICLR 2025. His research has been supported by numerous grants focusing on AI in healthcare, though specific grant details aren't provided in the available text. He has mentored numerous graduate students who have gone on to contribute significantly to medical AI research. As a leader in medical AI, Rudzicz contributes to major collaborative efforts including the Delphi consensus statement for digital surgery. His work frequently appears in top-tier AI and medical journals, demonstrating his ability to bridge the technical and clinical domains. Through his research group at the University of Toronto, he continues to advance the frontier of AI applications in healthcare while addressing the practical and ethical challenges of implementing these technologies in real clinical settings.

Adib MehrabiView profile

Lecturer

• Audio Signal Processing
• Acoustic Engineering
• Machine Listening
• +4 more

Adib Mehrabi is an Honorary Lecturer in the Department of Linguistics at Queen Mary University of London and Principal Audio Researcher at Sonos Inc. His research focuses on audio signal processing, vocal imitation systems, and acoustic environment modeling. Mehrabi holds a PhD from Queen Mary University of London and has contributed extensively to spatial audio technologies, room acoustics analysis, and binaural rendering systems. His work bridges theoretical linguistics with applied audio engineering, including projects on vocal mimicry for content retrieval, multichannel audio calibration, and real-time head-tracking systems. Mehrabi's research emphasizes practical applications in smart home audio systems and perceptual audio quality assessment. Key technical contributions include room-informed binaural rendering algorithms, self-calibrating playback device systems, and phase-based acoustic parameter estimation. His publications demonstrate interdisciplinary expertise spanning signal processing, machine learning, and sociolinguistic studies of bilingual speech patterns. Advising and grants: No specific advising roles or grant information provided in source texts. Current affiliations include dual roles at Queen Mary and Sonos Inc., indicating industry-academia collaboration in audio innovation. Labs/Teams: Affiliated with Sonos' research division and Queen Mary's Linguistics department research groups focused on experimental phonetics and computational linguistics.

• Natural Language Processing
• Audio Signal Processing
• Music Information Retrieval
• +4 more

Panagiotis Zervas serves as an Associate Professor in the Department of Electrical and Computer Engineering at the University of Peloponnese since June 4, 2020. His academic career includes prior appointments as Assistant Professor at the Department of Music Technology and Acoustics, Hellenic Mediterranean University (2015–2020) and Technological Educational Institute of Crete (2015–2019), and as Lecturer at the same department of the Technological Educational Institute of Crete (2008–2015). His research spans Natural Language Processing applications for text analysis , Large Language Models for structured data extraction , audio signal processing , and music information retrieval using machine learning . He specializes in developing AI-driven systems for knowledge mining from multimodal information, with applications in social protection and intelligent audio systems. His work integrates Retrieval-Augmented Generation techniques and focuses on embedded systems for IoT audio applications. Zervas leads major European research initiatives including EU-ALMPO (2025–), Train4Blue (2025–), GROWTH4BLUE (2024–), and MICROIDEA (2024–) as Principal Investigator or Deputy Scientific Director. Since 2023, he collaborates with the World Bank as a Short-Term Consultant, first implementing Greece's National Skills Framework with AI-based skill profiling (2022–2024), and currently developing an AI-driven job matching portal for the Pacific Islands Network for Employment. His teaching portfolio includes Signals & Systems, Digital Signal Processing, Statistical Processing & Learning, Audio & Music Technology, Machine Learning, and Educational Technology. He completed his PhD in 2007 at the University of Patras' Department of Electrical Engineering on Greek language modeling for text-to-speech systems, and has published in journals like Acoustics and Mathematics while presenting at conferences including Web Audio Conference (WAC2022) and Forum Acusticum 2023.

Ni LiView profile

Lecturer

• Climate Science
• Environmental Data Analysis
• Natural Language Processing
• +3 more

Ni Li is a Lecturer and doctoral candidate in the Department of Water and Climate at the Faculty of Engineering, Vrije Universiteit Brussel. Their research focuses on climate extremes, environmental data analysis, and natural language processing applications in climate science. Ni has presented at international workshops such as the Helmholtz Center for Environmental Research (UFZ) and the COMPASS Workshop on Climate Risks. They have also engaged in teaching activities, including a workshop on Shell & Git for big data and modeling. Ni’s interdisciplinary work bridges climate science, text-mining techniques, and music technology, as evidenced by their diverse publication record. Research Interests: Climate Impact Analysis using LLMs Text-as-data methods in hydrology and natural hazards IoT applications for environmental comfort Algorithmic fairness in recommendation systems Rhythm perception in music and language development Recent Activities: Keynote speaker at the UFZ workshop on Big Data and Modelling (Feb 2025) Presentation at ClimateNLP 2024 on climate impact databases Teaching workshops on computational tools for researchers

Ehsan Shareghi NojehdehView profile

Assistant Professor

• Natural Language Processing
• Large Language Models
• Deep Generative Models
• +5 more

Ehsan Shareghi Nojehdeh is an Assistant Professor in the Department of Data Science and Artificial Intelligence at Monash University and an affiliated lecturer at the University of Cambridge. He leads a research team focused on predictive models for language (text and speech), with a background in postdoctoral work at Cambridge and prior roles at UCL. His research interests include probing and augmenting Large Language Models (LLMs), reasoning in legal contexts, safety of multimodal models, speech/text translation, and self-supervised learning. Education: PhD in Computer Science (Scalable Non-Markovian Sequential Modelling) from Monash University (2017). Professional roles include Deputy Course Director for the Master of AI program at Monash, service on academic committees (ECA Committee, 2022–2023), and senior reviewing roles at major conferences (ACL2025, EMNLP2023). He teaches units like FIT5217 Natural Language Processing and FIT5212 Data Analysis for Semi-structured Data. Key projects include a Paul Ramsay Foundation-funded study on media narratives around disadvantage in Australia (2022–2023). His work addresses UN SDGs through advancing education and AI ethics. Research collaborations span global institutions, with a focus on knowledge-intensive tasks like biomedical domain applications, graph-to-text generation, and generative model disentanglement. Publications emphasize safety, multimodal capabilities, and logical reasoning with LLMs, with recent work appearing in ACL, EMNLP, and NAACL venues. His team investigates LLM shortcomings, including alignment with human judgment, speech-specific vulnerabilities, and tool-based reasoning strategies.

Elias IosifView profile

Assistant Professor

• Blockchain
• Machine Learning
• Natural Language Processing
• +5 more

Dr. Elias Iosif is an Assistant Professor in the Department of Digital Innovation at the School of Business, University of Nicosia. He specializes in blockchain technologies, machine learning, and natural language processing. His research focuses on integrating blockchain with AI, semantic models, and spoken dialogue systems. He holds a PhD in Network-Based Distributional Semantic Models from the Technical University of Crete (2013). His career includes postdoctoral roles at the National Technical University of Athens (2014–2017) and research positions at institutions like AT&T Labs and the Athena Research Center. Notable contributions include blockchain maturity models, fraud detection frameworks, and AI applications in pharmaceuticals. Research highlights include over 80 peer-reviewed publications, with recent work emphasizing blockchain scalability, NFTs, and cross-chain interoperability. He co-authored the Handbook of Blockchain Technology (2025), exploring cryptocurrency regulation and tokenization landscapes.

• Human-Computer Interaction
• Recommender Systems
• Deep Reinforcement Learning
• +4 more

Ingram Sandy is a Professor at the Haute école d'ingénierie et d'architecture de Fribourg (HES-SO), part of the iSIS Institute for Intelligent and Secure Systems. Her research focuses on Human-Computer Interaction, Recommender Systems, and Digital Wellbeing, with a strong emphasis on applying technology to education and workplace environments. She leads and collaborates on interdisciplinary projects addressing challenges in smart environments, user experience design, and AI integration in learning platforms. Key projects include developing serious games for mental health support (Sesame Project), workplace comfort systems (SpotOn), and chatbot-enhanced educational tools. She has also contributed to frameworks like eLogBook and Graasp, aiming to bridge formal and informal learning through social software innovations. Her work spans over 15 years, with publications in top venues like IEEE, ACM, and international conferences on human-computer interaction and learning technologies. Ingram Sandy’s research interests span from foundational AI methods (reinforcement learning, deep learning) to applied domains like digital wellbeing assessment, smart office comfort modeling, and chatbot-mediated education. She actively collaborates with industry partners (e.g., OrchardAI) and academic networks across Switzerland and Europe. Her contributions highlight the intersection of technology, human behavior, and educational design.

Felix BurkhardtView profile

Lecturer

• Speech Communication
• Emotion-Oriented Computing
• Machine Audio Analysis
• +7 more

Dr. Felix Burkhardt is a Lecturer at the Chair of Speech Communication, Technische Universität Berlin. He has held roles including temporary professor (2020-2022) and acting head of the Chair. His research focuses on emotion-oriented computing, speech synthesis/analysis, and machine learning applications in audio processing. He leads R&D at audEERING GmbH since 2018, developing emotion AI technologies. Education: PhD in Speech Communication from TU Berlin. Key projects include the EmoDB emotional speech database, open-source tools like Speechalyzer and Nkululeko, and contributions to W3C's EmotionML standard. He serves on program committees for top conferences (ACII, ICASSP, Interspeech) and journals. Research interests span emotional speech modeling, speaker classification, voice search systems, and ethical implications of paralinguistic technologies. Over 150+ publications in top venues (IEEE, Springer, ACM) showcase his work on emotion recognition, speech synthesis, and machine learning frameworks.