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Anjan DuttaView profile

Senior Lecturer

• Computer Vision
• Machine Learning
• Deep Learning
• +4 more

Dr. Anjan Dutta is a Senior Lecturer in Artificial Intelligence at the University of Surrey, UK. He holds a PhD in Computer Science from the Autonomous University of Barcelona (UAB), awarded with Excellent Cum Laude and the Extraordinary PhD Thesis Award (2013-14). His research focuses on computer vision and machine learning, particularly deep multi-modal embedding, zero-shot learning, and graph neural networks. Education: PhD in Computer Science, UAB (2014) MSc in Computer Vision & AI, UAB (2010) MCA, Maulana Abul Kalam Azad University of Technology (2009) BSc Mathematics (Honours), University of Calcutta (2006) Research Interests: Deep Learning for Vision Tasks Zero-Shot and Few-Shot Learning Graph Neural Networks Multi-modal Embedding Techniques Structured Representation Learning Recent Research Trends: His work emphasizes scalable and interpretable AI systems, with notable contributions in object counting, bias reduction in neural networks, and sketch-based retrieval. Publications span top-tier venues, reflecting interdisciplinary innovation in vision and learning. Awards & Honors: Extraordinary PhD Thesis Award (UAB, 2013-14) Excellent Cum Laude PhD Award (UAB, 2014) Labs & Affiliations: Active in the Surrey Institute for People-Centred AI (PAI) and the Centre for Vision, Speech and Signal Processing (CVSSP), contributing to cross-disciplinary AI research.

Ilhan AslanView profile

Associate Professor

• Human-Computer Interaction (HCI)
• Artificial Intelligence (AI)
• Machine Learning
• +6 more

Ilhan Aslan is an Associate Professor in the Department of Computer Science at Aalborg University (Denmark), affiliated with the Technical Faculty of IT and Design. His work focuses on Human-Centered Computing, with a strong emphasis on AI-driven interaction design, emotion-aware systems, and tangible/somaesthetic interfaces. He leads research in areas such as conversational AI, mobile computing, and smart environments, integrating machine learning with user-centered methodologies. Aslan's recent projects explore emotional speech processing, proactive AI agents, and creativity support tools, often bridging technical innovation with human well-being applications. His research spans over two decades, with notable contributions to mobile navigation systems (e.g., Bum Bag Navigator), gesture-based interaction, and ambient intelligence. Media coverage highlights his work on AI solutions for COPD patients and stress management applications. Aslan's approach combines rigorous technical development with ethnographic and participatory design practices, ensuring ethical and user-centric outcomes. His lab collaborates across disciplines, addressing challenges in HCI, AI ethics, and sustainable computing. Key Research Themes : Emotion-Aware AI, Human-AI Collaboration, Tangible Interaction, Mobile Health, Interactive Machine Learning Notable Projects : 'Feel my Speech' (haptic emotion conversion), 'BEHAVE AI' (ethical agent design), and somaesthetic smarthome systems Media Impact : Featured in 400,000+ Danes-relevant health tech stories and COPD patient support innovations Aslan's publications reflect a prolific trajectory from early mobile learning platforms (2000s) to cutting-edge generative AI and multimodal systems. His work consistently prioritizes real-world applicability through iterative prototyping and user studies, making him a key figure in both academic and applied HCI domains.

Vishnu MonnView profile

Associate Professor

• High-performance computing
• Predictive analytics
• Machine learning & Artificial Intelligence
• +7 more

Vishnu Monn is an Associate Professor at Monash University Malaysia's School of IT, serving as Deputy Head of Education and Director of the Advanced Computing Platform. He holds a PhD in Engineering from Multimedia University (2016) and has over 15 years of academic and industry experience, including roles at Panasonic R&D Centre Malaysia (2005–2009) and Multimedia University (2009–2017). His research focuses on high-performance computing, predictive analytics, machine learning, computer vision, and soft robotics, with over MYR 1 million in secured grants as principal investigator. Education: B.Eng. (First-Class Honours) in Electrical and Electronics Engineering (2004) M.Eng. in Electrical and Electronics Engineering (2007) Ph.D. in Engineering (2016) Research interests include: High-performance computing architectures and applications Predictive analytics for industrial and environmental systems Machine learning models for computer vision tasks Soft robotics control systems Recent projects span autonomous driving algorithms, IoT-enabled smart cities, and soft robotics using reinforcement learning. He has published 61+ peer-reviewed articles in top journals and conferences, with recent work emphasizing multimodal learning, generative models, and blockchain optimization. Scientific contributions include leadership in interdisciplinary research, securing major grants, and establishing Monash Malaysia's high-performance computing facility. Teaching commitments include courses on big data, parallel computing, and embedded systems, with roles as chief examiner and program coordinator.

Titus ZahariaView profile

Professor

• Computer Vision
• 3D Point Cloud Compression
• Neural Networks
• +5 more

Titus Zaharia is a Professor at Telecom SudParis, affiliated with the SAMOVAR research department. His work focuses on computer vision, 3D data compression, neural networks, and augmented reality applications. He has contributed extensively to standards like MPEG-4 and MPEG-7, particularly in 3D mesh compression and multimedia indexing. Zaharia leads research in assistive technologies for visually and hearing-impaired individuals, developing systems like DEEP-HEAR and wearable devices for navigation assistance. His work bridges theoretical advancements with practical industrial applications, such as optimizing AR systems for manufacturing and improving public transportation prediction through machine learning. Zaharia has collaborated on projects like the INVENIO platform for content reuse in multimedia production, and his contributions span over 20 years of academic and applied research. Key research areas include dynamic point cloud compression, lightweight neural network libraries (e.g., FasterAI), and multimodal fusion for video advertising and accessibility. He has published over 100 peer-reviewed papers in journals like IEEE Access and Image and Vision Computing, and presented at conferences including CVPR, ICCV, and ISMAR. His research emphasizes real-world impact, addressing challenges in efficient data representation, human-centric technology, and industrial automation.

David Guy BrizanView profile

Associate Professor

• Natural Language Processing
• Machine Learning
• Databases
• +2 more

David Guy Brizan is an Associate Professor at the University of San Francisco, specializing in Natural Language Processing, Machine Learning, and Databases. His research focuses on analyzing personal and cultural/demographic information embedded in speech and typing to enhance speech recognition systems and cybersecurity measures. He holds a PhD in Computer Science from the CUNY Graduate Center, alongside an MS from San Francisco State University and a BS from Brooklyn College. Education: PhD, Computer Science, CUNY Graduate Center (2018) MS, Computer Science, San Francisco State University BS, Computer & Information Science, Brooklyn College Brizan's research explores intersections of linguistics and technology, including keystroke dynamics for user authentication, speech-based demographic prediction, and conversational style analysis. His work has applications in cybersecurity, healthcare diagnostics (e.g., Parkinson’s disease detection via speech), and political discourse analysis. He has published extensively in journals like Scientometrics and conferences such as LREC. Prior to academia, he worked as an IT Coordinator for NYC’s Department of Correction, a Software Engineer at IBM, and an Interface Developer at McKesson. His industry experience informs his research on practical machine learning solutions. His advising and grants span collaborations with institutions like the Speech Lab at Queens College, where he previously served as a research assistant. He has no listed awards but is actively involved in interdisciplinary projects linking computational methods to social and medical domains.

• Human Language Technologies
• Robust Speech Recognition
• Speaker and Language Characterization
• +12 more

Alberto Abad is an Associate Professor at the Department of Computer Science and Engineering (DEI), Instituto Superior Técnico (IST), University of Lisbon, and a researcher at INESC-ID. He is the coordinator of the Human Language Technologies (HLT) laboratory at INESC-ID and the deputy coordinator of the Master in Computer Science and Engineering at IST. He holds a PhD and a degree in Telecommunication Engineering from the Technical University of Catalonia (UPC). He is also an IEEE Senior Member, ACM Member, and ISCA Member. Alberto Abad’s research interests include: Robust Speech Recognition Speaker and Language Characterization Applied Machine Learning Healthcare Applications of Speech Processing Privacy-Preserving Speech Technologies Speech as a Biomarker for Disease Detection His recent publications demonstrate a strong focus on privacy-aware speech systems, healthcare diagnostics (e.g., Parkinson’s and Alzheimer’s), children’s speech recognition, and secure speaker verification. He has contributed to over 100 peer-reviewed publications in top-tier conferences and journals such as IEEE TASLP, ICASSP, Interspeech, and LREC. Alberto Abad has been Principal Investigator for multiple national and international projects, including VITHEA (FCT), DIRHA (European), Biovisualspeech (CMU-Portugal), TAPAS ITN (EU), and currently leads the INESC-ID/IST contributions in the Accelerat.ai project. He actively collaborates with industry and startups in the field of speech technology. He has supervised over 25 Master’s theses and more than five PhD students, either as advisor or co-advisor, in areas such as speech biomarkers, privacy-preserving ML, children’s ASR, and cognitive disorder detection. He teaches graduate courses including Speech Processing, Programming Fundamentals, and Master’s Dissertation in Informatics and Computer Engineering. He is involved in the development of tools and platforms such as the SPA web-based platform for speech processing, the VITHEA virtual therapist for aphasia, and the BioVisualSpeech corpus and therapy system. His work bridges theoretical advances with real-world clinical and industrial applications.

Stuart MiddletonView profile

Associate Professor

• Natural Language Processing
• Information Extraction
• Human-in-the-loop NLP
• +17 more

Dr Stuart Middleton is an Associate Professor at the University of Southampton, affiliated with the School of Electronics and Computer Science within the Faculty of Engineering and Physical Sciences. He is a member of the Agents, Interaction and Complexity research group, the Centre for Machine Intelligence, and the Centre for Democratic Futures. His research focuses on Natural Language Processing (NLP), particularly information extraction and human-in-the-loop NLP, with applications in law enforcement, defence, mental health, and environmental science. His research interests include Natural Language Processing , Information Extraction , Human-in-the-loop NLP , Active Learning , Adversarial Training , Rationale-based Learning , and Argument Mining . He specializes in scenarios with limited or fragmented data, developing methods for few/zero-shot learning, graph-based models, and multimodal analysis. His work spans domains such as mental health, digital ethics (e.g., sharenting), defence, and climate science. The recent publications highlight a strong trend in applying NLP to societal challenges, including mental health monitoring through social media, ethical AI, multimodal argumentation in political debates, and climate data analysis. His work combines technical innovation in prompt engineering, summarization, and graph-based modeling with real-world impact. AI for defence: readiness, resilience and mental health (2024) Extraction and summarization of suicidal ideation evidence (2024) Sharenting and social media properties (2024) Implementing responsible innovation (2024) ConversationMoC: mood change detection (2024) Dr Middleton is actively involved in PhD supervision and academic leadership as Deputy Director of the UKRI MINDS Centre for Doctoral Training. He supervises multiple PhD students working on NLP and AI projects. He has secured research funding from ESRC and EPSRC for projects such as ProTecThem2.0 , SafeSpacesNLP , and GloSAT . He leads the Natural Language Processing modules COMP3225 (UG) and COMP6253 (PG) and mentors students in NLP research. He is a Fellow of The Alan Turing Institute and a member of the EPSRC peer review college, contributing to national research strategy and evaluation. His research is conducted within interdisciplinary teams, including collaborations with criminologists, social scientists, and environmental researchers. He is part of the Agents, Interaction and Complexity group and contributes to the Centre for Machine Intelligence and Centre for Democratic Futures , focusing on AI systems that interact meaningfully with humans and society.

Emmanouil BenetosView profile

Senior Lecturer

• Computational Audio Analysis
• Machine Listening
• Computer Audition
• +4 more

Emmanouil Benetos is a Reader in Machine Listening and Director of Research at Queen Mary University of London's School of Electronic Engineering and Computer Science. He co-leads the School's Machine Listening Lab and is affiliated with the Centre for Digital Music, Centre for Intelligent Sensing, Digital Environment Research Institute, and Centre for Multimodal AI. His research focuses on computational audio analysis applied to music, urban sounds, and bioacoustics. Key areas include machine listening, self-supervised learning, audio representation frameworks, and multimodal AI. Current projects involve resource-efficient audio processing and large language model integration for acoustic tasks. Recent publications highlight advancements in music source separation, lyrics transcription, graph neural networks for audio, and acoustic identification systems. His work bridges machine learning with real-world applications in music technology, environmental monitoring, and audio-language models. Royal Academy of Engineering / Leverhulme Trust Research Fellow Turing Fellow at the Alan Turing Institute Royal Academy of Engineering Research Fellow As academic service, he serves as Secretary for the International Society for Music Information Retrieval (ISMIR), chair of the IEEE Technical Committee education subcommittee, associate editor for IEEE/ACM Transactions and EURASIP Journal, and Deputy Director for the UKRI Centre for Doctoral Training in Artificial Intelligence and Music (AIM).

Arsha NagraniView profile

Researcher

• Machine Learning
• Multi-modal Learning
• Computer Vision
• +5 more

Arsha Nagrani is a senior research scientist at Google AI Research , focusing on machine learning for video understanding. She earned her PhD at the University of Oxford under Andrew Zisserman with a Google PhD Fellowship , and completed her undergraduate studies at the University of Cambridge with mentorship from Roberto Cipolla and Richard Turner. Research Interests : Self-supervised and multi-modal machine learning Video recognition using sound and text Computer vision for wildlife conservation Cross-modal self-supervision in biometric matching Transformer-based architectures for temporal modeling Zero-shot learning with frozen models Scientific Awards : ELLIS PhD Award Google PhD Fellowship ICASSP 2021 Outstanding Paper Award INTERSPEECH 2017 Best Student Paper Award Service Contributions : Area Chair for CVPR23, ICCV23 Reviewer for top conferences (CVPR, ECCV, ICCV, BMVC, NeurIPS, ICML, AAAI) Organized workshops: Sight and Sound Workshop @ CVPR [2020-2022] VoxSRC Challenges @ INTERSPEECH Video Understanding Pentathlon @ CVPR 2020 Women in Computer Vision (WiCV) Workshops

• Machine Learning
• Recommender Systems
• Natural Language Processing
• +10 more

Julian McAuley is a Professor in the Department of Computer Science at the University of California, San Diego (UCSD). His research bridges machine learning, natural language processing, and computer music, with a focus on generative models, recommender systems, and multimodal learning. He leads a lab that has produced influential datasets and frameworks for recommendation tasks. Primary Affiliation: UCSD, Department of Computer Science Research Themes: Generative AI, Recommender Systems, Music-Cognition Interfaces, Multimodal Learning McAuley's work explores the intersection of large language models (LLMs) with sequential recommendation, causal inference, and creative applications in music generation. His lab develops novel architectures like CoMMIT (multimodal instruction tuning) and SAND (LLM agent deliberation), while also advancing ethical AI through normative alignment techniques. Recent publications highlight trends in code-augmented reasoning , symbolic music processing , and contextual preference optimization . Notable applications include video-guided music synthesis, Explainable Chain-of-Thought systems, and tools for scalable self-updating models. He advises PhD students in areas spanning large language models , vision-language systems , and healthcare-driven AI . Collaborations span institutions like MIT-IBM Watson AI Lab, CMU, and companies including Google Deepmind, Meta, and Nvidia.

Vladimir MladenovićView profile

Professor

• Symbolic computation and processing
• Artificial intelligence and machine learning
• Deep learning and neural networks
• +7 more

Prof. Dr. Vladimir Mladenović is a Full Professor at the Department of Information Technologies , Faculty of Technical Sciences in Čačak , University of Kragujevac , Serbia. A 1975 native of Paraćin, he earned his Dipl. Ing. from the Faculty of Electronic Engineering, University of Niš (2000), followed by an M.Sc. (2005) and Ph.D. in Technical Sciences (2009) from the University of Kragujevac. His career spans industry (IT Manager, Serbian Glass Factory, 2001–2004), secondary education (teacher, 2004–2009), and academia (Professor of Vocational Studies, 2009–2013) before joining the University of Kragujevac. Research Focus: Symbolic software & tools, higher integration in IT systems Signal & multimedia processing, computer vision & AI/ML 5G/IoT architectures, edge computing, data protection Modern communication systems, web & network technologies He directs the Digital Innovation Lab at his faculty and has authored over 100 peer-reviewed publications in leading journals and conferences, with a strong emphasis on cross-layer modeling, deep learning, and IoT applications. He also co-holds 18 Serbian utility patents covering smart IoT systems ranging from greenhouse monitoring to smartwatch data collection, fire detection, and pedestrian safety. Honors & Professional Standing: Licensed Technology-Transfer Engineer – WIPO & Intellectual Property Office of Serbia (2012) Member, Union of Engineers and Technicians of Serbia Teaching Portfolio: Undergraduate: Modern Software Architectures, Multimedia Systems, Data Protection Master: Modern Communication Systems, Web Programming Doctoral: Modern Network Technologies, Applied Computer Vision

Catherine DominguèsView profile

Researcher

• Natural Language Processing
• Cartography
• Data Mining
• +3 more

Catherine Dominguès is a senior researcher at the French National Mapping Agency (IGN) specializing in Natural Language Processing for Cartography within the MEIG (Mediation and Enrichment of Geographic Information) research team. Her work bridges computational linguistics and geographic information science, focusing on the intersection of language analysis and spatial data representation. Education : Habilitation à diriger des recherches (HDR) in Natural Language Processing (2020), PhD in Computer Science (2001), DEA in Theoretical Foundations of Computer Systems (1989) Research Interests center on places and placenames , expressive variations in toponym spelling , location-based information retrieval , and sensitive cartography . Her methodology combines data mining , fuzzy spatial reasoning , and geovisualization techniques. Key publication trends show her work connects NLP for map analysis , mountain rescue support systems , and narrative-based mapping . She contributes to heterogeneous data integration and fuzzy logic applications in geographic contexts. Supervision : Co-supervised Dong Chuanming (PhD) and previously supervised Firas Bessadok (PhD) Projects : Core member of the CHOUCAS project (2017-2022) integrating heterogeneous data for mountain rescue, and involved in MATRICIEL (2016-2017) mapping migrant perceptions

• Computer Science
• Artificial Intelligence
• Machine Learning
• +9 more

Prof. Dr. Hayri Sever is a full-time faculty member at Çankaya University, Department of Computer Engineering, since 2018. He previously held professorship roles at Hacettepe University (1997-2018), University of Massachusetts Amherst (2000-2002), and Başkent University (2003-2007). His academic journey includes a PhD in Computer Science from the University of Louisiana at Lafayette (1995), a Master's from the University of Maine (1991), and a BSc in Computer Engineering from Hacettepe University (1987). Education: PhD (1995), Computer Science, University of Louisiana at Lafayette MS (1991), Computer Science, University of Maine BSc (1987), Computer Engineering, Hacettepe University Prof. Sever's research spans Artificial Intelligence, Data Mining, Natural Language Processing, Cybersecurity, and Machine Learning . His recent work focuses on applications in violence detection in videos, sentiment analysis, blockchain security, medical decision support systems, and Turkish language processing. He has supervised over 40 graduate students in topics ranging from deep learning to blockchain and IoT security. His 15 most recent publications (2019-2024) highlight expertise in deep learning for video analysis , blockchain reputation systems , 3D CNN applications , and sentiment analysis in Turkish/Arabic . Collaborations include institutions like Hacettepe University, University of Massachusetts, and international projects funded by TÜBİTAK, UNESCO, and the UN. He has led projects such as "KGYS: Smart Support Software Development" and "DiCoMa: Disaster Control Management" , with roles as researcher, coordinator, and advisor. Professional memberships include IEEE (2005-), ACM (2002-), and Tübitak (2005-).

Florian HuberView profile

Professor

• Data Science
• Machine Learning
• Visual Analytics
• +4 more

Prof. Dr. Florian Huber serves as Professor of Data Science and Visual Analytics at the Department of Media, Faculty of Media, Hochschule Düsseldorf University of Applied Sciences (HSD), a position held since September 2021. He is additionally affiliated with the Center for Digitalization and Digitality (ZDD), where he bridges data science methodologies with real-world applications in scientific and cultural domains through machine learning and visualization techniques. His academic foundation includes: Diploma in Physics, University of Leipzig PhD in Biological Physics, University of Leipzig (2012), thesis: "Emergent structure formation of the actin cytoskeleton" Prior to HSD, he worked as a postdoctoral researcher at AMOLF (Amsterdam) and TU Delft, founded the sustainable sweets company KÄNDI, and served as Data Scientist at the Netherlands eScience Center (2018-2021). Huber's research focuses on developing data science algorithms for comparative analysis of complex datasets, combining machine learning with domain expertise to enable meaningful comparisons through latent space transformations. Key areas include mass spectrometry-based metabolomics (chemical identification/similarity prediction), natural language processing (e.g., lyrical evolution analysis), and audio data science, with emphasis on interpretable and reproducible computational approaches for biology, medicine, and cultural studies. Analysis of his 2023-2025 publications reveals dominant themes in mass spectrometry data science (tools like matchms, MS2Query, Mass2SMILES) and NLP applications in musicology. His work increasingly integrates deep learning for cross-domain similarity prediction while creating educational resources, reflecting a trend toward versatile open-source solutions for interdisciplinary data challenges. Huber actively contributes to open science through GitHub repositories and reproducible pipelines, suggesting collaborative funding models centered on software sustainability. His teaching includes the "Data Science" module (MMI 05.35) with practical components for engineering programs, offered in German or English. He leads initiatives within HSD's Center for Digitalization and Digitality, fostering collaborations between data scientists and domain experts. Current projects involve interdisciplinary teams tackling metabolomics, cultural analytics, and educational tool development using Python-based open-source ecosystems, with future work likely expanding into new application domains while advancing methodological rigor.

Yoshi GotohView profile

Lecturer

• Speech and Language Processing
• Video Analysis
• Information Retrieval
• +2 more

Dr. Yoshi Gotoh is a Lecturer and Student Projects Officer in the Department of Computer Science at the University of Sheffield's School of Computer Science . He holds a PhD from Brown University and a first degree in Engineering from the University of Tokyo. As a member of the Speech and Hearing (SpandH) research group, his work bridges audio-visual processing and language technologies. His core research explores: Video analysis and retrieval systems Natural language generation for video content 3D visual speech animation Crowd behavior modeling through trajectory clustering Medical imaging enhancements via colorization techniques Analysis of his 15 most recent publications reveals strong emphasis on multimodal systems combining computer vision with speech/language processing. Dominant themes include egocentric video analysis, human activity recognition, and cross-modal translation between visual and textual domains. He has secured significant research funding as Co-Principal Investigator: £218,226 from Innovate UK (2021-2024) for fake imagery detection £393,115 from Innovate UK (2018-2021) for unsupervised dubbing systems £284,248 from EPSRC (2001-2005) for spoken language summarization He leads projects within the Speech and Hearing laboratory, focusing on developing computational methods for audiovisual integration and video understanding systems.