Anthony Adeyemi-Ejeye is an Associate Professor in the Department of Music and Media at the Faculty of Arts, Business and Social Sciences, University of Surrey. His research focuses on technical and creative aspects of video quality, streaming technologies, immersive media experiences, and video compression methodologies. Key research areas include: Development of immersive media systems Video quality assessment and optimization Streaming protocol innovations Advanced video compression techniques
Bo Wu is a Researcher at the MIT-IBM Watson AI Lab in Cambridge, MA, where he conducts pioneering research in deep learning, computer vision, natural language processing, and multimodal learning. Previously, he served as a postdoctoral research scientist at Columbia University after completing his Ph.D. at the Chinese Academy of Sciences (CAS) in Beijing, with additional research experience at Microsoft Research Asia (MSRA) and Academia Sinica. His academic foundation includes: Ph.D. in Computer Science, Chinese Academy of Sciences (CAS) Research internships at Microsoft Research Asia and Academia Sinica Wu's research focuses on advancing situated reasoning in real-world contexts, integrating neuro-symbolic approaches with deep learning for enhanced interpretability. His work spans video question answering, temporal forecasting, and multimodal understanding, with applications in social media prediction, enterprise AI, and personalized dialogue systems. He emphasizes bridging symbolic reasoning with neural networks to develop robust systems capable of handling open-world knowledge and dynamic environments. Analysis of his recent publications reveals three dominant trends: the creation of novel benchmarks for situated video reasoning (STAR, SOK-Bench), development of efficient multimodal architectures for enterprise applications (Granite Vision), and personalization techniques for language models. His research consistently merges computer vision with linguistic understanding while addressing practical constraints like real-time processing and model compression, demonstrating strong industry-academia translation. His scientific excellence is evidenced by prestigious recognitions including: IBM Master Inventor Award (2023) IBM Research Level-A Accomplishment Award (2021) ACL Best Demo Paper Award (2020) ICIP Prediction Challenge Champion (2020) Alibaba Global Vision AI Challenge Top 3 (2018) NIST TAC SM-KBP Top 1 (2019) Wu actively mentors emerging talent, currently recruiting students for vision-language projects. He provides significant academic service as Area Chair for ACM Multimedia, Senior Program Committee Member for AAAI and IJCAI, and organizer of the SMP Challenge at ACM Multimedia since 2017. His leadership extends to CVPR workshops on Multimodal Foundations Models (MMFM) and Multimodal Video Content Understanding (MVCS), while serving on program committees for NeurIPS, CVPR, ACL, and other top-tier conferences. As a core member of the MIT-IBM Watson AI Lab, Wu operates within a unique industry-academia ecosystem that fosters rapid translation of fundamental research into practical applications. His collaborative work with Chuang Gan and other researchers leverages IBM's computational resources and MIT's academic rigor, positioning him at the forefront of enterprise AI innovation where theoretical advances directly address real-world business challenges.
Josh McDermott is a Professor in the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology (MIT), where he also serves as Associate Department Head and previously as Interim Department Head. He leads the Laboratory for Computational Audition and conducts groundbreaking research at the intersection of psychology, neuroscience, and engineering, with a primary focus on understanding human auditory perception and its computational underpinnings. Dr. McDermott's educational background includes: PhD in Brain and Cognitive Sciences from MIT (2001-2006), advised by Edward Adelson MPhil in Computational Neuroscience from University College London (1998-2000), advised by Geoff Hinton B.A. in Brain and Cognitive Science, summa cum laude, from Harvard University (1994-1998), advised by Nancy Kanwisher McDermott's research program centers on understanding how humans derive information from sound in complex environments. His lab investigates why biological auditory systems outperform even the most sophisticated machine hearing systems in everyday situations like noisy city streets. He explores computational audition, natural sound statistics, music perception, and the relationship between sensory modalities, with long-term goals to improve treatments for hearing impairment and enable the design of machine systems that mirror human auditory abilities. His work bridges fundamental neuroscience with practical applications for hearing technologies. His recent publications reveal a strong trend toward integrating deep learning and neural network models with auditory neuroscience. His lab has been exploring how generative models, metamers, and task-optimized networks can illuminate human auditory processing. There's a clear progression from basic auditory phenomena to more complex computational models that bridge neuroscience and artificial intelligence, particularly in areas like sound segregation, music perception, and cross-modal integration. His work consistently demonstrates how computational approaches can reveal fundamental principles of auditory perception. Dr. McDermott has received numerous prestigious awards, including: Troland Research Award (2018) NSF CAREER Award (2015) APAN Young Investigator Award (2017) BCS Awards for Excellence in Undergraduate Advising (2014, 2018) BCS Award For Diversity, Equity, Inclusion and Justice (2023) As an advisor, McDermott has mentored numerous PhD students who have gone on to make significant contributions in auditory neuroscience and computational modeling. His Laboratory for Computational Audition serves as a hub for interdisciplinary research that bridges psychology, neuroscience, and engineering approaches to understanding hearing. The lab has developed innovative methodologies for studying auditory perception, including sound synthesis techniques, computational models, and cross-cultural approaches to music cognition. McDermott's work continues to push the boundaries of our understanding of auditory perception and its computational underpinnings.
He Kong is an Associate Professor at Southern University of Science and Technology (SUSTech), affiliated with the School of Automation and Intelligent Manufacturing, where he also serves as Deputy Director of the SUSTech Institute of Robotics. Previously, he was an Assistant Professor in the Department of Mechanical and Energy Engineering at SUSTech from January to May 2022. Prior to joining SUSTech, he was a Research Fellow at the Australian Centre for Field Robotics, University of Sydney (2016-2021), and at Cranfield University's Advanced Vehicle Engineering Centre (2015-2016). He received his Ph.D. in Electrical Engineering from the University of Newcastle, Australia (2014), M.E. in Control Science and Engineering from Harbin Institute of Technology (2010), and B.E. in Electrical Engineering and Automation from China University of Mining and Technology (2004). His research focuses on robotic intelligent perception and decision making, robot audition, optimal filtering and estimation, and advanced control methods. Specifically, he works on active multi-mode perception, parameter calibration of robot audition systems, optimal filtering under unknown inputs, and fully actuated system approaches. His work has significant applications in precision agriculture, environmental monitoring, and robotic inspection of hazardous industries such as chemical and mining operations. His recent publications reveal a strong emphasis on multi-modal perception systems, particularly combining visual and auditory sensing for robotic applications. His research shows a progression from theoretical control methods toward practical implementations in field robotics, with increasing focus on real-world applications in agriculture and hazardous environments. Finalist for Youth Author Prize, IFAC Workshop on Robot Control (2019) Fifth China Robotics Academic Annual Conference Best Poster Award (2024) 14th International Conference on Indoor Positioning and Indoor Navigation Best Paper Award (2024) The Equity Scholarship, Council of International Students Australia (2011) Outstanding Postgraduate Students Award, Harbin Institute of Technology (2010) Professor Kong actively supervises numerous PhD and Master's students and has established a productive research group focused on active intelligent systems. His laboratory is equipped with advanced facilities including over 30 motion capture systems, Unitree humanoid robots, robot dogs, wheeled mobile robots, and custom-developed platforms like Cubli and acoustic perception systems. He serves on editorial boards for several prestigious journals including IEEE Robotics and Automation Letters and IEEE Sensors Letters, and has been an Associate Editor for major robotics conferences such as IEEE ICRA and IEEE/RSJ IROS.
Dr. Stuart James is a Lecturer in the Composition and Music Technology program at the Western Australian Academy of Performing Arts , part of Edith Cowan University . His career spans composition, sound design, performance, and music technology development, with a focus on integrating acoustic and electronic media. Education: PhD (2015) in Spatial Audio and Synthesis Master of Arts in Creative Arts (2006) Bachelor of Music with First Class Honours (2001) Certificate in Jazz (1995) Research Interests encompass Music Composition with a blend of acoustic and electronic instruments, Immersive Audio-Visual Systems , Wave Terrain and Spectral Synthesis , and Digital Signal Processing for performance. His work explores Electroacoustic Performance Practice and Networked Music Environments . Publication Trends reflect a focus on experimental composition , interactive notation (notably iPad-based systems like the Decibel ScorePlayer), and spatial audio techniques . Collaborative projects with ensembles such as Decibel and artists like Erin Coates and Louise Devenish highlight his interdisciplinary approach. Scientific Awards include the ASME Young Composers' Competition State Finals Winner , the Dorothy Ransom Composition Prize , and nominations for Australian Music Centre Awards . Professional Contributions include founding membership in the Decibel Ensemble , managing a commercial recording studio for artists like ShockOne, and developing networked music notation software . He has supervised PhD projects on topics like meditative sonic praxis and timbre analysis .
Bruce DENBY is a Professor at Sorbonne University specializing in speech processing, telecommunications, and indoor localization. His research spans multiple disciplines including computer science, physics, and environmental science with significant contributions across these fields. His primary research interests include: Silent Speech Interfaces and speech restoration technologies Indoor localization using wireless networks and GSM fingerprints Telecommunications and signal processing Environmental modeling of road dust and air pollution Machine learning applications in speech recognition Dr. DENBY's research trajectory shows evolution from early work in high energy physics to speech processing and wireless communications, with recent publications (2022-2025) demonstrating continued innovation in WiFi analytics, client density mapping, and future speech interfaces. His work increasingly integrates deep learning techniques while maintaining focus on practical applications, particularly for speech restoration and privacy-preserving network analysis. Notable scientific achievements: Chester Sall Award Paper (2012) for work on FPGA-based FM broadcast receivers Significant contributions to the Silent Speech Challenge benchmark with deep learning approaches Development of the NORTRIP model for road dust emissions Highly cited work on Silent Speech Interfaces (over 500 citations) Dr. DENBY has secured research funding across multiple domains, collaborating with institutions across Europe. His work demonstrates strong interdisciplinary connections between speech technology, wireless communications, and environmental science, with applications ranging from assistive technologies to urban air quality management.
Professor Simeon Keates serves as Dean of the School of Engineering and The Built Environment at Edinburgh Napier University. With an academic background including MA (Cantab) and PhD (Cantab), he is also a Fellow of the Institution of Engineering and Technology (FIET). His leadership extends across engineering disciplines with a strong focus on digital innovation and accessibility. His research spans Interaction Design , User Experience , and Internet of Things with particular emphasis on information freshness, assistive technologies, and universal access. His work bridges theoretical networking concepts with practical applications for real-world systems, especially in the domains of speech processing and real-time information delivery. Analysis of his publication trends shows consistent contributions to human-computer interaction, with growing focus on information freshness metrics in IoT systems. His research demonstrates interdisciplinary connections between networking theory, accessibility technology, and real-time system optimization. As an academic supervisor, he has guided postgraduate research including Mohd Al Malki's 2017 doctoral work on road safety management in Qatar. His professional activities reflect commitment to both theoretical research and practical applications that address societal challenges through technological innovation.
Assoc. Prof. Dr. Gintautas Tamulevičius serves as Director of the Institute of Data Science and Digital Technologies at Vilnius University. His primary affiliation is with the Image and Signal Analysis Group, where he contributes as a Senior Researcher and Chief Researcher in projects. Doctor of Science in Technology (2008) Pedagogical Title: Associate Professor (2014, Vilnius Gediminas Technical University) Active in IEEE Computer Society and Signal Processing Society Dr. Tamulevičius specializes in speech signal processing, with research spanning three core domains: Speech Modeling : Autoregressive/linear prediction, nonlinear fractal modeling, non-parametric approaches Recognition Systems : Deep learning-based methods, Hidden Markov models, Wave-U-Net architectures Quality Assessment : Voice phonation evaluation, vocal fold condition analysis using acoustic methods His publication trends show strong focus on: Deep learning applications for speech processing 2D feature space analysis for recognition tasks Fractal dimension-based emotion classification Language preservation through technological development Human-centered AI applications Biomedical signal processing As an educator, he has taught: Digital Signal Processing (VGTU 2012–present) Speech Signal Processing (VGTU 2008–present) Data Visualization (VGTU 2015) User Interface Design (VU 2018–present) Audio Signal Processing (VU 2020–present) His editorial contributions include reviewing for: Informatica IEEE Access Neurocomputing Baltic Journal of Modern Computing Nonlinear Analysis: Modeling and Control IEEE Journal of Biomedical and Health Informatics International Journal of Applied Mathematics and Computer Sciences He has supervised doctoral research including: Daniel Zakševski (2023–2027): Deep learning models for speech enhancement Monika Danilovaitė (2020–2026): Voice quality assessment methods Tatjana Liogienė (2012–2016): Multistage speech emotion classification
Naimul Khan is an Associate Professor and Associate Chair in Graduate Studies at Toronto Metropolitan University's Department of Electrical, Computer, and Biomedical Engineering. His research focuses on practical applications of machine learning, medical imaging, computer vision, and augmented/virtual reality technologies to solve real-world healthcare and multimedia challenges. PhD (Toronto Metropolitan University, 2014) MSc (University of Windsor, 2010) BSc (Bangladesh University of Engineering and Technology, 2008) His research interests span medical imaging automation, virtual reality systems for healthcare applications, and machine learning techniques for biomedical signal processing. He has developed innovative algorithms for ultrasound analysis, emotion recognition systems using EEG data, and segmentation approaches for diabetic foot ulcers. Publications demonstrate consistent focus on practical implementations across computer vision, medical diagnostics, and extended reality (XR) technologies. Scientific contributions include a Best Paper Award at the IEEE International Symposium on Multimedia (2017) and prestigious postdoctoral and graduate scholarships. He actively collaborates with industry partners through the TMU Multimedia Research Laboratory, teaching graduate courses like ELE 725 (Basics of Multimedia Systems) and COE 318 (Software Systems). Best Paper Award, IEEE International Symposium on Multimedia (2017) OCE TalentEdge Postdoctoral Fellowship (2014-2016) Ontario Graduate Scholarship (2013-2014) Queen Elizabeth II Scholarship in Science & Technology (2012-2013) As co-director of the TMU Multimedia Research Laboratory, Khan bridges academic research with industry applications through practical implementations of machine learning and extended reality technologies in healthcare domains.
Oluwaseun Priscilla Olawale is a Lecturer in Software Engineering at Near East University (Nicosia, Cyprus), where she is concurrently pursuing her master's degree in Software Engineering. She previously served as a teaching assistant at the same institution. Her academic background includes a Bachelor's degree in Computer Science from Bowen University, Nigeria, complemented by professional experience as an IT Specialist at the Standards Organisation of Nigeria and Software Developer at Royal Niger Company. Research Focus: Her work centers on developing reliable software solutions with emphasis on artificial intelligence applications. Primary domains include healthcare technology (medical imaging, remote healthcare, IoHT security), industrial systems (Industry 4.0 automation), cybersecurity (blockchain integration, anomaly detection), and educational technology (post-pandemic learning environments). She demonstrates consistent interest in interdisciplinary AI implementations bridging theoretical models and practical challenges. Publication Trends: Recent articles (2020-2025) reflect a progression from foundational surveys in data mining and blockchain verification toward applied AI solutions. Emerging themes include disaster prediction systems, audio-visual signal processing, and security-enhanced healthcare networks. Her technical approach frequently employs deep learning architectures (CNNs, VGG-16), blockchain frameworks, and signal processing techniques to address real-world problems across diverse sectors.
Giorgos Stamou is a Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), and a Visiting Professor at the MIT Sloan School of Management and MIT Open Learning. He directs the Artificial Intelligence and Machine Learning Systems Laboratory (AILS Lab) and has been a senior researcher at the Institute of Communications and Computer Systems (2000-2008) and an academic visitor at Oxford University (2011-2012). His research spans Deep Learning , Explainable AI , and Knowledge Representation , with a focus on Large Language Models and Multimodal Learning . He has coordinated over 60 funded projects and published 150+ papers. Recent work highlights trends in LLM Evaluation , Gender Bias Mitigation , and Multimodal Music Analysis , reflecting his interdisciplinary approach to AI challenges. Stamou has served on steering committees for W3C working groups (Rule Interchange Format, Web Ontology Language) and contributed to cultural heritage metadata enrichment through the CrowdHeritage projects. He founded NTUA's MSc program in Data Science and Machine Learning (2018-2022) and has organized conference tracks on riddle-solving frameworks and hallucination detection.
Luis Arturo Cavazos Quero is an Assistant Professor in the Department of Computer Science and Engineering at Sejong University , South Korea. His research agenda centers on Human–Computer Interaction , accessibility for visually impaired users , and multimodal interaction technologies . Education: Ph.D. in Computer Science, Sungkyunkwan University (2022) M.S. in Computer Science, Sungkyunkwan University (2015) B.S. in Computer Science, Tecnológico de Monterrey (ITESM) (2010) Research Focus: Dr. Cavazos Quero designs and evaluates interactive systems that enable people with visual impairments to access art, education, and mobility. His projects include multimodal interfaces for artwork exploration, voice-based navigation aids, tactile map applications, and Braille text-entry tools for mobile devices. Recent Publications Trend: Since 2021 his output has concentrated on two parallel tracks: (1) assistive technologies that merge tactile, auditory, and olfactory feedback for non-visual art appreciation, and (2) robotics and data security —including quadruped robot control and reversible data-hiding algorithms—showing a broad yet accessibility-driven research portfolio. Scientific Awards: No specific awards are listed in the provided materials. Advising & Grants: Current information on funded projects or doctoral students is not available in the supplied text. Labs & Teams: He completed a postdoctoral fellowship at the Human–Computer Interaction Laboratory, Ewha Womans University (2022–2023) and now leads research activities within his department at Sejong University.
Dr. Jean-Luc Zarader is a Professor at Sorbonne University's Faculty of Engineering , serving as Deputy Director of the UFR of Engineering. Based at ISIR (Institute of Intelligent Systems and Robotics) in Paris, his research focuses on speech processing , neural networks , and binaural sound localization with applications in humanoid robotics and aircraft diagnostics. Key research areas: Speech coding, Nonlinear signal processing, Humanoid auditory systems, Fault diagnosis Active collaborations with teams: ACIDE, MLIA, IRIS Research Trends (2017-1996): 2017-2012: Whale bioacoustics, Aircraft diagnostics, Binaural localization 2007-2000: Speaker verification, Predictive coding, Neural network applications 1999-1996: Doppler lidar analysis, Speech compression His scientific contributions span multiple disciplines, including: Neural network optimization for signal processing Acoustic feature extraction Humanoid robot perception systems Aerospace fault detection Bioacoustic pattern recognition
Qi Feng is an Assistant Professor at the Faculty of Science and Engineering, Waseda University, specializing in deep learning applications for computer graphics and vision, with a focus on virtual and augmented reality. He holds a Doctor of Engineering (2022), Master of Engineering (2019), and Bachelor of Engineering (2017) from Waseda University. Research Interests: His work spans 3D reconstruction, depth estimation, occlusion handling, and multimodal systems integrating eye tracking and speech recognition. Notable projects include SyncViolinist for audio-driven motion generation and the Depth360 dataset for omnidirectional imaging. Publications: Qi's research has been featured in top conferences like CHI, ISMR, ICCVW, and journals such as The Visual Computer. His articles emphasize practical solutions for immersive environments, language learning, and synchronized audio-visual editing. Contact: Email: fengqi@ruri.waseda.jp
Dr. Stavros Nousias is a researcher at the Chair of Computing in Civil and Building Engineering at the Technical University of Munich , focusing on applications of Artificial Intelligence in the Built Environment . His work bridges Knowledge Representation and Reasoning , Geometry Processing , and Machine Learning to advance construction informatics and digital twinning. Research Interests: AI for building evacuation prediction, technical drawing segmentation, BIM optimization, and respiratory disease modeling. Publications: 15+ peer-reviewed articles on topics including graph neural networks for construction simulations, pulmonary airflow analysis, and heritage site monitoring. Supervised Theses: Guided projects on AI-based BIM command prediction and robotized construction simulation . Labs: Active in the BIM-Lab and Robotic Fabrication Lab . Teaching: Co-instructor for courses like Artificial Intelligence in Engineering and Computation in Engineering 1 .
