جست‌وجوی استاد و پژوهشگر

Mathieu Barthetمشاهده پروفایل

مدرس ارشد

• Music Information Retrieval
• Human-Computer Interaction
• Affective Computing
• +۵ مورد دیگر

Dr. Mathieu Barthet is a Senior Lecturer in Digital Media at Queen Mary University of London (School of Electronic Engineering and Computer Science). He leads research in Music Information Retrieval, Affective Computing, and Extended Reality (XR), with a focus on AI-driven musical interfaces and human-computer interaction. His work spans music emotion recognition, smart instruments, and participatory music systems. Education: MSc in Electronics & Computer Science (Paris VI University/Ecole Polytechnique de Montréal) MSc in Acoustics (Aix-Marseille II University/Ecole Centrale Marseille) PhD in Acoustics, Signal Processing & Computer Science applied to Music (Aix-Marseille II University/CNRS-LMA) Research: Over 100 peer-reviewed publications in fields like audiovisual interfaces, music recommendation, and AI ethics Key projects include the Internet of Musical Things (IoMT), AudioCommons, and mood-driven interactive systems Develops tools for music production collaboration (e.g., Open Symphony, Playsound.space) Grants & Collaborations: Lead on EU Horizon 2020 grants (IoMT, AudioCommons) Co-director of the EPSRC Centre for Doctoral Training in AI & Music Industry partnerships with companies like Music Tribe and Innovate UK Labs & Teams: Member of the Centre for Multimodal AI Conducts research through the Digital Music Lab (DML) initiative

Ekaterina Ivanovaمشاهده پروفایل

مدرس

• Human-Robot Interaction
• Haptic Communication
• User-Centred Robotics
• +۳ مورد دیگر

Dr. Ekaterina Ivanova is a Lecturer (Assistant Professor) in Human-Machine Interaction at Queen Mary University of London's School of Electronic Engineering and Computer Science. She is affiliated with the Centre for Human-Centred Computing, focusing on multimodal human-robot interaction, haptic communication, and user-centred robotics for medical applications. Her research integrates robotics, data science, neuroscience, and clinical expertise to develop assistive technologies. Teaching: She teaches modules such as ECS7029P Research Methods and ECS733P Interactive System Design, emphasizing interdisciplinary and hands-on approaches. Research Interests: Her work spans haptic communication, neurorehabilitation, and robot-assisted motor learning. Recent studies explore human-robot collaboration in training, sensory augmentation, and motor performance optimization. Grants: Improving motor learning via haptic human-human and human-robot interaction (Royal Society, £12,000) Multimodal human-human Interaction in Negotiation Tasks (Royal Society, £12,000) UKRI CDT in Artificial Intelligence and Music (EPSRC, £6.5M) Labs/Teams: Active contributor to the Centre for Human-Centred Computing, collaborating on projects like supernumerary limb systems and haptic social interaction.

Jinglan Zhangمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Big Data Analysis
• Computer Human Interaction
• +۷ مورد دیگر

Dr. Jinglan Zhang is a Senior Lecturer at Queensland University of Technology (QUT), specializing in Artificial Intelligence and Information Systems. She holds a PhD from QUT and has over 15 years of research experience, focusing on multimedia processing, big data analysis, and human-computer interaction. Her work spans environmental monitoring, health informatics, and web applications. Dr. Zhang has published over 100 refereed papers and secured over $1M in research funding. She actively supervises PhD and Masters students and mentors colleagues through QUT's mentoring program. Education: Bachelor of Engineering (1987) and Master of Engineering (1990), Beijing University of Aeronautics and Astronautics (now Beihang University), China. Master of Computing (1998–1999), Macquarie University, Australia. PhD (2003), Queensland University of Technology. Research Interests: Artificial Intelligence: Visual/Acoustic Information Processing, Machine Learning. Big Data Analysis and Visualization. Computer-Human Interaction, including accessibility for people with disabilities. Software Engineering, Mobile/Web Applications. Environmental Monitoring using Acoustic Sensors. Academic Contributions: Dr. Zhang has led projects in bioacoustics, healthcare AI, and accessibility tools. She organizes international conferences (e.g., IEEE e-Science 2010) and collaborates with institutions in China and Australia. Her recent work explores transfer learning, data scarcity solutions, and AI ethics. Grants & Advising: Over $1M in research grants, including projects on assistive technologies for people with intellectual disabilities. Supervised 10 PhD and 2 Masters students to completion. Current mentor for 3 PhD students and collaborator on interdisciplinary teams. Labs & Teams: Core member of QUT's Centre for Data Science, leading the Environment and Natural Systems research theme. Active in collaborative initiatives with Chinese universities and research institutions.

Unnikrishna Pillaiمشاهده پروفایل

استاد

• Signal Processing
• Radar Systems
• Machine Learning
• +۳ مورد دیگر

Unnikrishna Pillai is a Professor of Electrical Engineering at the Tandon School of Engineering, New York University since 1995. He previously held roles at Polytechnic Institute of New York from 1985. His research focuses on radar signal processing, synthetic aperture radar (SAR), autonomous systems, machine learning, and portfolio risk management. He has co-authored five textbooks and developed over 250 educational YouTube videos. Education: Ph.D. in Systems Engineering (University of Pennsylvania, 1985), M.S. in Electrical Engineering (IIT Kanpur, 1982), B.Tech. in Electronics Engineering (Banaras Hindu University, 1980). Research emphasizes signal decomposition for clutter mitigation, waveform design for radar systems, and dynamic swarming for vehicle coordination. His work bridges theoretical contributions with practical applications in defense and transportation. Teaching contributions include supplementary video lectures on electrical engineering topics. Non-academic interests include cultural projects like translating the Bhagavad Gita and producing classical Indian music recordings.

Richard Zhaoمشاهده پروفایل

استادیار

• Serious Games
• Artificial Intelligence
• Immersive Technologies
• +۳ مورد دیگر

Richard Zhao is an Assistant Professor in the Department of Computer Science at the University of Calgary. He leads the Serious Games Research Group , focusing on developing immersive technologies (e.g., VR, eye-tracking) and AI algorithms to enhance training, learning, and education through serious games. His work emphasizes adaptive experiences and automated content generation for educational applications. Research Interests: Serious Games for Education Artificial Intelligence in Virtual Environments Immersive Technologies (VR/AR) Collaborative Systems Procedural Content Generation Recent Work Trends: Recent publications highlight advancements in AI-driven poetry generation, VR interaction techniques, and collaborative problem-solving models. His work often bridges computational methods with human-centric design, particularly in manufacturing simulations and educational VR systems. Labs/Teams: He oversees the Serious Games Research Group, hosting projects like the Immersive Virtual School (IVS) and GEARS: Gearing up for Education . These initiatives integrate AI and VR to address real-world training challenges in engineering and education. Teaching: Teaches graduate and undergraduate courses on Game AI, Game Development, and introductory computer science. Courses include CPSC 686 (Research in Game AI) and CPSC 586 (Game AI) .

• Explainable AI
• Data Mining
• Clustering
• +۳ مورد دیگر

Riccardo Guidotti is an Assistant Professor in the Department of Computer Science at the University of Pisa, Italy, and a member of the Knowledge Discovery and Data Mining Laboratory (KDDLab). He holds a PhD in Computer Science from the University of Pisa, focusing on Personal Data Analytics. His research spans explainable AI, clustering, and transactional data analysis, with notable contributions to black box model interpretation and privacy risk assessment in mobility data. He has been an IBM Research intern and recipient of awards including the IBM Fellowship (2014), multiple ISTI Young Researcher Awards (2016–2019), and the NGDS Award (2018). Education: PhD in Computer Science, University of Pisa (2014) Master of Science in Computer Science, University of Pisa (2013, cum laude) Bachelor of Science in Computer Science, University of Pisa (2010, cum laude) Research interests include personal data mining, clustering techniques, and explainable models. He has led projects like SoBigData++ and XAI, focusing on social mining and trustworthy AI. His work emphasizes transparency in AI systems and their societal impact. Recent publications explore explanation methods for neural networks and privacy-aware data analysis. Awards: IBM Fellowship Award 2014 ISTI Young Researcher Awards (2016–2019) Next Generation Data Scientist Award (NGDS) 2018 He contributes to courses like Data Mining and Logica per la Programmazione at the University of Pisa. Collaborations include work with the CNR's Information Science and Technology Institute and international conferences such as ECML PKDD and DSAA. His research bridges theory and practice, addressing challenges in AI ethics and scalable data analytics.

Bhismadev Chakrabartiمشاهده پروفایل

استاد

• Social Neuroscience
• Autism Spectrum Disorders
• Gut-Brain Axis
• +۲ مورد دیگر

Bhismadev Chakrabarti is Professor of Neuroscience and Mental Health at the University of Reading's School of Psychology and Clinical Language Sciences. He leads research on human social behavior mechanisms, focusing on neural and gut microbiome influences. His work utilizes eye-tracking, MRI, psychophysics, and psychophysiology methodologies. Research examines: Neurobiological foundations of social cognition Autism spectrum heterogeneity Gut-brain axis interactions Cross-cultural emotion processing Neurodiversity across lifespan Recent publications demonstrate strong focus on autism phenotyping through multimodal approaches including neuroimaging, behavioral tasks, and computational modeling. Predominant themes include sensory integration differences, cross-cultural validation of traits, and transdiagnostic assessment frameworks. Neurodevelopmental trajectories and biomarker discovery are recurrent investigative threads. Laboratory activities coordinate with the Centre for Autism and Centre for Integrative Neuroscience and Neurodynamics (CINN), fostering interdisciplinary collaborations on neurodiversity research.

Nicholas Martin Fordمشاهده پروفایل

استادیار

• Economic History
• Long-term Economic Growth
• Productivity
• +۲ مورد دیگر

Nicholas Martin Ford serves as an Assistant Professor of Economic History in the Department of Economics at the University of Southern Denmark (SDU), affiliated with VIP and the Historical Economics & Development Group (HEDG). His research integrates econometric methods with artificial intelligence to analyze long-term economic trends through historical data. He holds a master's degree in economics from SDU and a PhD in economic history from Lund University. Ford currently acts as an External examiner for Censorkorpset (April 2022–March 2026). His research centers on Scandinavian higher education development (1800–1929), human capital formation, and productivity growth. Using machine learning to process historical records, he investigates how educational systems influenced economic trajectories, with particular focus on Denmark's institutional evolution and knowledge economy origins. Recent publications reveal consistent methodological innovation: his 2025 doctoral thesis establishes frameworks for measuring historical human capital, while 2022–2023 journal articles demonstrate novel applications in cultural transmission and educational metrics. These works collectively advance quantitative approaches to pre-modern economic analysis. Ford teaches International Economics (January 2025–August 2026) and actively contributes to HEDG's research agenda on historical economic development through collaborative projects and data-driven scholarship.

• Computational creativity
• Computational linguistics
• Artificial intelligence
• +۳ مورد دیگر

Björn Gambäck is a Professor of Language Technology at the Department of Computer Technology and Informatics, NTNU. His research focuses on computational creativity, computational linguistics, artificial intelligence, and machine learning, with a strong emphasis on natural language processing (NLP) and language technology. He actively contributes to the academic community through teaching courses such as 'Intelligent Text Analysis and Language Comprehension' and supervising master's theses. His work includes advancing techniques for sentiment analysis, code-mixed language processing, and computational creativity. Recent research highlights include developing deep learning models for code-mixed social media analysis and exploring coreference resolution in entity-level sentiment tasks. Gambäck’s contributions span interdisciplinary areas, such as applying evolutionary algorithms to media repositories and music composition. Notable collaborations include projects on hate speech detection, sarcasm annotation in tweets, and named entity recognition for low-resource languages like Amharic. His expertise bridges theoretical advancements and practical applications in NLP and computational systems.

• Aeroacoustics
• Noise Control Engineering
• Signal Processing
• +۳ مورد دیگر

Elias Zea Marcano is an Assistant Professor of Engineering Acoustics at the Marcus Wallenberg Laboratory for Sound and Vibration Research (MWL) at KTH Royal Institute of Technology. His research focuses on noise source separation, aeroacoustics, room acoustics, sparse signal processing, and data-driven methods. He reviews for prominent journals like the Journal of the Acoustical Society of America and serves as a Guest Editor for the Journal of Theoretical and Computational Acoustics. His work is supported by the Swedish Research Council and the European Commission. He teaches courses such as Room Acoustics and Spatial Audio and has supervised numerous graduate students and postdocs in areas like sustainable aviation noise reduction and acoustic material characterization. He actively participates in international conferences and publishes cutting-edge research on topics such as fan noise measurement and data-driven speech enhancement.

Hugo Maruri-Aguilarمشاهده پروفایل

مدرس

• Algebraic Statistics
• Computer Experiments
• Statistical Data Analysis
• +۴ مورد دیگر

Dr Hugo Maruri-Aguilar is a Lecturer in Statistics at Queen Mary University of London, affiliated with the School of Mathematical Sciences and the Centre for Probability, Statistics and Data Science. His research focuses on algebraic statistics, computer experiments, and statistical modeling using likelihood and penalized likelihood techniques. He explores design of experiments through algebraic methods and develops emulators for computer experiments using polynomial models regularized by smoothness. His research interests include Lasso regression, topological data analysis, and applications in music generation and healthcare. Recent work emphasizes model selection and sparse polynomial prediction. He has collaborated on projects ranging from biomarker prognostic models in heart failure to AI-driven music composition. Dr Maruri-Aguilar has contributed to over 15 peer-reviewed publications across journals like Statistical Papers , Annals of the Institute of Statistical Mathematics , and Technometrics . His methodologies address challenges in optimal design, computational topology, and interdisciplinary applications in music and medicine.

Vincenzo Nicosiaمشاهده پروفایل

مدرس ارشد

• Complex Networks
• Multilayer & Multiplex Networks
• Network Dynamics
• +۷ مورد دیگر

Vincenzo Nicosia is Senior Lecturer in Networks and Data Analysis at the School of Mathematical Sciences, Queen Mary University of London, and a member of the Centre for Complex Systems. His research deciphers the structure and dynamics of complex networks, with particular emphasis on multilayer and multiplex systems, random-walk processes, synchronisation, and their applications to urban analytics, neuroscience and epidemic modelling. Education & early career: While explicit degrees are not listed in the supplied text, Dr Nicosia has built an extensive publication record (100+ papers) since 2006, indicating long-standing academic training and international recognition in network science. Research interests: Nicosia’s work revolves around three inter-related pillars: Fundamental theory: random walks, diffusion, opinion dynamics, synchronisation and percolation on single and multilayer networks Methodological development: visibility graphs, first-passage observables, metadata-dependent embeddings, spectral and entropy-based metrics Data-driven applications: quantifying urban segregation and epidemic disparities, modelling cancer-spatial evolution, mining musical harmony networks, and analysing brain multiplex motifs His recent publications (2020-2023) reveal a strong focus on spatial stochastic processes —using random walks to measure segregation, mutation clustering in tumours, and the impact of city layout on COVID-19 spread—and on algorithmic inference in multiplex structures, including optimal percolation and compressed network representation. Grants & awards: He currently holds / has led EPSRC grant "Assessing spatial heterogeneity through random walks on graphs" (£162,886, 2019-2021). No other awards or fellowships are mentioned in the supplied material. PhD supervision & team: He advises an active cohort of doctoral researchers: Liam Fahey (temporal knowledge graphs), Yuhan Li (adaptive epidemic modelling), and Tom Roberts (stochastic sampling on lattice animals), among others. Outreach & service: Nicosia serves on the Council and Executive Committee of the Complex Systems Society, contributing to the governance and strategic direction of the international community.

Carl Verschuurمشاهده پروفایل

استاد

• Hearing Science
• Biomedical Engineering
• Sensory Substitution
• +۳ مورد دیگر

Professor Carl Verschuur is a leading academic at the University of Southampton, specializing in hearing science and cochlear implant technology. His research focuses on enhancing auditory perception through sensory substitution systems and investigating inflammatory mechanisms in age-related hearing loss. Member of the Institute for Life Sciences Active projects include DSE Award for medical device translation, Flexi Grant, and RNID Studentships Key research areas include cochlear implant optimization , inflammatory biomarkers for hearing loss , and tactile speech perception systems . His recent publications address noise-robust signal processing and electrode integrity testing. External engagements include invited speaking roles at the University of Manchester (2011) and presentations on topics ranging from music perception in implant users to PPIE initiatives in auditory research.

• Computer Science
• Mobile Computing
• Human-Computer Interaction
• +۴ مورد دیگر

Peter Reichl is a Professor in the Faculty of Computer Science, leading the Research Group Cooperative Systems. His work spans interdisciplinary research in Human-Computer Interaction, IoT systems, and Digital Ethics. He actively contributes to the UN Sustainable Development Goals through innovative technology solutions. Key projects include the European Distributed Data Infrastructure for Energy, COMPASS, and symbIoTe. Research interests include mobile computing, tangible interfaces, quantum computing education, and ethical implications of digital transformation. Over 134 publications since 2008 reflect his work in music technology, IoT frameworks, and socio-technical systems analysis. Recent articles highlight creative tech solutions like LEGO-based music sequencers and philosophical explorations of Digital Humanism. He has organized major events like the Dagstuhl Workshop on Quality of Experience (QoE Vadis?) and contributed to policy discussions on digital democracy in Austria. Key Projects: symbIoTe (2016-2018), COMPASS (IoT solutions), European Energy Data Infrastructure (2023-2025) Notable Activities: Panellist on Digital Transformation Ethics (2024), Poster Presenter for Muco app (2022) Labs/Teams: Research Group Cooperative Systems focuses on IoT interoperability and human-centered tech design.

• Computer Vision
• Artificial Intelligence
• Shape Recognition
• +۷ مورد دیگر

José Javier Valero Más is a faculty member at the University of Alicante, affiliated with the Department of Software and Computing Systems within the School of Engineering. He holds the academic rank of Assistant Professor (Profesor Ayudante Doctor) and is actively engaged in teaching and research. His institutional affiliations include the Institute of Informatics Research (I.U. Investigación Informática) and the Shape Recognition and Artificial Intelligence research group. Academic Background: PhD in Computer Science, University of Alicante (2017) Máster in Sound and Music Computing, Universitat Pompeu Fabra (2013) M.Sc. in Telecommunications Engineering, Cork Institute of Technology (2012) Ingeniero en Telecomunicación, Universidad Miguel Hernández de Elche (2012) Ingeniero Técnico en Telecomunicación (Sonido e Imagen), University of Alicante (2009) Máster in Teacher Training, Universidad de Murcia (2020) Research Interests: José Javier Valero Más specializes in computer vision and artificial intelligence, with a particular focus on shape recognition. His work extends into sound and music computing, digital sound synthesis, and multimedia systems. His interdisciplinary background bridges engineering, computer science, and digital arts, enabling innovative approaches in both visual and audio domains. His research integrates programming, data structures, and signal processing techniques to solve complex problems in recognition and synthesis. Scientific Awards: No awards were mentioned in the provided text. Advising and Grants: There is no explicit information regarding graduate student advising, research grants, or funded projects in the provided materials. However, his affiliation with active research groups suggests participation in collaborative research initiatives. Labs and Teams: José Javier Valero Más is a member of the Shape Recognition and Artificial Intelligence research group at the University of Alicante. This group operates under the Institute of Informatics Research (I.U. Investigación Informática), focusing on pattern recognition, machine learning applications in visual data, and intelligent systems. The group fosters interdisciplinary research and contributes to both academic and applied technological advancements.