جست‌وجوی استاد و پژوهشگر

Lorenzo Picinaliمشاهده پروفایل

استاد

• Spatial Acoustics
• Immersive Audio
• Ecoacoustic Monitoring
• +۳ مورد دیگر

Professor Lorenzo Picinali is a faculty member at Imperial College London's Dyson School of Design Engineering, specializing in Spatial Acoustics and Immersive Audio. He leads the Audio Experience Design research group, focusing on perceptual and computational aspects of spatial audio, ecoacoustic monitoring, and clinical hearing applications. His academic journey includes roles at Imperial College London since 2015, previously at De Montfort University (2009–2015). He holds a PhD in Music Technology (2010) and degrees in Auditory Science and Music Communication. Research interests span spatial audio rendering, binaural technologies, hearing science, and ecoacoustic monitoring. His work explores applications like VR-based auditory training for cochlear implant users (BEARS project), the SONICOM HRTF dataset for personalized audio, and MAARU devices for ecological soundscapes. Collaborations span industries and universities, with funding from EU and UK research initiatives. Key publications include advancements in HRTF upsampling via GANs, spatial hearing training, and ecoacoustic recording systems. He advises numerous students and oversees projects like PLUGGY for cultural heritage and HELIX for auditory-cognitive training. His labs develop tools like the 3D Tune-In Toolkit and MAARU units, emphasizing practical applications of audio research.

Nicolas Pugeaultمشاهده پروفایل

دانشیار

• Cognitive Vision
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Dr. Nicolas Pugeault is a Reader in Computing Science at the University of Glasgow's School of Computing. He holds a PhD from the University of Göttingen and has held roles including Postdoc at the University of Surrey and Assistant Professor at the University of Southern Denmark. His research focuses on cognitive vision, machine learning, and visual attention in complex control tasks. He has led projects like the EPSRC-funded DEVA grant and collaborated with institutions like the Alan Turing Institute. Education: Engineer degree from ESIEA Paris, MSc in Computational Intelligence from Plymouth, PhD from Göttingen. Research interests include human vision insights, visual attention modeling, and contextual scene understanding. Awards include the Alan Turing Institute Fellowship (2018–2020). Publications span anomaly detection in medical imaging, contextual object detection, and autonomous driving. His work integrates AI with robotics and environmental applications, such as weather forecasting and plankton analysis. He supervises a diverse cohort of PhD students across vision, robotics, and machine learning. Grants include leadership on EPSRC and InnovateUK projects. Teaching includes modules on Deep Learning and Data Science. His labs and teams contribute to advancements in cognitive vision systems and AI-driven solutions for real-world challenges.

Haimeng Wuمشاهده پروفایل

استادیار

• Power Electronics
• Renewable Energy Systems
• Electric Vehicles
• +۵ مورد دیگر

Haimeng Wu is an Assistant Professor and Head of the Electrical Power and Control Systems at Northumbria University's Department of Mathematics, Physics, and Electrical Engineering. He leads the EPSRC Centre for Doctoral Training (CDT) in Renewable Energy Northeast Universities (ReNU), a collaborative initiative with Newcastle and Durham Universities. Previously, he was part of Newcastle University's Electrical Power Research Group and served as a guest staff member in their School of Engineering. His research focuses on power electronics, renewable energy systems, wireless power transfer, and condition monitoring of wide bandgap (WBG) power modules. He has secured over £1.2 million in research funding, including grants from Innovate UK and The Royal Society. Wu is also an Associate Editor for IET Power Electronics and actively contributes to conferences like IEEE ECCE Europe and ISEEIE. Education: PhD in Power Electronics, Newcastle University (2012–2016) MSc in Hybrid Renewable Energy Distributed Generation Systems, Zhejiang University (2011) BEng in Electrical Engineering (2008) Research Interests: Design and control of power converters for electric vehicles and renewable energy networks Health monitoring and condition control of WBG power modules Wireless power transfer systems and their integration into IoT and smart energy systems His work aligns with UN Sustainable Development Goals, particularly in affordable and clean energy and industry innovation. Grants and Awards: £208,000 Innovate UK grant for a PV-powered outdoor lighting system (2025–2027) £316,000 Newton International Fellowship for IoT-integrated wireless power transfer (2024–2026) Siemens European Competition in Power Electronics (2018) Fellow of the Higher Education Academy (HEA) Advising and Teaching: Supervises four PhD students, as well as MSc and undergraduate students Develops courses in smart energy systems, renewable energy technologies, and analogue electronics His research has produced over 50 peer-reviewed publications and three patents, emphasizing industry collaboration and innovation.

Maija Hirvonenمشاهده پروفایل

استاد

• Accessibility Studies
• Translation Studies
• Multimodal Communication
• +۴ مورد دیگر

Maija Hirvonen is a Full Professor in German language, culture, and translation at Tampere University's Faculty of Information Technology and Communication Sciences. She leads BA, MA, and PhD programs in German linguistics and translation studies. As director of Langnet (Network of Doctoral Programmes in Language Sciences in Finland), she oversees multidisciplinary research initiatives. She co-leads the Tampere Accessibility Unit and the Multimodality in Translation and Interpreting research group, while also serving on the steering board of Plural (Research Centre for Languages and Cultures). Her research focuses on accessibility (especially audio-description), multimodal translation, distributed cognition, and human-centered machine learning. Notable grants include the Horizon Europe-funded NewWorkTech (2024-2027) and the Jane and Aatos Erkko Foundation's Guided Audio Captioning project (2022-2023). Hirvonen has supervised numerous MA and PhD students in German linguistics and translation studies. She actively contributes to academic governance via editorial roles (e.g., SKY Journal of Linguistics) and co-edits key publications like the 57th Linguistic Colloquium proceedings. Her work bridges theoretical research with practical applications in inclusive communication technologies.

Jordan B. L. Smithمشاهده پروفایل

مدرس

• Music Structure Analysis
• Audio Signal Processing
• Music Information Retrieval
• +۴ مورد دیگر

Jordan B. L. Smith is a Lecturer in Audio Signal Processing at Queen Mary University of London's School of Physical and Chemical Sciences. He teaches digital audio courses in the Queen Mary School Hainan program, requiring annual 2-week trips to China. His research focuses on music structure analysis, music decomposition, and interactive tools for musical creativity. Education: BA in Music and Physics (Harvard College) MA in Music Technology (McGill University) MSc in Operations Research Engineering (University of Southern California) PhD in Computer Science (Queen Mary University of London) Research Interests: Jordan explores nested patterns in musical structure, listener perception of structure, algorithmic music generation, and interfaces for music remixing. His work bridges computer science and musicology, with contributions to tools like Unmixer (loop extraction) and CrossSong (musical logic puzzles). Publications Trends: His work emphasizes music structure segmentation, computational creativity, and human-audio interaction. Recent studies focus on scalable music generation (e.g., SymPAC) and improving structural analysis with semi-supervised methods (e.g., MuSFA). Awards & Collaborations: No explicit awards listed, but collaborations include AIST Japan, IRCAM Paris, and TikTok. Past roles include postdoc at AIST (2014–2017) and industry work at TikTok (2018–2022). Labs & Teams: Collaborates with the Centre for Digital Music (C4DM) in EECS. Engages in interdisciplinary projects combining music theory, AI, and user interaction design.

• Software Engineering
• Natural Language Processing
• Data Mining
• +۳ مورد دیگر

Nouh Alhindawi is an Associate Teaching Professor at Arizona State University's School of Computing and Augmented Intelligence. His research focuses on Software Engineering, Natural Language Processing, and Machine Learning applications in education. He actively teaches courses like Software Enterprise, Programming Languages, and Software Factory Capstone projects. Key research interests include improving software testing quality, analyzing code complexity, and leveraging AI for education technology. Notable work includes studies on student performance prediction, IoT security, and Arabic text emotion classification. His teaching portfolio spans both undergraduate and graduate levels, emphasizing practical software development methodologies. Prominent publications address challenges in localization techniques, network security, and deep learning applications across healthcare and education sectors.

Ross T. Whitakerمشاهده پروفایل

استاد

• Image Processing
• Scientific Computing
• Statistical Shape Analysis
• +۷ مورد دیگر

Ross T. Whitaker is a Professor and Director of the School of Computing at the University of Utah, where he is also a faculty member of the Scientific Computing and Imaging (SCI) Institute. He has been a key figure in advancing computational methods in medical imaging, geometry processing, and scientific visualization. University: University of Utah School: School of Computing Institute: Scientific Computing and Imaging (SCI) Institute Education: B.S. in Electrical Engineering and Computer Science, Princeton University (Summa Cum Laude, 1986) Ph.D. in Computer Science, University of North Carolina at Chapel Hill (1994) Research Interests: Ross Whitaker's research spans image analysis, statistical shape modeling, GPU-based PDEs, level-set methods, and uncertainty visualization. His work emphasizes adaptive, data-driven models for image denoising, segmentation, and reconstruction, often using nonparametric statistics and information-theoretic principles. He has pioneered methods in tetrahedral meshing, particle systems for surface sampling, and manifold learning. Recent Publication Trends: His recent publications (2020–2025) highlight a strong focus on statistical shape analysis in medical applications, particularly in metopic craniosynostosis , using tools like DeepSSM and CranioRate . There is a growing emphasis on deep learning, explainable AI, and multimodal sensing (e.g., audio-visual detection of idling vehicles). His interdisciplinary work bridges computer science, biomedical engineering, and environmental health. Scientific Awards: IEEE Fellow NSF Career Award Alumni Scholarship Award (UNC Chapel Hill) Advising and Grants: He leads a graduate research group in image analysis and scientific computing, mentoring students like Miriah Meyer (Ph.D. 2010). His projects are supported by federal agencies and industrial contracts, focusing on shape modeling, medical imaging, and environmental monitoring. He has developed influential software such as ShapeWorks, Seg3D, Cleaver, and contributed to ITK. Labs and Teams: He is a core member of the SCI Institute, a leading interdisciplinary research center in scientific computing and visualization. His team develops computational tools for biomedical image analysis and uncertainty quantification, with applications in surgery, diagnostics, and public health.

• Human-Computer Interaction
• Music Technology
• Digital Instrument Design
• +۷ مورد دیگر

Adam Tindale is an Associate Professor of Human-Computer Interaction at OCAD University’s Faculty of Arts & Science, specializing in electronic musical instruments and innovative interaction design. He is renowned for his work on the E-Drumset, an instrument combining physical modeling, machine learning, and intuitive interfaces. His research bridges music technology, wearable systems, and immersive environments, emphasizing cross-disciplinary collaboration. Education: B.Mus. (Queen's University), M.M. in Music Technology (McGill University), Ph.D. in Interdisciplinary Studies (University of Victoria, focusing on Music, Computer Science, and Electrical Engineering). Research interests include: Design of digital musical instruments Wearable technology for performance VR audio interfaces Gesture-based interaction systems His recent work explores: Generative art through GANs (e.g., calligraphic Arabic scripts) Anisochronous rhythmic interfaces 3D-printed information embedding Key contributions include the JunctionBox toolkit for sound interfaces and frameworks like Flocking for web-based music.

• Digital Signal Processing
• Power Quality
• Motor Current Signature Analysis
• +۵ مورد دیگر

Prof. Levent EREN is a Professor and Vice Rector at İzmir University of Economics, leading the Faculty of Engineering's Electrical and Electronics Engineering department. He holds a Ph.D. from the University of Missouri (2002) and has extensive experience in academia and industry, including roles as Vice Dean (2014–2019) and faculty member at Bahçeşehir University (2003–2012). His research focuses on motor fault diagnosis, power quality, and digital signal processing, with over 30 journal and conference publications. Prof. EREN’s work emphasizes innovative applications of neural networks and wavelet transforms in machinery condition monitoring. He has contributed to EU-funded projects like Future Education and Training in Computing (2013–2014). His administrative roles include managing institutional research and academic policies as Vice Rector since 2019. His teaching spans courses on electrical energy conversion, digital signal processing, and senior engineering projects. He has no explicitly listed awards but is recognized for his technical contributions in motor current signature analysis and bearing fault detection.

Flora Salimمشاهده پروفایل

استاد

• Ubiquitous computing
• Machine learning
• Data science
• +۶ مورد دیگر

Professor Flora Salim is a leading academic at the University of New South Wales (UNSW) Sydney, holding a full Professorship in the School of Computer Science and Engineering. She serves as Deputy Director (Engagement) of the UNSW AI Institute and contributes to multidisciplinary research at the intersection of ubiquitous computing, machine learning, and data science. Human-centred AI and ethical systems Spatio-temporal data modeling Applications for climate resilience and urban mobility Her research focuses on multimodal foundation models, continual learning, and responsible AI deployment in real-world environments. She explores: Time-series and sensor data analysis Wearable and environmental sensing AI for sustainable infrastructure Robustness and trustworthiness in models Recent publications highlight her work on: Transformer-based climate downscaling Cross-modal fairness in mobility Privacy-preserving spatio-temporal generation Patient similarity networks in healthcare She has received multiple competitive fellowships including: Humboldt Fellowship Bayer Fellowship Victoria Fellowship ARC Australian Postdoctoral Industry (APDI) Fellowship Additional accolades include: Women in AI Award Australia and New Zealand (2022) IBM Smarter Planet Industry Innovation Award As Chief Investigator in: ARC Centre of Excellence for Automated Decision Making and Society (ADM+S) ARC Training Centre for Whole Life Design for Carbon Neutral Infrastructure She maintains active collaborations through: Associate position at ELLIS Alicante Visiting Professor roles at University of Kassel (2019-2020) and University of Cambridge (2019) Editorial board memberships in ACM TIST, IEEE Pervasive Computing, and Nature Scientific Data

• Speech Recognition
• Data Augmentation Techniques
• Signal Processing
• +۳ مورد دیگر

Prof. Jovan Galić is an Associate Professor at the Faculty of Electrical Engineering, University of Banja Luka, specializing in the Department of Telecommunications. His research focuses on speech recognition systems, signal processing, data augmentation techniques, and audio communication technologies. He has contributed to over 30 publications since 2010, with recent work emphasizing whispered speech recognition and visible light communication systems. He has participated in multiple international projects, including IoT applications using optical wireless technologies and smart city solutions. Notable collaborations involve华为 (Huawei) and NATO-funded initiatives. His educational contributions include textbooks on telecommunications fundamentals and information theory. Key research trends include applying machine learning to enhance speech recognition accuracy, optimizing feature extraction methods for whispered speech, and evaluating audio quality in emerging communication technologies like visible light systems.

• Computer Vision
• Pattern Recognition
• Digital Cultural Heritage
• +۳ مورد دیگر

FH-Prof. Dr. Markus Seidl is a Professor in the Department of Media and Digital Technologies at the University of Applied Sciences St. Pölten. He leads the Creative Computing (BA) program and serves as interim head of the Media Computing Research Group within the Institute of Creative Media/Technologies. His roles include academic director and former Managing Director (2015–2020). Seidl holds a PhD in Computer Vision from TU Wien (2016) and has extensive experience in interdisciplinary projects, particularly in digital cultural heritage. His research focuses on computer vision techniques for analyzing rock art (petroglyphs) and enhancing museum experiences through multi-touch interfaces and BYOD integration. Key projects include the Europa Nostra-award-winning rock art analysis work and multi-touch table applications exhibited globally. He has supervised over 20 theses on topics like medieval manuscript analysis and AI-driven cultural artifact understanding. Notable contributions span 120+ publications, including work on 3D surface segmentation, flood level estimation from social media, and interactive museum technologies. His work bridges computer science with archaeology, design, and education, emphasizing experiential learning and technology-driven cultural preservation.

Yu-Lin Weiمشاهده پروفایل

عضو هیئت علمی

• Audio Signal Processing
• Wireless Communication
• Indoor Positioning Systems
• +۴ مورد دیگر

Yu-Lin Wei is a Teaching Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC). He holds a Ph.D. from UIUC (2024), and M.S. and B.S. degrees in Computer Science and Information Engineering from National Taiwan University (2016 and 2014, respectively). His research focuses on audio signal processing, IoT systems, wireless/visible light communication, and indoor positioning. He has been honored with the ECE Rambus Fellowship (2020-2021) and was a Young Researcher at the Heidelberg Laureate Forum (2019). He teaches CS/ECE 438: Communication Networks and CS/ECE 434: Real-World Algorithms for IoT and Data Science . His work spans innovations in acoustic augmented reality (Ear-AR), teeth activity sensing via earphones (EarSense), and positioning systems using polarized light (CELLI). His research group, SiNRG, explores signal processing and IoT applications under the advisement of Romit Roy Choudhury. Key contributions include MobiSys Best Demo Runner-up (2017) for CELLI and advancements in sample-constrained optimization for audio personalization. He has contributed to IEEE standards (e.g., 802.15.7r1) and served as a tutorial speaker at MobiCom and conference co-chair roles.

• AI and Machine Learning in Marketing
• Online Word-of-Mouth Analysis
• Unstructured Data Analysis (Text, Image, Video, Audio)
• +۷ مورد دیگر

Shane X. Wang is a Professor of Marketing at the Pamplin College of Business, Virginia Tech, and serves on the academic council of the American Marketing Association. He previously held a faculty position at Ivey Business School, Western University, Canada. His research focuses on AI-driven marketing strategies, unstructured data analysis, statistical methodologies, and fintech applications. Education: Ph.D. in Business Administration, University of Cincinnati M.S. in Statistics, Georgetown University B.S. in Mathematics, University of Toulouse, France Research Interests: Shane’s work bridges artificial intelligence and marketing, emphasizing consumer behavior analysis through novel data sources like text, images, and blockchain. He explores statistical frameworks for mediation and meta-analysis, as well as the intersection of fintech innovations (e.g., Bitcoin, blockchain) with marketing strategies. Editorial Roles: Associate/Area Editor at Journal of Marketing , Journal of the Academy of Marketing Science , International Journal of Research in Marketing , and Journal of Retailing ; Senior Editor at Production and Operations Management . He also serves on the editorial review boards of Journal of Marketing Research and Journal of Consumer Research . Advising/Grants: No specific advisees or grants listed. His research has been published in top-tier journals including Marketing Science , Journal of Consumer Research , and Management Science .

Daniel Büscherمشاهده پروفایل

پژوهشگر

• Autonomous Robot Navigation
• Deep Learning
• Computer Vision
• +۱ مورد دیگر

Daniel Büscher is a post-doctoral researcher at the Robot Learning Lab of the University of Freiburg. His work focuses on autonomous robot navigation , deep learning , and probabilistic state estimation . He has contributed to robotics research through projects involving mobile manipulation and audio-visual navigation . Research Interests : Autonomous navigation, deep learning, computer vision, probabilistic estimation Teaching : Lecturer and tutor for Introduction to Mobile Robotics and Robot Mapping (2017–2023) Publication Trends : Recent work emphasizes robot design optimization , language-grounded scene graphs , and uncertainty-aware perception . His research connects reinforcement learning with mobile manipulation and cross-domain navigation challenges.