جست‌وجوی استاد و پژوهشگر

Myounghoon Jeonمشاهده پروفایل

استاد

• Human-Computer Interaction
• Human-Robot Interaction
• Sound and Music Computing
• +۴ مورد دیگر

Myounghoon "Philart" Jeon is a Professor in the Department of Industrial and Systems Engineering at Virginia Tech. He leads the Mind Music Machine (tri-M) Lab, focusing on interactive technologies for artistic and creative experiences. His research spans Human-Computer Interaction, Human-Robot Interaction, and Affective Computing, with applications in automotive interfaces, assistive technologies, and extended reality (XR). Education: PhD in Engineering Psychology and Human-Computer Interaction from Georgia Tech, M.S. in Engineering Psychology (Georgia Tech), and B.S. in Psychology from Yonsei University. Research emphasizes interdisciplinary approaches combining psychology, neuroergonomics, and computational modeling. Key areas include sonification, emotion-aware design, and inclusive technologies. He has authored numerous publications in top journals like International Journal of Human-Computer Interaction and IEEE Transactions on Visualization and Computer Graphics. Lab Focus: tri-M Lab explores AI-driven art, automotive UX, assistive robotics, and XR applications. Grants & Awards: Includes Earl Alluisi Early Career Award and multiple best paper awards. Active in diversity initiatives through HFES COAG and BIPOC Affinity Group. Editorial Roles: Associate Editor for International Journal of Human-Computer Studies and Presence: VR & Teleop. His work bridges technical innovation with human-centric values, addressing challenges in automated vehicles, creative collaboration, and ethical AI/robotics education.

• Obstructive Sleep Apnea
• Neural Networks
• Multimodal Data Fusion
• +۴ مورد دیگر

Jiali Xie is a University Researcher at Eindhoven University of Technology, affiliated with the Electrical Engineering school and Signal Processing Systems department. They work within the Biomedical Diagnostics Lab and Eindhoven MedTech Innovation Center, focusing on medical signal processing applications. Research spans obstructive sleep apnea diagnostics using multimodal signals (ECG, audio, respiratory effort) and medical deep learning systems. Key approaches include multi-task neural networks, convolutional architectures, and sensor fusion techniques for non-invasive monitoring. Recent work demonstrates strong applications in sleep staging, severity assessment, and real-world clinical implementation. Their publication trends show concentrated focus on sleep-disordered breathing (87.5% of recent articles) with emerging contributions to remote sensing (25% of 2023 output). Methodologically, neural networks dominate (100% of medical publications) with emphasis on multi-modal data integration (66%) and frequency-scale analysis (44%). Active in two major projects: MEDUSA (Medical Data Utilization Solutions Accelerator, 2020-2026) developing multimodal signal analysis for sleep apnea, and an earlier multimodal signal analysis project (2016-2021) focused on unobtrusive sleep apnea characterization. Media coverage includes expert commentary on building reconstruction methods from Guilin University of Technology.

Maham Riazمشاهده پروفایل

پژوهشگر ارشد

• Immersive Audio
• Psychoacoustic Perception
• Spatial Audio
• +۶ مورد دیگر

Maham Riaz is a Doctoral Research Fellow at the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion at the University of Oslo, affiliated with the Faculty of Humanities. Her research focuses on audiovisual rhythms, motion capture, spatial audio, and psychoacoustic perception. She holds an M.Mus in Music Technology from New York University (NYU) and a B.Sc in Accounting & Finance from Lahore University of Management Sciences (LUMS). Her academic interests span immersive audio, gestural music, microphone techniques, and the intersection of music with digital animation and audiovisual media. She has received prestigious awards such as the Fulbright Scholarship and the NYU Music Tech Academic Achievement Award. Riaz’s work bridges music technology and interdisciplinary research, particularly through projects like the AMBIENT initiative. She collaborates on experimental audio setups, spatial audio recording, and interactive systems. Her contributions include advancements in soundfield microphone techniques and the analysis of human micromotion using inertial sensors. Key Projects: AMBIENT (Bodily Entrainment to Audiovisual Rhythms), Bodies in Concert Labs: RITMO Centre, fourMs Lab

• Electrical Engineering
• Computer Engineering
• Signal Processing
• +۷ مورد دیگر

Ming Zhu is an Assistant Professor in Residence in the Department of Electrical and Computer Engineering at the University of Nevada, Las Vegas (UNLV). His work bridges electrical engineering and computer science to solve real-world problems in transportation, industrial systems, and healthcare. His research interests include Signal Processing , Machine Learning , Unmanned Aerial Vehicles (UAVs) , and Edge Computing . He applies these to Rail Infrastructure Monitoring , Industrial Fault Detection , and Health Monitoring Systems , using technologies like LiDAR, acoustic sensors, and generative models. Analysis of recent publications reveals a focus on non-invasive rail track measurement (using UAVs and LiDAR), sound-based malfunction detection in machinery, and efficient edge computing for video streaming and healthcare applications. His work demonstrates a strong trend towards practical, real-time systems for critical infrastructure.

Sagnik Dakshitمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Healthcare Informatics

Sagnik Dakshit is an Assistant Professor in the Computer Science Department at the University of Texas at Tyler. He holds a Ph.D. in Computer Science from the University of Texas at Dallas and a B.Tech in Computer Science and Engineering from West Bengal University of Technology (India). His research focuses on Machine Learning, Artificial Intelligence, and Healthcare Informatics, with a particular emphasis on Explainable AI systems and their applications in improving quality of life. Dr. Dakshit’s work has been supported by collaborations with industry partners such as Robert Bosch, Seagate Technologies, HP Machine Learning Labs, and Nokia Bell Labs, as well as prior experience in software development at IBM and Tata Technologies. His teaching philosophy emphasizes hands-on project-based learning in Machine Learning, AI, and Healthcare Informatics. His research trends span healthcare time series analysis, ECG classification frameworks, and AI ethics in medical applications. Notable contributions include work on explainable CNN models for ECG signals, VR-based attention measurement systems, and bias analysis in healthcare decision support systems. Dr. Dakshit has been actively involved in academic-industrial projects, including the SSBC 2020 sclera segmentation benchmarking competition. His publications reflect a blend of theoretical advancements and practical clinical applications.

Gary Holnessمشاهده پروفایل

دانشیار

• Machine Learning
• Robotics
• Ensemble Methods
• +۴ مورد دیگر

Gary Holness is an Associate Professor of Computer Science at Clark University, where he directs the Laboratory for Intelligent Perceptual Systems (LIPS). He holds a PhD in Computer Science from the University of Massachusetts Amherst (2008) focusing on machine learning ensembles, robotics, and distributed systems. Research spans: Machine learning ensembles and error diversity Robotic perception and autonomous systems Nonparametric density estimation Human-robot interaction for autism therapy Distributed frameworks for medical alerting Interactive AI with manifold learning His publications show progression from theoretical machine learning to applied systems, with recent work on spectroscopic analysis and kernel methods. Maintains active student research programs in robotics, GPU acceleration, and terrain classification.

• Dependability and Security of Digital Systems
• Machine Learning and AI Algorithms
• Accelerator Design for AI
• +۴ مورد دیگر

Ioannis Voyiatzis is a Professor and Head of the Department of Informatics and Computer Engineering at the University of West Attica, Greece. He directs the Computing and Embedded Systems Technology, Internet of Things and Optimization (CESIO) lab. His research focuses on dependability/security of digital systems, AI/ML algorithms, UAVs, and blockchain. He has authored over 120 publications and participated in EU/Government-funded projects. His educational contributions include serious games for informatics and blockchain-based smart city frameworks. Despite no listed awards, his work spans cybersecurity tools, UAV communication systems, and adaptive learning systems. Education: BSc/MSc from National Kapodistrian University of Athens, PhD in Computing. Research interests bridge hardware-software systems (e.g., FPGA architectures for UAV security) and educational technology (e.g., AR/VR in nursing training). Recent publications emphasize drone hardware selection, aggression prediction in dementia patients, and AI-driven personalized education. Active in EU projects, his lab develops IoT seismic monitoring systems and photovoltaic fault detection algorithms.

Venera Adanovaمشاهده پروفایل

استادیار

• Image Processing
• Computer Vision
• Symmetry Analysis
• +۲ مورد دیگر

Venera Adanova is an Assistant Professor and Vice Head of the Computer Engineering Department at TED University. She holds a Ph.D. (2015) and M.S. (2008) in Computer Engineering from Middle East Technical University, and a B.S. from Kyrgyz-Turkish Manas University (2005). Her research focuses on image processing, computer vision, and computational symmetry analysis. Research explores symmetry detection in visual patterns, tiling classification, and computational analysis of ornaments. Recent publications address GPT-generated text analysis and stuttering detection using neural networks. Dr. Adanova completed postdoctoral research at Middle East Technical University before joining TED University in 2018. Her work combines geometric computation with pattern recognition for applications in cultural heritage and AI systems.

• Machine Learning
• Deep Learning
• Audio Signal Processing
• +۳ مورد دیگر

Khaled Koutini is a Post-Doctoral Researcher at the Institute of Computational Perception, Johannes Kepler University Linz. His research focuses on learning general-purpose audio representations using deep neural networks, addressing challenges like data scarcity and improving generalization through inductive biases in architectures and training processes. He explores transfer learning applications in audio classification/tagging for multimedia, smart devices, and monitoring systems. His current PhD project (ongoing since 2020), supervised by Gerhard Widmer, investigates enhancing audio recognition via inductive biases and representation extraction from large-scale models. Affiliated with the Institute of Computational Perception, his work bridges theoretical advancements in neural networks with practical applications in machine listening.

• Mechanical Engineering
• Acoustics
• Vibration Analysis
• +۳ مورد دیگر

Abhishek Ray Mohapatra is a Research Fellow at the University of Technology Sydney (UTS), affiliated with the Faculty of Engineering and Information Technology and the Centre for Audio Acoustics and Vibration. His research focuses on signal processing in acoustics and vibration, with recent work involving time-series analysis of wild bee signals. He holds a postdoctoral position and collaborates actively in the field of bioacoustics. Education details are not explicitly provided in the text, but his disciplinary expertise aligns with mechanical engineering and acoustics. His sole listed publication to date, from 2024, explores classification methods for biological sound signals. No academic awards or grants are mentioned. He is reachable at Abhishek.RayMohapatra@uts.edu.au .

• Machine Learning
• AI Integration
• Data Visualization
• +۳ مورد دیگر

Yuanzhe Jin is a DPhil Candidate at the Oxford e-Research Centre, University of Oxford. Their research focuses on interdisciplinary areas including AI integration in systems, machine learning applications, and data visualization. They contribute to advancing technologies like adversarial attack mitigation, generative AI workflows, and smart IoT systems. Key research interests span machine learning robustness, AI-driven education tools, and robotics navigation algorithms. Their work bridges theoretical advancements with practical implementations in domains such as environmental monitoring (via aerial imagery), UAV obstacle avoidance, and music generation through neural networks. Yuanzhe has collaborated on projects like the 'Otaku' dormitory management system and developed novel algorithms for mobile robotics path planning. Their publications reflect a strong computational focus with applications in both technical and creative fields. Currently affiliated with the Oxford e-Research Centre, they are actively involved in cutting-edge research at the intersection of artificial intelligence and real-world systems.

Arshdeep Singhمشاهده پروفایل

پژوهشگر ارشد

• Audio Signal Processing
• Machine Learning
• Neural Networks
• +۳ مورد دیگر

Dr. Arshdeep Singh is a Research Fellow A at the Centre for Vision, Speech and Signal Processing (CVSSP) at the University of Surrey, specializing in AI-driven audio processing. He concurrently serves as a Sustainability Fellow at the University’s Institute for Sustainability and holds the UKAN+ Early Career Acoustic Champion title (2023). His research focuses on efficient neural network architectures for audio classification, model compression, and sustainable AI applications. Singh completed his PhD at IIT Mandi, India, with prior roles including internships at Intel Bangalore and CSIR-CSIO Chandigarh, and an M.E. from Panjab University (Gold Medal recipient). Research interests include audio signal processing, CNN compression via filter pruning, and sustainable AI. Notable contributions include developing lightweight CNN frameworks for acoustic scene classification and sound event detection, with applications in smart home technologies and industrial health monitoring. His work often targets resource-constrained devices, emphasizing real-world deployability. Publications highlight innovations like graph centrality-based filter pruning and quaternion transformations for CNN efficiency. Awards include the Panjab University Gold Medal for M.E. and recognition as an Early Career Acoustic Champion. Supervision includes MSc students (e.g., Soham Bhattacharya, Bars Szegedi) and undergraduates (Kristaps Redmers, Yan-Ping Liao). His interdisciplinary projects span audio captioning, embedded hardware deployment, and dataset creation for sound event detection in residential environments.

Mounya Elhilaliمشاهده پروفایل

استاد

• Computational Neuroscience
• Auditory Signal Processing
• Machine Learning
• +۷ مورد دیگر

Dr. Mounya Elhilali is a Professor at The Johns Hopkins University with primary appointment in the Department of Electrical and Computer Engineering and joint appointment in the Department of Psychology and Brain Sciences. She also holds affiliations with the Kavli Neuroscience Discovery Institute and Center for Hearing and Balance. Her research focuses on computational neuroscience, auditory scene analysis, and biomedical signal processing with applications in both neurophysiology and clinical diagnostics. Her work spans auditory attention modeling, statistical regularity extraction from dynamic sounds, and development of the D-REX computational framework. She has led advancements in lung auscultation technologies through projects like the programmable electronic stethoscope and noise suppression algorithms for low-resource settings. Dr. Elhilali's teaching portfolio includes courses on Audio Signal Processing Computational and Systems Neuroscience Speech and Audio Processing She has received prestigious awards including the NSF Early Career Award (2009), ONR Young Investigator Award (2012), and Johns Hopkins Catalyst Award (2017). Scientific contributions include Neural mechanisms of auditory scene segregation Machine learning approaches to speech processing Biomedical applications of sound analysis Temporal coherence theory in cortical sound representation Her publications demonstrate strong interdisciplinary impact across neuroscience, biomedical engineering, and machine learning domains.

Carey Bunksمشاهده پروفایل

استاد مهمان

• Music Informatics
• Computer Science
• Signal Processing
• +۳ مورد دیگر

Carey Bunks is a Visiting Professor at the School of Electronic Engineering and Computer Science, Queen Mary University of London. Their research focuses on music informatics and algorithm development, particularly in Cover Song Identification (CSI) funded by Apple. The project emphasizes robust algorithms for detecting musical improvisations and variations across song versions, with applications in copyright protection and music recommendation systems. Key research interests include harmonic analysis, jazz musicology, signal processing, and computational methods applied to music. Their work bridges computer science and music theory, addressing challenges in audio analysis and algorithmic musicology. Collaborations involve industry partnerships like Apple, leveraging interdisciplinary expertise in software engineering, numerical computing (e.g., Scilab), and signal processing. Earlier contributions span seismic analysis, reservoir monitoring, and cybersecurity methodologies.

• Machine Learning
• Natural Language Processing
• Artificial Intelligence
• +۳ مورد دیگر

Iacopo Ghinassi is a Visiting Professor in the School of Electronic Engineering and Computer Science at Queen Mary University of London. He specializes in machine learning and natural language processing (NLP), with a focus on text and audio segmentation, ethical AI, and language model applications. His teaching includes a postgraduate module on Machine Learning, covering pattern recognition, clustering, and neural networks. Research interests span computational linguistics, multimodal analysis, and social discourse ethics. Notable projects include MoralBERT (detecting moral values in social media and music lyrics) and audio/text segmentation techniques for podcasts and climate reports. He explores challenges in long-context language models and historical language processing via parallel corpora. Publications emphasize cross-domain neural encoder comparisons, ethical AI frameworks, and unsupervised learning approaches. His work bridges theoretical NLP advancements with practical applications in media analysis, education, and cultural heritage preservation.