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Jan SchlüterView profile

Assistant Professor

• Convolutional Neural Networks
• Deep Learning
• Acoustic Sequence Labeling
• +4 more

Jan Schlüter is an Assistant Professor at the Institute of Computational Perception, Johannes Kepler University Linz. His research focuses on deep learning, acoustic sequence labeling, and audio signal processing. He has held academic positions including University Assistant at JKU, postdoctoral researcher at the University of Toulon, and research roles at the Austrian Research Institute for Artificial Intelligence (OFAI). Schlüter holds a PhD in Informatics from JKU and advanced degrees from TU Munich and the University of Hamburg. Education: PhD in Informatics, Johannes Kepler University Linz (2017) Master of Science in Informatics, Technical University Munich (2011) Bachelor of Science in Informatics, University of Hamburg (2008) Schlüter's research interests include convolutional neural networks, weakly-labeled learning, and differentiable time-frequency representations. He contributes to projects involving birdcall monitoring, music structure analysis, and environmental acoustics. Teaching responsibilities include courses on audio processing, machine learning, and artificial intelligence. His work bridges theoretical advancements in deep learning with practical applications in audio analysis, environmental monitoring, and music informatics. Notable contributions include the madmom Python library for audio and music processing.

• Machine Learning
• Music Information Retrieval
• Artificial Intelligence
• +3 more

Maarten Grachten is a researcher in Machine Learning and Music Technology, currently serving as an independent consultant and visiting researcher at the Artificial Intelligence Research Institute (IIIA) in Bellaterra, Spain. Previously, he held senior researcher positions at institutions such as the Department of Computational Perception at Johannes Kepler University (Linz, Austria) and the Austrian Research Institute for Artificial Intelligence (Vienna). His work focuses on computational models of music performance, expressive dynamics, and music information retrieval. Education: PhD in Computer Science and Digital Communication from Pompeu Fabra University (2007) M.Sc./B.Sc. in Artificial Intelligence from University of Groningen (2001) Propaedeutic year in Philosophy, University of Groningen (1996) Research Interests: His research spans machine learning applications in music, expressive performance modeling, and computational creativity. Key areas include: Developing algorithms for music generation and analysis Modeling human-like musical expression through AI Applications of deep learning in audio processing Awards: Recipient of several accolades including ISMIR's Outstanding Reviewer Award (2014), Best Reviewer Award (2013), and the Rencon Awards for performance rendering systems. Notably, his work on 'YQX plays Chopin' was featured in AI Magazine. Grants and Collaborations: Involved in EU projects like Lrn2Cre8 (FET 2013–2016) and PHENICX (ICT 2013–2016), focusing on interactive music experiences and creative technologies. Labs/Teams: Core contributor to projects like the Accompanion system for expressive accompaniment and co-developer of the partitura Python package for symbolic music data handling.

• Machine Learning
• Deep Learning
• Audio Signal Processing
• +3 more

Khaled Koutini is a Post-Doctoral Researcher at the Institute of Computational Perception, Johannes Kepler University Linz. His research focuses on developing general-purpose audio representations using deep neural networks, addressing challenges in audio classification, tagging, and machine listening. He investigates inductive biases in neural architectures and training processes to enhance generalization on small datasets, as well as transfer learning from large-scale models to specialized tasks. Affiliation: Institute of Computational Perception, JKU Linz Supervisor of PhD project: Gerhard Widmer PhD project period: January 2020 – Ongoing His work spans applications in content-based multimedia retrieval, context-aware devices, and environmental monitoring systems. The research emphasizes overcoming data scarcity issues in machine audio recognition.

• Machine Learning
• Signal Processing
• Remote Sensing
• +4 more

Wei Xue is an active academic researcher affiliated with Tsinghua University's Department of Computer Science and Technology. Their work spans multiple disciplines, including Machine Learning , Signal Processing , Medical Imaging , and Natural Language Processing . Key research areas include adversarial robustness in DNNs for remote sensing, hierarchical speaker representation learning for speech extraction, and multi-view fuzzy classification for brain network analysis. Recent publications focus on imbalanced data classification, spectrum sensing in cognitive radio, and innovative applications in soft robotics and medical diagnostics. Collaborations include institutions like Tsinghua University, Jiangnan University, and Hong Kong Baptist University, with frequent contributions to journals such as IEEE Transactions on Geoscience and Remote Sensing and Neural Networks .

• Music Information Retrieval
• Machine Learning
• Evaluation of Computer Experiments
• +4 more

Arthur Flexer is a Post-Doctoral Researcher at the Institute of Computational Perception, Johannes Kepler University Linz (JKU). His work bridges music informatics, machine learning, and computational humanities. He leads projects like 'Pop Music Culture' and 'Spirits in Complexity' exploring AI in cultural contexts. Flexer holds a PhD and has advised multiple students, including Katharina Hoedt (PhD) and Selene Ximena Andrade Lopez (Master's). Research focuses on music similarity, adversarial attacks in recommendation systems, and ethical AI applications in art curation. He has organized major conferences like ISMIR 2019 and serves on editorial boards for Transactions of the International Society for Music Information Retrieval. His collaborations span museums like the Belvedere and institutions in Stockholm, Vienna, and Barcelona. Key projects include 'Dust and Data' (AI-driven museum curation), protein sequence analysis with the University of Vienna, and EU-funded initiatives on music perception. Flexer's work emphasizes validity in experiments, hubness reduction in high-dimensional data, and interdisciplinary approaches to music technology.

Myounghoon JeonView profile

Professor

• Human-Computer Interaction
• Human-Robot Interaction
• Sound and Music Computing
• +4 more

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
• +4 more

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 RiazView profile

Research Fellow

• Immersive Audio
• Psychoacoustic Perception
• Spatial Audio
• +6 more

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
• +7 more

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 DakshitView profile

Assistant Professor

• 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 HolnessView profile

Associate Professor

• Machine Learning
• Robotics
• Ensemble Methods
• +4 more

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
• +4 more

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 AdanovaView profile

Assistant Professor

• Image Processing
• Computer Vision
• Symmetry Analysis
• +2 more

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
• +3 more

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
• +3 more

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 .