Find Academic Supervisors

Alessandra GalliView profile

Researcher

• Biomedical Signal Processing
• Compression
• Anomaly Detection
• +5 more

Alessandra Galli is a postdoctoral researcher at the Eindhoven University of Technology, affiliated with the Signal Processing Systems group within the Electrical Engineering Department and the Biomedical Diagnostics Lab. Her work focuses on biomedical signal processing, compression, anomaly detection, and machine learning, particularly for cardiac non-invasive and long-term monitoring. Academic Background: B.Sc. and M.Sc. in Biomedical Engineering from the University of Padova (2015, 2017), Ph.D. in Information and Communication Technology from the University of Padova (2021). Research Objective: Developing innovative tools for early diagnosis and prediction of pregnancy complications by studying maternal-fetal cardiovascular coupling. Projects: Pregnancy monitoring research emphasizing uterine/placental mediation in cardiovascular interactions. Scientific Awards: Marie Skłodowska-Curie Postdoctoral Fellowship Labs & Collaborations: Member of the Biomedical Diagnostics Lab at TU/e; collaborated with the Instrumentation and Measurements Group at the University of Padova.

Edmund CampionView profile

Professor

• Music Composition
• Electronic Music
• Audio Technology
• +4 more

Edmund Campion is a Professor of Music Composition at the University of California, Berkeley , where he co-directs the Center for New Music and Audio Technologies (CNMAT) within the Department of Music. His career spans over 25 years as a composer, performer, software developer, and collaborator. Current affiliation: UC Berkeley (College of Letters and Science, Department of Music) Academic role: Professor Leadership: Co-Director at CNMAT Edmund Campion's research and composition focus on: Electronic music integration with traditional ensembles Multimedia performance art Interactive audiovisual experiences Algorithmic composition techniques Emergent sound technologies Cultural fusion through historical instruments His work spans both academic and artistic domains, with notable projects involving: Kronos Quartet collaborations Pacific Rim Festival performances Philharmonie de Paris premieres Development of spherical speaker array applications Scientific awards and recognition: Recipient of Guggenheim Fellowship (2016) Bearinger Award winner American Rome Prize awardee Lili Boulanger Prize recipient Paul Fromm Award at Tanglewood Goddard Lieberson Fellowship As an advisor, Campion has collaborated with: Audiovisual artist Kurt Hentschläger Korean Traditional Contemporary Orchestra San Francisco Contemporary Music Players He has received commissions from: Les Percussion des Strasbourg (2011) Société Générale (2011) Serge Koussevitzky Foundation (2012) ECO Ensemble (2014) French Ministry of Culture (2011) NEUMA Records (2023) Campion contributes to technological innovation through: Development of CNMAT Spherical Loudspeaker Array Integration of digital processing in live performances Exploration of algorithmic composition tools

Jinsheng ZhangView profile

Professor

• Auditory Neuroscience
• Tinnitus Mechanisms
• Neural Implants
• +5 more

Jinsheng Zhang is a full-time Professor and Chair at Wayne State University, holding joint appointments across the School of Medicine, College of Liberal Arts and Sciences, and College of Engineering. He serves as Director of the Laboratory of Tinnitus and Auditory Neuroscience Research. PhD in Auditory Neurophysiology (University of Fribourg, Switzerland, 1997) Postdoctoral training and advanced coursework in electronics at Wayne State University His research focuses on tinnitus mechanisms , auditory neural implants , and brain neuromodulation . Key methodologies include multichannel electrophysiology , MRI imaging , and clinical translation . Recent publications highlight neuroinflammation in tinnitus , flexible neural probes , and cross-modal plasticity . He has secured grants from NIH , DoD , and NSF , and serves on editorial boards of JMIHI and Journal of American Audiology . Zhang has mentored over 50 trainees, including MD residents, PhD candidates, and postdoctoral fellows, with many achieving academic or clinical leadership roles.

Alistair MacDonaldView profile

Professor

• Electroacoustic Composition
• Live Electronics
• Field Recording
• +3 more

Alistair MacDonald serves as Professor and Director of the Electroacoustic Studios at the Royal Conservatoire of Scotland (RCS), where he also teaches composition. Appointed Fellow of RCS in 2012 and promoted to Professor in 2017, he actively accepts PhD students and maintains current research output through 2025. His institutional roles center on electroacoustic music education and studio leadership. MacDonald's research explores field recording, live electronics, interactivity, and improvisation through international collaborations with performers, artists, and choreographers. Key projects include AD 1136 (2023) for voice/electronics at Glasgow Cathedral, film collaborations like The moss and the cosmos (2021), dance works with Reckless Sleepers (2018-22), and cross-disciplinary pieces such as Le Sirenuse for glass percussion/electronics. His practice spans performance, broadcast, and installation media with emphasis on spatialized sound design. His 56 research outputs (1998-2025) demonstrate consistent electroacoustic innovation, particularly in vocal processing (2023-2025), collaborative film scoring (2021-2022), and interdisciplinary installations (2014-2015). Recent works integrate historical contexts like Hildegard von Bingen's writings with contemporary electronics, while ongoing METRIC network activities show European collaboration in music technology education. Creative Scotland Award (2002) for Sensuous Geographies installation Multiple festival commissions including Made in Scotland showcase (2017) Selected for Royal Scottish Academy Open Exhibition (2015) Coburg Prize for Contemporary Glass finalist (2014) MacDonald leads the Electroacoustic Studios at RCS while maintaining external examiner roles at Newcastle University (2020-2026). His collaborative network spans UK, Germany, Belgium, Norway, and Australia with commissions from Scottish Ensemble, BBC Radio Scotland, and Theatre Cryptic. Current projects include METRIC intensive workshops in Helsinki (2025) and Antwerp (2024), focusing on international electroacoustic pedagogy development. His studio practice involves custom computer-based sound processing environments for spatialized music creation, documented through releases on empreintes DIGITALes. Ongoing collaborations include vocalist Stephanie Lamprea and harpist Catriona McKay, with recent recordings like OTSEGO (2023) and Postcards (2023) available on Bandcamp.

Reinhold Häb-UmbachView profile

Professor

• Statistical Signal Processing
• Machine Learning
• Speech Processing
• +7 more

Reinhold Häb-Umbach is a distinguished Professor in the Communications Engineering Department at the University of Paderborn, leading the Heinz Nixdorf Institute's Speech and Communications group. His work spans statistical signal processing and machine learning for speech and audio applications , with a focus on robustness and real-world deployment. His research integrates probabilistic models with deep learning , addressing challenges in speech separation , noise suppression , and acoustic distance estimation . Recent projects include WestAI , SAIL , and TRR 318 , emphasizing AI-driven solutions for sociotechnical systems. 2020: IEEE Fellow 2015: ISCA Fellow 2021-2023: ISCA Distinguished Lecturer Over 300 peer-reviewed publications He has received multiple best student paper awards at venues like IWAENC , Interspeech , and ASRU , and has contributed to 15 competitive DFG grants . His leadership roles include Technical Program Committee Chair for Interspeech 2024 and editorial roles in IEEE journals.

Qing QuView profile

Assistant Professor

• Signal Processing
• Data Science
• Machine Learning
• +4 more

Qing Qu is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan – Ann Arbor, with affiliations to the Michigan Institute for Data Science (MIDAS), the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM), and the Michigan Institute for Computational Discovery and Engineering (MICDE). He earned a B.E. from Tsinghua University (2011), a M.Sc. from Johns Hopkins University (2012), and a Ph.D. from Columbia University (2018) under the supervision of John Wright. From 2018–2020, he was a Moore-Sloan Fellow at NYU Center for Data Science. Research Focus: At the intersection of signal processing, data science, machine learning, and numerical optimization, with emphasis on deep representation learning and diffusion models. Awards: Microsoft Ph.D. Fellowship (2016), NSF Career Award (2022), Amazon AWS AI Award (2023), UM CHS Junior Faculty Award (2025), Google Research Scholar Award (2025), and Best Paper at NeurIPS’23 Workshop on Diffusion Models. Publications: Recent work explores generalization in diffusion models via Probability Flow Distance , low-dimensional subspace clustering, controllable image editing, and watermarking techniques. His research also investigates neural collapse theory and optimization landscapes in deep learning. Collaborations: Co-organizer of ICCV’25 and ICML’25 tutorials with experts like Yi Ma, Yuxin Chen, and Liyue Shen. Former students include Peng Wang, Yifu Lu, Jinfan Zhou, and Siyi Chen, who have joined institutions like Princeton, UChicago, Duke, and University of Macau. Grants: Funded by NSF, ONR, ARO, Amazon Research, KLA Corporation, MICDE, and MIDAS.

• Predictive Analytics
• Recommender Systems
• AI Applications
• +3 more

Sung-Hyuk Park is an Assistant Professor at KAIST College of Business, focusing on predictive analytics, recommender systems, and AI applications. His work spans marketing technology, healthcare informatics, and computer vision, with recent publications in journals like IEEE Transactions and Biomedical Signal Processing . Current research integrates deep learning, social network analysis, and optimization models across domains.

Nikolaos NikolaidisView profile

Professor

• Computer Vision
• Machine Learning
• Digital Image Processing
• +7 more

Nikolaos Nikolaidis is a Professor at the Department of Informatics, School of Sciences, Aristotle University of Thessaloniki. His academic career spans multiple roles including Associate Professor (2016-2024), Assistant Professor (2008-2016), and Lecturer (2002-2007) at the same institution. Education: Diploma in Electrical and Computer Engineering (1991), Faculty of Engineering, Aristotle University of Thessaloniki. PhD in Electrical and Computer Engineering (1997), Faculty of Engineering, Aristotle University of Thessaloniki. Thesis: «Digital Processing of Multichannel Images and Video». Research Interests: Computer/robot vision, including synthetic and mixed data generation for AI training. Human-centric image/video analysis: facial expression recognition, activity tracking, clustering in 3D data. Applications of computational intelligence in computer graphics and gaming technologies. Multi-view/sparse data analysis for identity detection and label propagation. Scientific Contributions: Co-authored 1 book, 16 book chapters, 73 journal papers, and 208 conference papers. Over 7570 citations (h-index 39) as per Google Scholar. Participated in 29 EU/nationally funded research projects. Organized 2 workshops (EUSIPCO 2019, IROS 2021) and 6 special conference sessions. Professional Roles: Associate/Area Editor: Signal Processing: Image Communication, EURASIP Journal on Image and Video Processing. Former Associate Editor: IET Image Processing. Conference Chair roles: Technical Program Chair (IEEE IVMSP 2013), Publicity Co-Chair (EUSIPCO 2015, IEEE ICIP 2018).

• Stem Cell Biology
• Developmental Biology
• Molecular Biology
• +6 more

Florian Raible is an Associate Professor at the Department of Neuroscience and Developmental Biology , University of Vienna. His research spans stem cell biology, chronobiology, and marine organism molecular biology, focusing on annelid models like Platynereis dumerilii to explore evolutionary developmental mechanisms, circadian/circalunidian clocks, and photoreceptor evolution. Research Focus: Stem cell lineage restrictions, light-regulated hormonal systems, and molecular rhythmicity in marine annelids Key Methodologies: SHInE tissue labeling, HCR hybridization, metabolomics platforms Recent Trends: 2024 articles emphasize annelid stem cell profiling, moonlight sensing cryptochromes, and fatty acid elongation pathways Scientific Contributions: • Pioneering transgenesis in Platynereis • Establishing Platynereis as a model for circalunar clock research • Discovering methylfarnesoate as an annelid brain hormone Contact: Email: florian.raible@univie.ac.at Phone: +43-1-4277-54616

Claudia CecchettoView profile

Assistant Professor

• Neuroscience
• Neuroengineering
• Biomedical Engineering
• +3 more

Dr. Claudia Cecchetto is an Assistant Professor (Junior Researcher, RTDA) at the Department of Biomedical Sciences, University of Padua, where she conducts research in the Neurochip Lab led by Prof. Stefano Vassanelli. Her work focuses on developing neurotechnological tools to study sensory processing in the rodent barrel cortex. Education: PhD in Biomedical Engineering, University of Padua (2013-2016) MSc in Physics, University of Padua (2010-2012) BSc in Physics, University of Padua (2007-2010) Research Interests: Dr. Cecchetto specializes in in vivo electrophysiology, two-photon microscopy, and implantable neuroprobes. Her current projects investigate neuronal network dynamics in the somatosensory cortex using combined imaging and electrophysiological techniques, supported by machine learning analysis. Her research spans neuroengineering, biophysics, and computational neuroscience. Publications Focus: Her recent articles emphasize neurotechnology development (e.g., CMOS probes, LFP analysis algorithms) and sensory processing studies in rodent models, frequently incorporating advanced imaging and electrophysiological methods. Awards: Marie Skłodowska-Curie Actions Fellowship (PNRR-MSCA project NEU-PAGES) Projects & Grants: She manages multiple EU-funded initiatives including SYNCH (#824162), NEUREKA (#863245), and leads the NEU-PAGES project combining two-photon imaging with neuroprobe technology. Previously, she completed the MSCA project GRACE (#796177) at Okinawa Institute of Science and Technology. Affiliations: She collaborates with the Optical Neuroimaging Unit (OIST, Japan) and maintains active profiles on ResearchGate and LinkedIn.

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.

• Ocean Acoustics
• Signal Processing
• Underwater Communications
• +2 more

Zoi-Heleni Michalopoulou is Professor in Mathematical Sciences at New Jersey Institute of Technology, specializing in ocean acoustics and signal processing. Her research develops advanced algorithms for underwater signal detection, geoacoustic inversion, and ocean parameter estimation. Current projects include Navy-sponsored investigations into shallow water inversion using machine learning and optimization methods. Research integrates Gaussian processes, genetic algorithms, and Bayesian methods for solving inverse problems in uncertain ocean environments. Recent publications focus on multichannel signal detection in distorted underwater channels, direction-of-arrival estimation, and particle filter optimization. Maintains active collaborations with Navy research divisions. Publications demonstrate consistent methodological innovation in statistical signal processing, with applications to underwater sensor networks and acoustic characterization. Currently leads NSF-funded nanotechnology education initiatives.

Romit Roy ChoudhuryView profile

Professor

• Machine Learning
• Signal Processing
• Generative Models
• +6 more

Romit Roy Choudhury is the Gilmore Family Endowed Professor of Electrical and Computer Engineering (ECE) at the University of Illinois at Urbana-Champaign (UIUC) and an Amazon Scholar since 2022. He earned his Ph.D. in Computer Science from UIUC in 2006 and previously held positions at Duke University, including Associate Professor (2011–2013) and Assistant Professor (2006–2011). His research spans generative models, signal processing, and wearable computing, with a focus on audio denoising, source separation, and spatial inference. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign, 2006. Research Interests: Generative Models, Machine Learning, Sensing, Inverse Problems, Wearable Devices, Localization, and Acoustic Sensing. His lab, Signals and Inference Research Group (SiNRG), explores innovations in audio and sensor-based systems. Publications: Over 50+ peer-reviewed articles, including impactful work on inaudible sound attacks (MobiSys 2017 Best Paper), noise cancellation via IoT (SIGCOMM 2018), and earable computing (MobiCom 2020). Awards: ACM Sigmobile Rockstar Award (2015), IEEE Fellow (2019), NSF CAREER Award (2007), and multiple best-paper recognitions. Teaching: Courses include ECE 438 (Communication Networks), ECE 434 (Real-World Algorithms for IoT), and ECE 101 (Intro to Computing). Labs/Teams: SiNRG at UIUC, collaborations with Amazon, Samsung AI, Intel, and Microsoft Research.

Nilesh MadhuView profile

Professor

• Machine Learning
• Audio & Speech Enhancement
• Automated Audio Scene Analysis
• +4 more

Nilesh Madhu is a Professor for Audio and Speech Processing at Ghent University and imec, Belgium, where he leads research in the Internet Technology and Data Science Lab (IDLab). His academic journey began with a Dr.-Ing. degree (summa cum laude) from Ruhr-Universität Bochum, Germany in 2009, followed by a Marie-Curie postdoctoral fellowship at KU Leuven. Prior to his academic appointment, he served as Principal Scientist and team lead at NXP Semiconductors from 2011-2017, developing advanced audio algorithms for mobile devices. His research spans multiple domains within signal processing, with a particular focus on machine learning applications for audio and speech enhancement, automated audio scene analysis, and hearing prostheses. Professor Madhu's work bridges theoretical signal processing with practical implementations in communications, healthcare, and assistive technologies. Analysis of his recent publication record reveals a strong emphasis on deep learning approaches for speech enhancement, with particular attention to phase reconstruction, multichannel processing, and efficient model architectures. His research increasingly incorporates biomedical applications, particularly in cardiovascular monitoring using advanced sensing techniques like laser Doppler vibrometry. His laboratory work centers around the Internet Technology and Data Science Lab (IDLab) at Ghent University, where his team develops innovative signal processing algorithms with applications spanning mobile communications, hearing aids, and biomedical monitoring systems. The group maintains strong industry connections, evidenced by their focus on practical implementation constraints and real-world performance metrics.

• Machine Learning
• Biomedical Engineering
• Signal Processing
• +5 more

Amir Nassibi is a Postdoctoral Research Associate at Imperial College London's Communication and Signal Processing Research Group (CSP), Department of Electrical and Electronic Engineering. He holds a PhD from Imperial College London and an MSc in Advanced Digital Systems from the University of Hertfordshire. His research focuses on machine learning applications in healthcare, wearable sensor systems, and signal processing for biomedical diagnostics. He is affiliated with the UK Dementia Research Institute (UKDRI) and collaborates on multidisciplinary projects. Education: PhD in Electrical and Electronic Engineering, Imperial College London (UK) MSc in Advanced Digital Systems, University of Hertfordshire (UK) Research interests include EEG-based depression diagnosis, wearable multimodal sensing platforms like BioBoard, and edge computing for AI deployment in healthcare. His work addresses challenges in signal enhancement, algorithm optimization for embedded systems, and reducing bias in medical device outputs. He explores topics such as memristor modeling for integrated circuits and in-ear ECG/PPG fusion for vital signs monitoring. Awards: None explicitly stated. Advising & Grants: While specific grants are not detailed, his role as a postdoctoral researcher suggests involvement in funded projects. He collaborates on lab initiatives within the CSP group and UKDRI. Labs/Teams: Active contributor to the Communication and Signal Processing Group at Imperial College London and the UK Dementia Research Institute's interdisciplinary teams.