Find Academic Supervisors

• Neuroeconomics
• Decision Neuroscience
• Epilepsy Research
• +2 more

Bernd Weber is Professor at the Medical Faculty of the University of Bonn and Steering Committee member of the Transdisciplinary Research Area (TRA) 'Life and Health'. A medical graduate of Bonn (2003), he co-founded the Center for Economics and Neuroscience, focusing on biological mechanisms of human decision-making. Research Leadership: Former Editor-in-Chief of the Journal for Neuroscience, Psychology and Economics and President of the Association of Neuropsychoeconomics. Research integrates neuroimaging, behavioral paradigms, and clinical neurology to decode value-based decision processes. Thematic Focus: Work spans epilepsy neuroimaging (ENIGMA consortium), gut-brain axis interactions, neuroeconomic modeling, and metabolic influences on cognition. Recent methodological innovations include low-radiation SPECT pipelines and normative brain modeling frameworks. Collaborative Networks: Contributes to multinational neuroimaging initiatives and the NeurotechEU alliance, advancing brain research infrastructure across Europe.

Filippo MolinariView profile

Professor

• Biomedical Signal Processing
• Medical Imaging
• Ultrasound Technology
• +4 more

Filippo Molinari is a Full Professor at the Politecnico di Torino, affiliated with the Department of Electronics and Telecommunications (DET). His research focuses on biomedical signal processing, medical imaging, and AI-driven diagnostic tools, particularly in neuroimaging, ultrasound technology, and oncology. Key areas include non-invasive vascular quantification, neurovascular assessment, and multi-modal imaging analysis for neurodegenerative disorders. Research areas: Ultrasound imaging, EEG signal decomposition, CT/PET analysis, and radiomics. Leading projects: REAP (photoacoustic tomography for cancer), InSiDe (cardiovascular disease monitoring), and AI-driven stain normalization in pathology. Teaching and thesis supervision at DET@POLITO, with a focus on biomedical engineering and signal processing. Recent work emphasizes AI explainability, uncertainty quantification in medical models, and augmented reality for surgical training. Over 50 publications since 2021, including studies on MRI-based brain disorder detection and deep learning for stroke classification. Ongoing collaborations involve multi-center studies on vascular imaging, digital pathology, and AI ethics in healthcare.

Massimo SalviView profile

Assistant Professor

• Biomedical Engineering
• Medical Imaging
• Artificial Intelligence
• +5 more

Massimo Salvi is an Assistant Professor at the Department of Electronics and Telecommunications of the Politecnico di Torino, Italy. His research focuses on biomedical signal and image processing, artificial intelligence, and deep learning, with a particular emphasis on developing hybrid techniques for complex biomedical problems. He leads projects in fluorescence microscopy analysis, dermatological imaging, and digital pathology. Education: Master’s in Biomedical Engineering (Politecnico di Torino, 2014), PhD in Biomedical Engineering (Politecnico di Torino, 2018). Research areas include synthetic image generation via GANs, AI-driven diagnostics, and stain normalization in histology. Key Projects: REAP (cancer cell analysis), DIPAT3-Care (digital pathology pathways), and automated solutions for microscopy imaging. Teaching activities include courses at DET@POLITO. Research Contributions: Over 150 publications in AI-driven medical imaging, including work on uncertainty quantification, augmented reality in surgery, and histological image synthesis. His work bridges clinical needs with computational innovations. Awards: No awards explicitly listed for Salvi in the provided text.

Eran MukamelView profile

Professor

• Computational Neuroscience
• Epigenomics
• Neural Data Analysis
• +3 more

Eran Mukamel is a Professor in the Department of Cognitive Science at the University of California San Diego (UCSD). His research focuses on computational analysis of neural data, electrophysiology of sleep and general anesthesia, and epigenomic dynamics in brain cells. He leads the Computational Neural DNA Dynamics (CNDD) Lab, affiliated with UCSD’s Halicioğlu Data Science Institute, the Kavli Institute for Brain and Mind, and the Neurosciences Graduate Program. His work integrates genomic, transcriptomic, and electrophysiological approaches to study brain function and disease. Research interests include neuronal epigenomics, single-cell methylome analysis, and systems neuroscience. He has pioneered methods for studying DNA methylation patterns in brain cells, linking epigenetic signatures to neuronal function and disease states. Recent projects involve investigating how environmental factors (e.g., maternal immune activation) impact brain development and epigenetic programming. Eran’s lab develops computational tools like SnapATAC for analyzing single-cell ATAC-seq data and collaborations span neurology, psychiatry, and computational biology. His contributions bridge molecular neuroscience with clinical applications, particularly in understanding neurodegenerative disorders like Alzheimer’s and ALS.

Hussein Al OsmanView profile

Associate Professor

• Affective Computing
• Human-Computer Interaction
• Serious Gaming
• +3 more

Hussein Al Osman is an Associate Professor and Associate Director at the School of Electrical Engineering and Computer Science, University of Ottawa. He holds B.A.Sc., M.A.Sc., and Ph.D. degrees from the same institution. His research focuses on affective computing, multimodal affect estimation, human-computer interaction, serious gaming, and multimedia systems, with recent emphasis on remote physiological signal measurement in healthcare contexts. He has authored over 50 peer-reviewed articles, secured 2 patents, and contributed to technology transfers in industry. Education: Bachelor of Applied Science (B.A.Sc.) Master of Applied Science (M.A.Sc.) Doctor of Philosophy (Ph.D.) Research Interests: Dr. Al Osman’s work spans affective computing, leveraging machine learning for emotion recognition, remote physiological monitoring, and mental health applications. He pioneers systems for suicidal ideation detection via social media, driver distraction analysis, and relapse prediction in psychiatric disorders. His group develops serious games for education and therapy, integrating haptic feedback and virtual reality. Notable contributions include multimodal fusion techniques and biofeedback systems for stress management. Publications: His articles highlight innovations in EEG-based emotion classification, stock market prediction through sentiment analysis, and anomaly detection in healthcare. Recent work explores synthetic data augmentation using large language models and graph neural networks for biomedical signal processing. Labs & Teams: He leads the Multimedia Processing and Interaction Group, advancing interdisciplinary projects at the intersection of AI, human-computer interaction, and healthcare technology.

Alain BuretteView profile

Assistant Professor

• Synaptic Structure and Function
• Neurodevelopmental Disorders
• Neurobiology of Angelman Syndrome
• +3 more

Alain Burette, PhD, is an Assistant Professor in the Department of Cell Biology and Physiology at the University of North Carolina School of Medicine. He holds a PhD from Blaise Pascal University (1997). His research focuses on the structural organization of synapses and their malfunction in neurological disorders like Angelman syndrome. Using advanced microscopy techniques (electron tomography, FIB-SEM, super-resolution imaging), he investigates protein localization and synaptic ultrastructure. His work spans 43+ publications since 1997, emphasizing neurodevelopmental mechanisms and synaptic dysfunction. Research Interests: Synaptic architecture, neurodevelopmental disorders, protein complex organization, and imaging technologies. Major projects include UBE3A subcellular localization in neurons and postsynaptic inhibition mechanisms. Collaborations with neuroscience labs (e.g., Philpot, Hell, and Sheng groups) highlight his interdisciplinary approach. Publications span synaptic calcium dynamics, glutamate receptor interactions, and Angelman syndrome pathophysiology. His most recent work (2017) explores UBE3A’s role in neuron function via subcellular organization insights.

Ronald M. AartsView profile

Professor

• Ambulatory Monitoring
• Signal Processing
• Medical Devices
• +9 more

Ronald M. Aarts is a Full Professor (part-time) at Eindhoven University of Technology's Department of Signal Processing Systems, Faculty of Electrical Engineering. He holds affiliations with the Eindhoven MedTech Innovation Center (EMTEC) and EAISI Foundational. His research focuses on ambulatory monitoring, sensor systems, and signal processing for medical applications such as sleep, cardiology, and epilepsy detection. Dr. Aarts has collaborated extensively with Philips Research since 1977, contributing to over 450 publications and 250+ patents. He received his BSc and PhD in Physics from Delft University of Technology. His academic career includes supervising 19 PhD students and co-developing technologies in audio engineering (AES Fellowship 1995), signal processing (IEEE Fellow 2007), and medical device innovation. Awards include the Philips Gold Invention Award (2012) and AES Silver Medal (2010). He advises GrAI Matter Labs and operates Aarts Consultancy, emphasizing the four P’s: People, Patents, Papers, and Products. Research interests span electromagnetism, acoustics, wearable sensors, and unobtrusive monitoring systems. Notable projects include ECG-based seizure detection, preterm infant sleep classification, and non-invasive circulatory volume assessment. His work aligns with UN Sustainable Development Goals in health and innovation. Affiliations: TU/e, Philips Research, EMTEC, EAISI Foundational Education: BSc (Electrical Engineering), PhD (Physics), Delft University of Technology Grants & Industry: Over 250 patents (Philips), Philips Fellow (2003), Philips Gold Invention Award (2012/2018) Labs/Teams: Signal Processing Systems group, collaborations with Radboud University and NXP Semiconductors

Hongbo XieView profile

Research Fellow

• Statistical/Bayesian Inference
• Machine Learning
• Deep Learning
• +3 more

Dr Hongbo Xie is a Vice-Chancellor’s Senior Research Fellow/Lecturer at QUT and an Associate Investigator at ACEMS. He holds a PhD in Biomedical Engineering from Shanghai Jiao Tong University. His research focuses on statistical/Bayesian inference, machine learning, biomedical signal/image processing, and matrix/tensor analysis for signal recovery. With over 70 publications (as of 2022), his work spans IEEE journals, conference proceedings, and book chapters. Education: PhD in Biomedical Engineering, Shanghai Jiao Tong University, China Research Interests: Statistical and Bayesian methodologies for complex data Machine learning applications in biomedical signal processing Image recovery via matrix and tensor analysis Advanced signal processing for healthcare and engineering Publications: His recent work includes EEG-based analysis of adolescent psychological distress, variational Bayesian models for movement control, and kernelized Bayesian matrix factorization. Articles frequently address biomedical signal/image processing, emphasizing robust algorithms for real-world applications. Affiliations: School of Mathematical Sciences (QUT), ACEMS.

• Bioinformatics and computational biology
• Plasma physics; fusion plasmas; electrical discharges
• Computational Neuroscience
• +3 more

Prof Damien Hicks is a Professor at Swinburne University of Technology's School of Science, Computing and Emerging Technologies, leading the Complex Systems group within the Optical Sciences Centre. He holds a PhD from MIT and prior roles include physicist at Lawrence Livermore National Laboratory (2000-2014) and Australian Research Council Future Fellow (2015-2019). His research spans computational neuroscience, plasma physics, bioinformatics, and optical systems, with notable contributions to EEG modeling, fusion energy, and cell lineage tracking tools like DeepKymoTracker. He teaches statistical physics and classical mechanics. Key research interests include: Statistical models in single-cell biology Neural population dynamics and EEG analysis Inertial fusion experiments achieving target gain >1 Optical neuromorphic computing (11 Tera-OPS processors) Publications highlight interdisciplinary work across neuroscience, plasma physics, and computational biology. Awards include ARC Future Fellowship. Active grants focus on T-cell fate control and nanofabrication. Professional memberships include Sigma Xi and the American Physical Society.

• Data Science
• Machine Learning
• Computational Social Science
• +2 more

Ioannis Ziogas is an Assistant Teaching Professor at Georgetown University’s McCourt School of Public Policy and an Assistant Research Professor at the Massive Data Institute. His research bridges data science, machine learning, and computational social science, with a focus on applying algorithmic methods to political phenomena and interdisciplinary challenges. He holds a PhD in Political Science from the University of Florida and is pursuing a second PhD in Computer Science from the University of Auckland. Education: PhD in Political Science, University of Florida Pursuing PhD in Computer Science, University of Auckland (NZ) His research interests span emotion recognition in conversations, computational social science, and health informatics (e.g., Parkinson’s disease analysis via signal processing). Recent work explores hybrid recommendation systems, multimodal emotion detection, and theory-grounded threat assessment for online extremist content. Publications emphasize AI-driven solutions in healthcare monitoring (e.g., tremor classification using IMU sensors), affective computing (e.g., bispectral speech analysis), and tourism technology. His methodological innovations include self-supervised learning frameworks and swarm-based decomposition techniques. Lab/Affiliation: Active contributor to Georgetown’s Massive Data Institute, focusing on large-scale data applications in policy and social science.

• Biomedical Signal Processing
• Machine Learning
• Neonatal EEG Analysis
• +5 more

William Marnane is a Professor of Electrical and Electronic Engineering at University College Cork (UCC). He holds a B.E. from UCC (1984) and a D.Phil. from the University of Oxford (1989). His career includes roles as Lecturer (1989), Senior Lecturer (1999), Dean of Graduate Studies (2013–2016), and Head of the School of Engineering (2016–2019). He has led major research initiatives, including the SFI-funded INFANT Centre and the Claude Shannon Institute. His research focuses on biomedical signal processing, machine learning, and neonatal EEG analysis, with notable contributions to neonatal seizure detection algorithms and fetal health monitoring. He has been awarded the Giner de Los Ríos Visiting Research Fellowship (2007, 2020) and leads projects funded by Wellcome Trust, Science Foundation Ireland, and EU grants. His educational background includes significant contributions to curriculum development and graduate training in engineering and biomedical sciences. He has supervised numerous research projects, including advancements in cryptographic hardware, embedded systems, and low-power signal processing architectures. Research highlights include the ANSeR neonatal seizure detection algorithm (Wellcome Trust-funded), clinical trials on EEG-based seizure recognition, and development of secure TLS coprocessors. He has published over 200 peer-reviewed articles, with key contributions in neonatal EEG analysis, machine learning applications in healthcare, and cryptographic processor design. Current research explores AI for fetal heart rate monitoring and wearable health technologies. Grants and partnerships include leadership roles in EU-funded projects, SFI Strategic Research Clusters, and industry collaborations. He co-directs the INFANT Centre, bridging engineering, medicine, and data science for translational research in neonatal care.

Kang LiView profile

Professor

• Smart grid optimization
• Battery management systems
• Renewable energy integration
• +2 more

Kang Li is Chair of Smart Energy Systems at the University of Leeds, directing the Institute of Communication and Power Networks. With over 30 years' experience, his research develops control systems for decarbonizing energy infrastructure. Key research areas include microgrid operation, railway electrification, battery management, and EV-grid integration. He leads projects funded by EPSRC, Innovate UK, and Ofgem on renewable integration and transport electrification. Industrial collaborations include developing cloud-based energy analytics adopted in manufacturing, winning multiple innovation awards. Current projects examine vehicle-to-grid (V2X) technologies and renewable-powered railway microgrids. 2015 ICI Prize for Best Application Paper 2016 INVENT Award 2016 Sustainable Energy Awards Finalist 2015 Outstanding KTP Award

• Applied Cryptography
• Image Encryption
• Post-Quantum Cryptography
• +5 more

Shahbaz Khan is a researcher and Ph.D. candidate in Computing (Cyber Security and AI) at Edinburgh Napier University, UK. He holds B.S. and M.S. degrees in Electronics and Electrical Engineering from NFC IET and HITEC University, Pakistan. He previously served as a Lecturer in Electrical Engineering at HITEC University for eight years. His research focuses on applied cryptography, image encryption, post-quantum cryptography, and intelligent healthcare systems. He is a member of the Centre for Cybersecurity, IoT and Cyberphysical Systems at Edinburgh Napier University. Education: Bachelor of Science (B.S.) in Electronics and Electrical Engineering, NFC Institute of Engineering & Technology (NFC IET), Pakistan Master of Science (M.S.) in Electronics and Electrical Engineering, HITEC University, Taxila, Pakistan Research Interests: Image Encryption Techniques (e.g., chaos-based, DNA computing, and hybrid models) Post-quantum cryptographic schemes Federated learning in healthcare and IoT Cybersecurity in industrial IoT and edge computing AI-driven medical diagnostics (e.g., EEG-based MDD detection, skin disease classification) Notable Projects: SafeNet: Carnegie-funded project (2024-2025) to enhance IoT network security through robust cryptographic methods. Development of encryption algorithms like VisCrypt , PermutEx , and Noise-Crypt for secure image transmission. AI models for decentralized EEG-based mental health detection and federated learning in skin disease diagnosis. Awards: None explicitly mentioned in available texts. Grants: £13,381 from the Carnegie Trust for the SafeNet project. Labs/Teams: Active member of the Centre for Cybersecurity, IoT and Cyberphysical Systems at Edinburgh Napier University.

Mahnaz ArvanehView profile

Senior Lecturer

• Brain-computer interface (BCI)
• Neural signal processing
• Biomedical signal processing
• +5 more

Dr. Mahnaz Arvaneh is a Senior Lecturer in the Department of Automatic Control and Systems Engineering at the University of Sheffield. She holds a PhD in Advanced Brain-Computer Interface from Nanyang Technological University (2013) and has held academic roles at University College Dublin and Trinity College Dublin. Her research focuses on brain-computer interfaces (BCI), neural signal processing, and their applications in healthcare, including stroke rehabilitation and neuroprosthetics. She directs the Physiological Signals and Systems Laboratory and has published over 50 peer-reviewed papers in top journals/conferences. Research interests include biomedical signal processing, machine learning, neuroprosthetics, and clinical BCI applications. Notable achievements include high-impact media coverage (BBC, Sky News), IEEE editorial roles, and co-editing the IET book Signal Processing and Machine Learning for Brain-Machine Interfaces . She has secured grants totaling over £500k, including EPSRC funding for exoskeleton robotics and neurotechnology development. Dr. Arvaneh's work emphasizes translational research, with projects like TeleRegain (BCI-based stroke telerehabilitation) and Neurophysiological Biomarkers for Neurodegenerative Diseases. Her teaching includes modules on biomedical devices, signal processing, and rehabilitation engineering. Current grants focus on neurotechnology inclusivity, mental healthcare applications, and thermal stimulus quantification in dentistry.

Olga BuchmüllerView profile

Researcher

• Linguistic Register
• Sociolinguistics
• Syntax
• +3 more

Olga Buchmüller is a Researcher at Humboldt-Universität zu Berlin's Institut für Slawistik und Hungarologie. She specializes in linguistic register, syntax, and sociolinguistic variation across Slavic languages. Her work focuses on experimental investigations of language perception, particularly in Czech and Russian, integrating corpus-based methods with perceptual studies. She co-leads Project A03 exploring multilingual registers in Slavic contexts within the SFB 1412 research framework. Teaching: Conducted seminars on psycholinguistics for Slavists, corpus linguistics methods, and German-Slavic linguistic comparisons Research: Active in experimental design for register analysis, sociolinguistic dimensions of speech variation, and cross-modal context effects Recent research highlights include studies on Czech register dimensions using MDA frameworks, syntactic complexity variations in Russian functional styles, and the impact of speaker social status on morphosyntactic evaluations. She collaborates with leading scholars like Prof. Roland Meyer and contributes to interdisciplinary projects bridging experimental linguistics and computational methods. Public engagement includes podcast productions explaining linguistic concepts to broader audiences. Her work consistently emphasizes empirical validation through controlled experiments and perceptual rating studies.