جست‌وجوی استاد و پژوهشگر

Hussein Al Osmanمشاهده پروفایل

دانشیار

• Affective Computing
• Human-Computer Interaction
• Serious Gaming
• +۳ مورد دیگر

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 Buretteمشاهده پروفایل

استادیار

• Synaptic Structure and Function
• Neurodevelopmental Disorders
• Neurobiology of Angelman Syndrome
• +۳ مورد دیگر

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. Aartsمشاهده پروفایل

استاد

• Ambulatory Monitoring
• Signal Processing
• Medical Devices
• +۹ مورد دیگر

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 Xieمشاهده پروفایل

پژوهشگر ارشد

• Statistical/Bayesian Inference
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

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
• +۳ مورد دیگر

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
• +۲ مورد دیگر

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
• +۵ مورد دیگر

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 Liمشاهده پروفایل

استاد

• Smart grid optimization
• Battery management systems
• Renewable energy integration
• +۲ مورد دیگر

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
• +۵ مورد دیگر

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 Arvanehمشاهده پروفایل

مدرس ارشد

• Brain-computer interface (BCI)
• Neural signal processing
• Biomedical signal processing
• +۵ مورد دیگر

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 Buchmüllerمشاهده پروفایل

پژوهشگر

• Linguistic Register
• Sociolinguistics
• Syntax
• +۳ مورد دیگر

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.

Peter Henningsenمشاهده پروفایل

استاد

• Psychosomatic Medicine
• Functional Disorders
• Persistent Physical Symptoms
• +۳ مورد دیگر

Prof. Peter Henningsen is a Professor of Psychosomatic Medicine and Psychotherapy at the Technical University of Munich (TUM), holding the prestigious position of Chair in this field since 2005. He previously served as Dean of the TUM School of Medicine from 2010 to 2019 and as Vice Dean from 2006. His academic journey includes studies in medicine across Stuttgart-Hohenheim, Freiburg, Berlin, and Cambridge (UK), followed by specialization in neurology and psychosomatic medicine. He completed his habilitation in Heidelberg in 2002. Prof. Henningsen’s research focuses on persistent physical symptoms without clear organic causes, including somatoform and functional disorders. He coordinates studies on diagnostic approaches, neurophysiological mechanisms, and therapeutic interventions. He is actively involved in healthcare policy, serving on the German Council of Science and Humanities Medical Committee and leading national initiatives in psychosomatic medicine. His key contributions include advancing understanding of the neurophysiological basis of bodily complaints and advocating for integrated biopsychosocial care models. Awards include the Hans Roemer Prize (2012) and DKV Cochrane Prize (2000). He has authored influential publications in Lancet and Psychosomatic Medicine , emphasizing translational research and clinical implications. Prof. Henningsen’s academic leadership extends to educational innovation, such as evaluating flipped classroom methods in medical education. He collaborates internationally, contributing to guidelines like the S3 Guideline on Functional Body Complaints and the German National Depression Guideline for Primary Care.

Heba El-Fiqiمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Swarm Intelligence
• +۷ مورد دیگر

Dr. Heba El-Fiqi is a Lecturer at the School of Engineering and Information Technology (SEIT), UNSW Canberra. Her research focuses on artificial intelligence, swarm intelligence, machine learning, and computational linguistics. Notable contributions include a novel AI-based solution for air traffic control using swarm robotics and the development of the Weighted Gate Layer Autoencoders (WGLAE) for EEG signal recovery. She holds an IEEE active membership, co-chaired Women in Artificial Intelligence (WAI) activities, and served on conference program committees (AAAI, IJCAI, ECAI). Education: PhD in Computer Science (2013): UNSW, Thesis: "Detection of Translator Stylometry using Pair-wise Comparative Classification and Network Motif Mining" Master of Computer Science (2009): Cairo University, Thesis: "An Intelligent System for Tracking Network Attacks" Research Interests: Swarm robotics, autonomous systems, and AI for air traffic management Machine learning applications in signal recovery and medical diagnostics Computational linguistics and translator stylometry Awards: Dell EMC Award of Distinction (2017) IBM Big Data Developer - Instructor Award for Educators (2017) Teaching & Supervision: Course Coordinator for ZEIT4150 (Artificial Intelligence) and ZEIT4151 (Machine Learning) Supervised over 10 undergraduate engineering projects, including work on deep reinforcement learning for autonomous combat and humanoid robot navigation Labs & Teams: Active in UNSW's AI research community, contributing to initiatives like Canberra AI Week and Women in Engineering (WIE) leadership roles.

Jorne Latonمشاهده پروفایل

استاد

• Neurophysiological Data Analysis
• Machine Learning in Healthcare
• Neurological Disorders Diagnosis
• +۳ مورد دیگر

Jorne Laton is an Unpaid Guest Professor in Clinical Sciences and a postdoctoral researcher in Neuroprotection & Neuromodulation at Vrije Universiteit Brussel. His research focuses on analyzing neurophysiological data (EEG/MEG) to develop machine learning tools for diagnosing neurological disorders like Alzheimer’s, multiple sclerosis, and schizophrenia. He held postdoctoral positions at the University of Oxford (2017–2022) and currently leads projects on AI-driven clinical modeling and cognitive decline prediction. Education: PhD, MScEng Key Projects: ANI419: Multimodal predictor for cognitive decline in mild cognitive impairment (2025–2026) IOF3021: AI applications in neurology and psychiatry (2022–2026) Research Interests: EEG/MEG signal processing, machine learning for diagnosis, neurodegenerative disease biomarkers His work emphasizes translating AI techniques into clinical practice, with recent contributions to federated learning frameworks for brain age modeling and EEG-based schizophrenia classification.

Hafeez Ullah Aminمشاهده پروفایل

مدرس ارشد

• Neuroimaging
• Biomedical Signal Processing
• EEG Signal Processing with Machine Learning
• +۳ مورد دیگر

Hafeez Ullah Amin is a Senior Lecturer in the School of Computer Science at Edge Hill University, UK. Previously, he served as an Assistant Professor at the University of Nottingham Malaysia Campus (2020–2023) and held various research positions at Universiti Teknologi PETRONAS (UTP), including Postdoctoral Researcher and Research Scientist. He holds a PhD in Electrical and Electronic Engineering from UTP, specializing in EEG Signal Processing with Machine Learning. His expertise spans Neuroimaging, Biomedical Signal Processing, and Applied AI/ML in healthcare and education. Education: PhD (Electrical and Electronic Engineering), Universiti Teknologi PETRONAS, 2011–2015 MPhil (Computer Science with AI), Kohat University of Science and Technology, 2006–2009 BSc (Information Technology), Kohat University of Science and Technology, 2001–2005 Research Interests: Neuroimaging, EEG Signal Processing, AI in Mental Healthcare, Data Analytics, and Machine Learning applications in education and business. His work intersects neuroscience, biomedical engineering, and computational methods to address challenges in memory assessment, stress mitigation, and learning efficacy. Publications: Over 50 articles in high-impact journals and conferences, including work on EEG-based neurofeedback, machine learning for healthcare analytics, and 3D educational content efficacy. Recent trends focus on EEG functional connectivity, fraud detection via graph autoencoders, and energy forecasting. Awards: Senior Member, IEEE Fellow (FHEA), Advance HE, UK Advising & Grants: Supervises PhD students in AI/ML applications. Active in research grants involving EEG experiments, patent on LTM assessment via EEG, and collaborations in smart cities and energy systems. Teaching: Leads courses in Data Science, AI, Microprocessor Systems, and Knowledge Representation. Past roles include Module Leader for Programming Principles and Techniques. Labs/Tech Teams: Associated with Edge Hill's Data and Complex Systems Research Centre and the International Centre for Applied Research in Education.