جستجوی استاد راهنمای دانشگاهی

• Optical Fibers
• Photonics
• Fiber Optic Sensors
• +۳ مورد دیگر

Oskar Arrizabalaga is an Associate Professor at the Department of Applied Mathematics, University of the Basque Country (UPV/EHU), located at the Faculty of Engineering in Bilbao. His research focuses on theoretical and experimental studies of polarization in microstructured polymer optical fibers (mPOFs), with applications in advanced sensor technologies. He teaches Advanced Mathematics and contributes to the Applied Photonics Group (APG), a 25-member team recognized for excellence in fiber-optic research. Research Interests: His work emphasizes developing optical fiber-based sensors for biomedical, environmental, and structural monitoring applications. Key areas include biosensors for viral detection, multi-core fiber sensors, and interferometric devices for precision measurements. His research integrates polymer optics and photonics to address challenges in healthcare and engineering. Publications: Oskar’s recent work highlights innovations in fiber optic biosensors (e.g., SARS-CoV-2 detection), high-performance curvature sensors, and miniature interferometric systems. These studies reflect a focus on practical, scalable solutions for real-world sensing needs. Teaching & Affiliations: He teaches Advanced Mathematics and collaborates within the APG’s facilities, which specialize in polymer optical fiber fabrication, sensor design, and interdisciplinary photonics applications. No scientific awards are explicitly mentioned, though his contributions align with the group’s broader recognition in the Basque Science System.

• Data-driven intelligence
• Control systems
• Machine learning
• +۵ مورد دیگر

Dr. Long Zhang is a Senior Lecturer in the Department of Electrical and Electronic Engineering at the University of Manchester. His research focuses on data-driven intelligence and control systems, including machine learning, neural networks, and their applications in renewable energy, robotics, and transportation. He leads the first industrial-scale wind turbine pitch bearing and blade laboratory at the university and directs the MSc program in Advanced Control and System Engineering. His work is supported by over £2.5m in research funding from EPSRC, Innovate UK, and industry partners. Research interests include data-driven modeling, system identification, wind turbine condition monitoring, and smart device design. His group has supervised 12 PhD students, 50 MSc students, and 50 undergraduate projects. Key contributions include fault diagnosis methodologies for wind turbines and robotics, and predictive maintenance frameworks using advanced signal processing and machine learning techniques. Recent publications highlight advancements in autonomous system fault detection, energy grid flexibility mechanisms, and wave energy converter monitoring. Dr. Zhang collaborates with interdisciplinary teams across robotics, aerospace, and digital futures, contributing to UN Sustainable Development Goals related to affordable energy and climate action.

Peter Raynerمشاهده پروفایل

دانشگاهی

• Organic Chemistry
• Catalytic Chemistry
• Nuclear Magnetic Resonance (NMR)
• +۳ مورد دیگر

Peter Rayner is an Associate Lecturer in the Department of Chemistry at the University of York, affiliated with the Centre for Hyperpolarisation in Magnetic Resonance. He holds an MChem (2009) and a PhD in Organic Chemistry (2013), both from the University of York. His research focuses on developing synthetic and catalytic chemistry for biomedical applications, particularly leveraging the SABRE (Signal Amplification by Reversible Exchange) technique to enhance nuclear magnetic resonance (NMR) imaging for diagnostic purposes. Key areas include designing isotopically labeled substrates and novel ligands for SABRE catalysts to improve hyperpolarization and prolong magnetic lifetimes. He has presented at conferences such as the Dalton Division Northern Meeting and the NMR Discussion Group, and his work spans interdisciplinary collaborations in analytical chemistry, catalysis, and materials science. Rayner’s research emphasizes translating SABRE advancements into pre-clinical MRI applications for disease diagnosis. His publications highlight innovations in catalyst design, hyperpolarization methods, and applications in drug scaffolds, including tuberculosis treatments. Over 30 peer-reviewed articles and seven datasets underscore his contributions to SABRE’s development and its biomedical potential. His lab utilizes techniques like X-ray crystallography and mass spectrometry to study structure-activity relationships, driving advancements in hyperpolarized MRI probes and pharmaceutical agents.

John Chaputمشاهده پروفایل

استاد

• Chemical Biology
• Biochemistry
• Synthetic Biology
• +۳ مورد دیگر

John Chaput is a Professor in the Department of Chemistry at the University of California, Irvine. His research focuses on chemical biology, synthetic nucleic acids, and biomolecular engineering. He leads efforts to design novel genetic polymers like XNA (xeno-nucleic acid) and TNA (threose nucleic acid), exploring their applications in diagnostics, therapeutics, and synthetic biology. Chaput’s work emphasizes functional nucleic acid engineering, including the development of XNAzymes for gene silencing and diagnostic systems. His lab employs directed evolution, microfluidics, and structural biology techniques to enhance polymerase efficiency and nucleic acid stability. Key areas of innovation include the REVEALR diagnostic platform for rapid pathogen detection, advances in TNA polymerase evolution, and the synthesis of stable nucleic acid analogs resistant to biological degradation. Chaput’s research bridges organic chemistry, enzymology, and molecular biology, with implications for drug delivery, genetic engineering, and understanding fundamental replication mechanisms. His contributions span over 150 peer-reviewed publications, with recent work highlighting nucleic acid therapeutics, cross-species genetic systems, and the structural basis of DNA replication. Chaput collaborates with industry partners to translate lab discoveries into clinical diagnostics and therapies.

E. Ricky Chanمشاهده پروفایل

دانشگاهی

• Bioinformatics
• Genomics
• Immunology
• +۴ مورد دیگر

E. Ricky Chan, PhD is an Instructor in the Department of Population and Quantitative Health Sciences at Case Western Reserve University School of Medicine. He serves as Director of the Bioinformatics Core at the Institute for Computational Biology. His work integrates genomics, computational biology, and systems biology to study immune mechanisms, metabolic pathways, and microbiome interactions. Dr. Chan holds a PhD from Case Western Reserve University. His research focuses on translational applications of big data analytics in precision medicine, with emphasis on inflammatory bowel disease, cancer biology, and infectious diseases. Recent work includes groundbreaking studies on GSDMB's role in IBD epithelial repair, microbiome metabolic interactions, and synthetic lethality in cancer therapy. Key research trends include multi-omic spatial analysis of neuroinflammation, transcriptomic profiling of drug resistance mechanisms, and leveraging genomic data to understand disease progression. His studies have identified critical biomarkers like the NRF2 gene set and MBOAT7-driven lipid pathways. Current projects involve developing computational tools for viral genomic surveillance and improving microelectrode implant biocompatibility. Dr. Chan leads the Bioinformatics Core supporting over 50 research groups, providing expertise in next-gen sequencing analysis and machine learning applications. His team bridges basic research and clinical translation through collaborative initiatives in immunology, oncology, and metabolic disorders.

Simon Kasifمشاهده پروفایل

استاد

• Computational Biology
• Genomics
• Bioinformatics
• +۵ مورد دیگر

Simon Kasif is a Professor of Biomedical Engineering at Boston University. He co-founded the Center for Advanced Genomic Technology (CAGT), COMBREX (Computational Bridges to Experiments), and is a member of the I2B2 Center. His work spans computational biology, systems biology, and AI applications in healthcare. He contributed to the Human Genome Project, co-developed tools like Glimmer and Mummer, and pioneered probabilistic networks for gene function prediction. Education: M.S. & Ph.D., Computer Science, University of Maryland B.Sc., Mathematics, Tel Aviv University Research Interests: Dr. Kasif focuses on integrating computational methods with biological data to understand disease mechanisms, particularly diabetes and cancer. His work emphasizes AI ethics, network biology, and translational research. He has developed methodologies for protein interaction networks, genomic data analysis, and systems biology modeling. Key Contributions: Co-developed Glimmer, a widely used gene prediction tool Pioneered functional linkage networks for gene function prediction Advocated Bayesian networks in computational biology Investigated disease network signatures, including diabetes and Alzheimer’s Lead projects on AI safety, SARS-CoV-2 interaction mapping, and metabolic pathway analysis Grants & Collaborations: Active in interdisciplinary projects, including the COMBREX initiative for protein function annotation and I2B2’s clinical data integration. His work bridges computational methods with clinical applications, fostering open science and reproducible research practices. Labs & Teams: Directs research at CAGT and I2B2, collaborating across institutions to advance genomic technologies and systems biology approaches.

• Electrical Engineering
• Computer Science
• Wireless Communications
• +۵ مورد دیگر

Dr. Ahmed Mohamed Hossam is a part-time lecturer in the School of Electrical Engineering and Computer Science at the University of Ottawa. His research interests span multiple disciplines, including wireless communications, network security, renewable energy systems, and healthcare technology. He has contributed to advancements in reconfigurable intelligent surfaces (RIS) for 6G networks, IoT applications, and smart health systems. His work often intersects with interdisciplinary topics such as energy systems modeling (e.g., PV-wind systems for hydrogen generation) and medical informatics (e.g., obesity trends and neurological case studies). Recent publications highlight his focus on securing low-power wireless networks, optimizing 5G/6G architectures, and improving urban infrastructure through algorithmic solutions. Notable contributions include studies on physical layer security in emerging wireless systems, smart ambulance systems, and parking allocation algorithms. His research bridges theoretical advancements with practical applications in healthcare technology and sustainable energy.

George Dwapanyinمشاهده پروفایل

پژوهشگر ارشد

• Optical Imaging Techniques
• Biomedical Applications
• Analytical Chemistry
• +۳ مورد دیگر

Dr. George Dwapanyin is a Research Fellow in Advanced Optical Imaging at the School of Physics and Astronomy, University of St Andrews. His research focuses on developing advanced optical imaging techniques with applications in biomedical diagnostics, analytical chemistry, and embryology. He specializes in Raman spectroscopy, digital holographic microscopy, and hyperspectral light-sheet imaging, particularly for non-invasive assessment of embryo development and quality control in food products like whisky. His work bridges biophotonics and engineering to address challenges in reproductive medicine, metabolic imaging, and material analysis. Dr. Dwapanyin has contributed to pioneering studies such as using portable Raman spectroscopy for whisky authentication and UVA hyperspectral microscopy for volumetric metabolic imaging of preimplantation embryos. His research outputs emphasize innovation in optical systems and their practical applications. He collaborates with international teams on projects involving light-sheet microscopy, laser beam shaping for concealed sample analysis, and refractive index dynamics in embryos. His datasets underpin advancements in embryo development monitoring and imaging techniques. Contact: god1@st-andrews.ac.uk | Phone: +44 (0)1334 461822

Tao Guمشاهده پروفایل

استاد

• Internet of Things
• Ubiquitous Computing
• Mobile Computing
• +۳ مورد دیگر

Prof. Tao Gu is a Professor at the Department of Computing, Macquarie University, Sydney. He holds affiliations with the Future Communications Research Centre, Hearing Research Centre, and Smart Green Cities Research Centre. His research focuses on IoT, Ubiquitous Computing, Mobile Computing, Embedded AI, Wireless Sensor Networks, and Big Data Analytics. He earned his Ph.D. in Computer Science from the National University of Singapore (2006), M.Sc. in Electrical and Electronic Engineering from Nanyang Technological University, and B.Eng. in Automatic Control from Huazhong University of Science and Technology. Education: PhD (NUS), MSc (NTU), B.Eng (HUST) Affiliations: School of Computing, Macquarie University Research Themes: IoT, Embedded AI, Wireless Communication His research emphasizes innovative sensing and connectivity solutions. Notable projects include LoRa network optimization, mobile deep learning frameworks (MDLdroid), and acoustic-based health monitoring systems. He has received awards such as the IEEE SMARTCOMP 2016 Best Paper Award and the Ten Years CoMoRea Impact Paper Award at PerCom 2013. Current work explores secure device pairing, energy-efficient LoRa protocols, and multi-modal sensing using WiFi/RF signals. His lab develops systems like AudioGuard for intrusion detection and AIMSafe for driver behavior analysis. Ongoing projects address data-driven economies, hearing healthcare analytics, and non-intrusive human activity sensing.

Mehmet Orgunمشاهده پروفایل

استاد

• Artificial Intelligence
• Biomedical Image Processing
• Knowledge Discovery
• +۳ مورد دیگر

Mehmet Orgun is a Professor at the School of Computing, Macquarie University. He holds a BSc and MSc from Hacettepe University (Turkey) and a PhD from the University of Victoria (Canada). His research spans artificial intelligence, biomedical image processing, multi-agent systems, and secure systems. He has supervised over 30 PhD and master's students and led multiple ARC-funded projects totaling over $4M. His professional service includes roles at PRICAI 2010 and SIN 2019. Research Interests: Artificial Intelligence Quantum Cryptography Trusted Systems Medical Imaging Recommender Systems Awards: Best Student Runner Up Award (ADMA'23) Best Paper Award (MoMM2020) Best Student Paper Award (ICWS2016) Advising & Grants: 30+ PhD/Master's students ARC grants exceeding $4M Labs/Teams: Collaborations include Australian Taxation Office and DSTO. Projects focus on trust-oriented data analytics and secure systems.

Satarupa Chakrabartiمشاهده پروفایل

پژوهشگر ارشد

• Biomedical Engineering
• Signal Processing
• Machine Learning
• +۳ مورد دیگر

Satarupa Chakrabarti is a Digital Futures Postdoctoral Research Fellow at KTH Royal Institute of Technology, based in the Computational Brain Science group within the Division of Computational Science and Technology. She holds a PhD in Computer Science from KIIT Deemed to be University, India, and previously worked as a Junior Research Fellow with DST-SERB, India. Her research focuses on biomedical engineering, signal processing, machine learning, and space physics, with a particular emphasis on developing brain activity-based biomarkers for Parkinson’s disease diagnosis and prognosis. Her academic background includes interdisciplinary projects on biomedical signal analysis, pediatric epilepsy detection, and power system fault localization. She collaborated with multidisciplinary teams at KTH, contributing to innovations in healthcare technology and smart societal solutions. Her postdoc project (2020–2022), funded by Digital Futures, aimed to extract temporal features from brain activity to improve PD diagnosis, leveraging machine learning advancements. Supervised by Professors Arvind Kumar and Saikat Chatterjee, her work bridges computational science and clinical applications. Publications span biomedical engineering (e.g., pediatric seizure detection), electrical engineering (HVDC fault localization), and space physics (ionospheric plasma analysis). Her contributions highlight machine learning applications across diverse domains. No scientific awards were explicitly mentioned. She is affiliated with Digital Futures, a cross-disciplinary center jointly established by KTH, Stockholm University, and RISE.

Samaneh Kouchakiمشاهده پروفایل

مدرس ارشد

• Biomedical signal processing
• Deep supervised/semi-supervised learning for healthcare data
• Graph learning and embedding for omics data
• +۵ مورد دیگر

Samaneh Kouchaki is a Senior Lecturer in Machine Learning for Healthcare at the University of Surrey, affiliated with the Centre for Vision, Speech and Signal Processing (CVSSP) within the School of Computer Science and Electronic Engineering. She leads research on machine learning applications in healthcare, particularly dementia care and antibiotic resistance. Her roles include teaching, supervision of 6 PhD students, and collaboration with the UK Dementia Research Institute. Education : PhD in Computer Science (University of Surrey, 2015), postdoctoral research at University of Oxford (TB genomics) and University of Manchester (bioinformatics). Research Interests : Focuses on healthcare AI, including biomedical signal processing, graph learning for omics data, and sensor-based remote monitoring. Develops machine learning tools for early disease detection and personalized care. Publications : Over 30 peer-reviewed articles in top journals like NPJ Digital Medicine and The Lancet Microbe , covering topics from antibiotic resistance prediction to dementia care technologies. Recent work emphasizes lightweight models for clinical NLP and unsupervised anomaly detection. Lab & Teams : Leads research in health AI at CVSSP, collaborating with UK DRI, Imperial College London, and NHS trusts. Develops TIHM platform for remote monitoring of dementia patients.

Rui Henriquesمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Bioinformatics
• +۴ مورد دیگر

Rui Henriques is an Assistant Professor at the Department of Electrical and Computer Engineering (DEI), Instituto Superior Técnico (IST), University of Lisbon. He is also an Associate Researcher at INESC-ID. His research focuses on data mining, machine learning, bioinformatics, and urban analytics, with applications in healthcare, transportation systems, and pattern recognition. He has advised numerous PhD and MSc students, contributing to impactful projects in areas like healthcare decision support, mobility optimization, and environmental data analysis. Education: PhD in Learning from High-Dimensional Data (2016) from IST, University of Lisbon. Research interests include developing algorithms for high-dimensional data analysis, integrating spatiotemporal data for urban planning, and applying machine learning to biomedical challenges. Notable contributions include tools like BicPAMS for biological data analysis and software frameworks for transportation analytics. His work spans over 100 publications in top journals and conferences, addressing topics from triclustering algorithms to fraud detection in online tests. He leads or contributes to projects such as ILU (city mobility optimization) and Data2Help (emergency medical services optimization). His research emphasizes interdisciplinary approaches, combining data science with real-world applications in health, transport, and sustainability.

Tim Bardouilleمشاهده پروفایل

استادیار

• Neuroscience
• Biomedical Engineering
• Neuroimaging
• +۳ مورد دیگر

Tim Bardouille is an Assistant Professor at Dalhousie University, holding a primary appointment in the School of Biomedical Engineering and the Department of Physics & Atmospheric Science, with additional cross-appointments in the Department of Diagnostic Radiology, the Department of Psychology and Neuroscience, and the School of Physiotherapy. His educational qualifications are as follows: Doctor of Philosophy, University of Toronto Master of Science, Dalhousie University Bachelor of Science (Honours), Queen's University, Kingston, ON Dr. Bardouille's research focuses on neuroimaging and the development of non-invasive techniques to study brain activity. He is particularly interested in magnetoencephalography (MEG) using optically pumped magnetometers, which the Biosignal Lab at Dalhousie houses as Canada's first such system. His work involves creating new hardware and software for brain imaging and developing advanced data analysis methods, including machine learning algorithms, to interpret complex brain signals. This research has applications in understanding brain plasticity, stroke recovery, and neurodevelopmental disorders. The Biosignal Lab, directed by Dr. Bardouille and located in Room 229 of the Sir James Dunn Building, serves as a center for innovation in neuroimaging. The lab collects local data using state-of-the-art MEG technology and also utilizes large open-access datasets to develop and test new analytical approaches.

Deirdre Murrayمشاهده پروفایل

استاد

• Cancer Epidemiology
• Public Health Medicine
• Neonatal Care
• +۳ مورد دیگر

Professor Deirdre Murray is the Director of the National Cancer Registry Ireland (NCRI) and Professor of Cancer Epidemiology at University College Cork (UCC). She leads the NCRI's strategic initiatives and oversees cancer data analysis across Ireland. Her expertise spans public health medicine, cancer epidemiology, and neonatal neurology research. She chairs the UK and Ireland Association of Cancer Registries and contributes to international collaborations like the EUROCARE consortium. Professionally, she holds roles on the National Screening Advisory Committee and National Research Ethics Committee, emphasizing her commitment to public health governance. Trained in medicine at UCC and with a Master’s in Public Health from UCD, Dr. Murray’s career bridges clinical practice and population health. Her research focuses on cancer outcomes, neonatal brain injury, and early childhood neurodevelopment. Notable projects include developing machine learning models for neonatal risk assessment and examining long-term outcomes of preterm infants. She advocates for standardized neonatology training through pan-European initiatives and explores digital health tools for monitoring conditions like motor neurone disease. Her work integrates interdisciplinary approaches to address complex health challenges, such as optimizing cancer registries, improving data governance in multi-national studies, and advancing care for at-risk populations. Her contributions span clinical leadership, policy development, and innovative research methodologies.