جست‌وجوی استاد و پژوهشگر

Yaqub Jonmohamadiمشاهده پروفایل

پژوهشگر

• Computer Vision
• Deep Learning
• Multimodal Data Fusion
• +۳ مورد دیگر

Yaqub Jonmohamadi is a former Associate Investigator at the QUT Centre for Robotics. He holds a PhD in neuroimaging and has conducted postdoctoral research since 2015 in computer vision, biomedical engineering, and neuroimaging. His research interests span computer vision, deep learning, multimodal data fusion, and signal processing. He has contributed to advancements in medical imaging, robotic surgery, and neuroimaging through interdisciplinary collaborations. His work includes 3D semantic mapping for surgical robotics, illumination control in arthroscopy, and EEG-fMRI data fusion techniques. Publications focus on applying machine learning and computer vision to healthcare challenges, such as surgical scene restoration and knee arthroscopy segmentation. His research demonstrates expertise in both theoretical and applied aspects of robotics and biomedical engineering. Jonmohamadi’s GitHub repository hosts code related to his projects, reflecting a commitment to open science. He has collaborated with institutions like Elsevier and Springer on works advancing robotic surgery and medical imaging. Despite no listed awards, his academic contributions are evident through his peer-reviewed publications and technical innovations in medical robotics. His work bridges computer science and healthcare, addressing real-world surgical and diagnostic challenges.

Ian McQuillanمشاهده پروفایل

استاد

• Natural Computing
• Biological Modelling
• Bioinformatics
• +۳ مورد دیگر

Ian McQuillan is a Professor in the Department of Computer Science at the University of Saskatchewan. He is affiliated with the Bioinformatics Lab and the Bioinformatics Program, and an associate member of the Division of Biomedical Engineering. He holds a Ph.D. and undergraduate degree in Computer Science and Mathematics from the University of Western Ontario. His research focuses on natural computing, biological modeling, bioinformatics, formal language theory, and theoretical computer science. Current projects include modeling transposable elements, plant growth simulation, and gene assembly in ciliates. He is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC). McQuillan teaches courses such as Bioinformatics and Computational Biology, Modelling and Algorithms of Biological Systems, and Automata and Formal Languages. His lab emphasizes professional conduct, collaboration, and inclusivity, adhering to a detailed Code of Conduct. He advises a diverse group of graduate and undergraduate students, with research contributions spanning computational biology, formal languages, and algorithm design. His work bridges theoretical foundations with practical applications in systems biology and bioinformatics.

• RNA Biology
• Neurodegenerative Diseases
• Neuronal mRNA Translation
• +۵ مورد دیگر

Junjie Guo is an Associate Professor of Neuroscience at Yale School of Medicine, leading the Guo Lab focused on RNA biology and neurological mechanisms. He holds affiliations with the Interdepartmental Neuroscience Program, Center for RNA Science and Medicine, and Wu Tsai Institute. Education : B.A. in Biology (Peking University, 2006), Ph.D. in Neuroscience (Johns Hopkins University, 2011), Postdoctoral Fellow at Whitehead Institute/MIT (2017). Research Interests : Investigates RNA dysregulation in neurodegenerative diseases (e.g., ALS/FTD), noncanonical mRNA translation in neuronal development, and spatial/temporal regulation of neuronal mRNA transport and local translation. Utilizes genomic, molecular, and computational approaches in stem cells, neuronal models, and mouse models. Recent Article Trends : Focuses on SARS-CoV-2 replication mechanisms, RNA structural biology (e.g., G-quadruplexes and tandem repeats), and translational control pathways. Highlights include CRISPR-based screens for viral dependency factors and discovery of alternative translation initiation roles in synaptic organizers. Awards : McKnight Neurobiology of Brain Disorders Award (2023), NYSCF-Robertson Neuroscience Investigator (2021), NIH New Innovator Award (2018), and multiple fellowships from the Klingenstein-Simons and Damon Runyon Foundations. Advising & Grants : Mentors postdocs (e.g., Zhen Lei), graduate students (e.g., Ata Isiktas), and others. Leads NIH-funded projects on RNA biology and neurological disorders. Collaborates with researchers like Marina Picciotto and Craig Wilen. Labs/Teams : Directs the Guo Lab, part of the Yale Neuroscience community. Engages in interdisciplinary initiatives through the Wu Tsai Institute and Center for RNA Science and Medicine.

• Computer Vision
• Machine Learning
• Image Processing
• +۴ مورد دیگر

Adjunct Professor Shuai Wan is affiliated with the School of Engineering at RMIT University (City Campus, Australia). His research focuses on computer vision, machine learning, 3D point cloud compression, neural video coding, and remote sensing . Key contributions include lightweight deep learning frameworks for image/video compression, spatio-temporal context models for point clouds, and adaptive quantization techniques. Research Outputs Insights : Wan’s work spans 2024–2025 , emphasizing end-to-end deep learning solutions for challenges in Exemplar-based colorization with semantic attention Rendering-oriented 3D point cloud compression Slimmable video codecs with variable bitrate G-PCC standard enhancements for quantization and entropy coding Adversarial example detection in remote sensing Technical Domains : His articles intersect artificial intelligence, signal processing, and computer graphics , with applications in cloud gaming, SAR systems, and industrial data compression. Methods include transformers, attention networks, and 3D convolutional architectures .

Hugo Larochelleمشاهده پروفایل

دانشیار

• Neural Networks
• Deep Learning
• Machine Learning
• +۴ مورد دیگر

Hugo Larochelle is an Associate Professor at the Université de Montréal's Department of Computer Science and Operations Research (DIRO) within the Faculty of Arts and Sciences. His expertise spans Neural Networks, Deep Learning, and Machine Learning, with contributions to fields like Computer Vision and Natural Language Processing. A graduate of DIRO (Bacc 2004, PhD 2009), he held roles at the University of Sherbrooke (2011–2016) and co-founded Whetlab (acquired by Twitter in 2015). Currently at Google Brain, he balances academic and industrial research, leading projects like the UNIQUE initiative exploring neuroscience-AI intersections. He actively contributes to Quebec’s AI ecosystem through MILA and has received prestigious awards, including the 2019 Diplômé d'honneur. His work emphasizes ethical AI, reproducibility, and interdisciplinary applications. Education: Baccalauréat (Computer Science), Université de Montréal, 2004 Doctorat (Computer Science), Université de Montréal, 2009 Research Interests: Hugo’s work focuses on advancing deep learning techniques for real-world applications, including environmental monitoring (e.g., tree crown segmentation via drone imagery), AI ethics, and model unlearning. He explores intersections between neuroscience and AI, leveraging interdisciplinary approaches to solve complex problems. Recent efforts emphasize benchmark standardization (e.g., EEVEE/GATE) and improving model efficiency through architecture innovations like SoftMoE. Articles Trends: Recent publications highlight contributions to instance segmentation (SAM models), ethical AI (unlearning frameworks), and interdisciplinary applications (e.g., bird species modeling with remote sensing). His work bridges theoretical advancements with practical tools for industries and environmental science. Scientific Awards: 2019 Diplômé d'honneur (Université de Montréal). Advising & Grants: Supervised PhD students in topics like neural networks, code modeling, and program execution (e.g., Sara Hooker, David Bieber). Co-PI on the UNIQUE project (2019–2024, FRQNT-funded), exploring neuro-AI synergies. Recipient of CIFAR funding for ICRA research (2017–2022). Labs & Collaborations: Active contributor to MILA as an associate member. Leads interdisciplinary efforts in AI for environmental challenges and healthcare imaging optimization.

Ihsan Al-affanمشاهده پروفایل

مدرس ارشد

• Radiation Safety
• Monte Carlo Modeling
• Radiotherapy Room Design
• +۳ مورد دیگر

Dr. Ihsan Al-Affan is a Senior Lecturer in Medical Physics at Swansea University's Faculty of Medicine, Health and Life Sciences, part of the Swansea University Medical School. He holds an academic position within the Medical Physics department and is available for postgraduate supervision. His career spans roles including Clinical Scientist at Velindre Hospital (1990-1998), Lecturer at Zarka University (Jordan, 1998-2001), and Academic Adviser for the UAE Technical Training Project (2001-2011). Currently teaching on the MSc Medical Radiation Physics program, he instructs modules such as Radiation Safety, Advanced Radiation Safety, and Introduction to Medical Physics Practice. Research focuses on radiation safety engineering, Monte Carlo modeling for radiation therapy design, and mitigating radiation risks in medical facilities. Notable projects include optimizing maze designs for linear accelerator rooms and assessing radon exposure risks in dwellings. His work combines theoretical modeling (e.g., FLUKA code simulations) with experimental validation using scintillation detectors. Teaching responsibilities include: PMPM03 Radiation Physics PMPM09 Radiation Protection PMPM19 Advances in Radiation Safety PMPM29 Specialist Radiation Safety PMRM00 Introduction to Medical Physics Practice His research contributions emphasize practical applications of medical physics principles to improve healthcare facility safety standards.

Yan Yanمشاهده پروفایل

استادیار

• Bioinformatics
• Machine Learning
• Big Data Analysis
• +۷ مورد دیگر

Yan Yan is an Assistant Professor at the School of Computer Science, University of Guelph. His research focuses on bioinformatics, multi-omics analysis, and AI-driven computational models for understanding biological processes. His lab develops algorithms for genome-wide association studies, single-cell RNA-seq analysis, and de novo peptide sequencing, with applications in plant genomics and digital agriculture. Education : Not explicitly stated, but his publications suggest advanced training in bioinformatics and computational biology. Research Interests : Bioinformatics, Machine Learning, Big Data Analysis, Statistical Association, and AI applications in agriculture. His work integrates artificial intelligence with omics data to uncover disease mechanisms and improve agricultural practices. Recent trends in his publications emphasize robotics, structural health monitoring, and environmental sensing using hybrid neural networks and deep learning techniques. Grants & Awards : No awards explicitly listed, but his prolific publication record indicates sustained research activity. Advising & Labs : Advises students like B S Puliparambil and J Tomal. His lab actively collaborates on projects involving AI in agriculture, robotics, and structural engineering. Current initiatives include digital twins for human-robot collaboration and precision livestock monitoring systems. Labs/Teams : Leads a computational biology and AI lab focusing on multi-omics integration and smart agriculture solutions.

• Biomedical Engineering
• Autonomous Systems
• Robotics
• +۴ مورد دیگر

Rafael Barea Navarro is a Professor in the Department of Electronics Technology at the University of Alcalá. His research focuses on biomedical engineering, autonomous systems, robotics, artificial intelligence, and driver safety. He leads the Biomedical Engineering Research Group (GIB) and the Robótica de Servicios y Tecnologías para la Seguridad Vial (Robesafe) group. His work bridges AI applications in medical diagnostics (e.g., multiple sclerosis via OCT) and advanced autonomous vehicle technologies, including motion prediction, reinforcement learning, and real-time safety systems. Education: PhD in Electronics from the University of Alcalá (2001), with a thesis on EOG-based human-computer interfaces for mobility assistance. His research spans over 50 peer-reviewed articles since 2000, emphasizing practical applications in healthcare and robotics. Key research themes include: 1) AI-driven medical diagnostics; 2) autonomous vehicle control systems; 3) sensor fusion for navigation; and 4) driver attention monitoring. His work integrates robotics, computer vision, and biomedical signal processing. Publications emphasize interdisciplinary approaches, with recent work combining OCT and explainable AI for early disease diagnosis, and hybrid reinforcement learning frameworks for urban autonomous driving.

Spilianakis Charalamposمشاهده پروفایل

دانشیار

• 3D Epigenetics
• Genome Architecture
• Chromosomal Interactions
• +۵ مورد دیگر

Charalampos Spilianakis is an Associate Professor at the University of Crete with a research affiliation at the Institute of Molecular Biology and Biotechnology (IMBB) of the Foundation for Research and Technology - Hellas (FORTH). His work focuses on the three-dimensional organization of the genome and its critical role in gene regulation within immune cells, particularly T cells. Dr. Spilianakis earned his PhD in 2003 from the University of Crete in Biochemistry, Molecular Biology, Cellular and Developmental Biology. His academic career has been dedicated to understanding how nuclear architecture influences cellular function and gene expression patterns. His research program investigates both cis and trans regulatory mechanisms of gene expression, with particular emphasis on how protein complexes generate and maintain long-range chromosomal interactions in immune cell populations. His laboratory employs an integrated approach combining biocomputing, molecular techniques, biochemical assays, advanced imaging, and genetic analyses to explore the field of 3D epigenetics. This work has significant implications for understanding immune cell differentiation and function. Analysis of Dr. Spilianakis' publication record reveals a consistent research trajectory focused on chromatin architecture and transcriptional regulation in immune cells. His contributions include developing protocols for chromosome conformation capture in primary T cells, elucidating SATB1's role in shaping the 3D enhancer network during T cell development, and investigating the nuclear periphery's role in microRNA gene localization. His work bridges molecular biology, immunology, and genomics to advance spatial genomics understanding. Based at IMBB-FORTH (Office A251, Lab A204), Dr. Spilianakis leads a research group that serves as an important hub for investigating the relationship between nuclear architecture and gene expression in the immune system. His laboratory combines cutting-edge molecular and imaging technologies to address fundamental questions about genome organization and its functional consequences in health and disease.

Stéphane BORDASمشاهده پروفایل

استاد

• Computational Mechanics
• Biomechanics
• Finite Element Methods
• +۶ مورد دیگر

Stéphane BORDAS is a Full Professor in Computational Mechanics at the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM), leading the Computational Mechanics (Legato) research group. His work focuses on free boundary problems, method development for complex geometries, and applications in fracture mechanics, biomechanics, and computational engineering. He previously held roles at Cardiff University and the Swiss Federal Institute of Technology in Lausanne (EPFL). His research integrates computational methods like XFEM, isogeometric analysis, and meshfree techniques to address challenges in engineering and medicine. Education: Ph.D. in Theoretical and Applied Mechanics, Northwestern University (2003) M.Sc. in Civil Engineering, École Spéciale des Travaux Publics and Northwestern University (1999) Research Interests: Computational Mechanics, Biomechanics, Finite Element Methods, Fracture Mechanics, High-Performance Computing, Isogeometric Analysis. Grants & Projects: ERC Starting Grant (RealTCut) for surgical simulation and material cutting FP7 ITN INSIST for meshless methods Labs/Teams: Computational Mechanics (Legato) Group at the University of Luxembourg. His work bridges academia and industry, with applications in aerospace, biomedical engineering, and materials science. He is active in open-source software development, including codes for XFEM, isogeometric analysis, and meshfree methods.

• Artificial Intelligence
• Neural Networks
• Quantization
• +۳ مورد دیگر

Hang Li is a Researcher in the Department of Molecular Biophysics and Biochemistry at Yale University’s Yale School of Medicine. Their work focuses on advancing neural network architectures, quantization techniques, and spiking neural networks (SNNs). They are affiliated with the Molecular Biophysics and Biochemistry department and contribute to interdisciplinary research in artificial intelligence and computational neuroscience. Research interests include optimizing neural networks for efficiency through quantization, exploring spiking neural networks for low-power computing, and developing methods like hybrid SNN designs, post-training calibration, and neuromorphic architectures. Their recent work addresses challenges in extreme low-bit quantization, data augmentation for object detection, and temporal coding in SNNs. Publications highlight innovations in quantization methods (e.g., TesseraQ, GenQ), spiking transformer architectures, and workload-balanced pruning strategies. While no awards are explicitly listed, their contributions to model efficiency and neuromorphic computing are notable in the field. Hang Li collaborates on projects involving neuromorphic hardware, system inconsistency benchmarking (SysNoise), and data-driven spatio-temporal analysis. Their research bridges theoretical advancements and practical applications in AI and biomedical informatics.

Emily Jeffersonمشاهده پروفایل

استاد

• Health Data Science
• Trusted Research Environments (TREs)
• Data Science
• +۶ مورد دیگر

Emily Jefferson is a Professor of Health Data Science at the University of Dundee, currently serving as CTO of Health Data Research (HDR) UK and Interim Director of DARE UK. She holds an honorary professorship in Population Health and Genomics. With a PhD in Bioinformatics and industry experience in Big Data and project management, her career spans academia, finance, and solo global travel. Her research focuses on Trusted Research Environments (TREs), data governance, and machine learning applications in healthcare. She led the Health Informatics Centre (2013–2022), managing a 60-person team supporting over 100 projects. Key achievements include ISO27001 certification and Scottish Government-accredited Safe Haven infrastructure. Since 2014, she has secured over £120M in grants as PI/Co-I, delivering £28M. Notable projects include HT-ADVANCE (hypertension biomarkers), Alleviate (chronic pain data hub), and GRAIMATTER (TRE disclosure control guidelines). She chairs boards at Swansea University and European Bioinformatics Institute. Awards include the 2016 Farr Institute Future Leader. Her work addresses SDG 3 (Good Health) and 9 (Industry/Innovation). Recent publications emphasize TRE innovation, hypertension subtyping, and pandemic data infrastructure (e.g., CO-CONNECT for COVID-19).

Antonio Nicasoمشاهده پروفایل

مدرس

• Organized Crime
• Mafia Studies
• Calabrian 'Ndrangheta
• +۳ مورد دیگر

Professor Antonio Nicaso is a Lecturer at Queen's University's Department of Languages, Literatures and Cultures under the Faculty of Arts and Science. He also holds teaching positions at the Italian School of Middlebury College (USA), St. Jerome’s University (Waterloo, Canada), and advises institutions globally. His expertise focuses on organized crime, particularly the Calabrian 'Ndrangheta, with 30 authored books exploring mafia structures, rituals, and modern adaptations. He consults governments and law enforcement worldwide, sits on advisory boards including the Nathanson Centre on Transnational Human Rights, and leads Centro Scuola e Cultura in Toronto. His courses include LLCU 213 (Social History of Organized Crime in Canada) and LLCU 214 (Mafia Culture and Symbols). Research emphasizes criminal networks' cultural dimensions, digital-age transformations, and transnational operations. Recent work examines dark web criminal activities, cyber-enabled crime, and mafia integration into global systems. Notable contributions include analyzing mafia adaptation to technology, political complicity, and symbolic systems. His advisory roles highlight practical impact, while academic output spans criminology, sociology, and cultural studies.

Stefan Köhlerمشاهده پروفایل

استاد

• Human Memory
• Cognitive Neuroscience
• Human Brain Mapping
• +۳ مورد دیگر

Dr. Stefan Köhler is a Professor and Department Chair of Behavioral and Cognitive Neuroscience at Western University's Department of Psychology. He also serves as a Principle Investigator at the Western Institute for Neuroscience and holds affiliations with the Rotman Research Institute and Rotman Institute of Philosophy. His research focuses on the neural and cognitive mechanisms of human memory, particularly in aging, neurological, and psychiatric disorders. Dr. Köhler holds a Diplom (MSc) from the University of Bielefeld and a PhD from the University of Toronto (1995). He completed postdoctoral training at the Montreal Neurological Institute under Dr. Brenda Milner. His work employs techniques like fMRI, TMS, and behavioral experiments to study memory impairments and their neural substrates. Research Interests: The lab explores episodic memory's functional and anatomical organization, including interactions between memory systems and visual processing, neural correlates of novelty detection, and hippocampal contributions to pattern separation. Key themes include how brain regions differentiate memory types and support perceptual discrimination. Labs/Teams: Active in the Western Institute for Neuroscience and collaborates with the Rotman Research Institute. His lab integrates clinical neuroscience with advanced neuroimaging methods. Grants/Advising: Not explicitly listed, but his extensive publication record suggests sustained funding for studies on memory disorders and neuroimaging techniques. Advises graduate students in neuroscience and psychology programs.

• Signal Processing
• Antenna Engineering
• Radar Imaging
• +۵ مورد دیگر

Amir Masoud Molaei is a Research Fellow at the School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast. His research focuses on advanced radar imaging techniques, antenna engineering, and signal processing, with particular emphasis on near-field localization, MIMO systems, and computational imaging using metasurface antennas. He has collaborated extensively on projects involving millimeter-wave systems, sparse array configurations, and security applications. His work integrates theoretical models like Kirchhoff migration with experimental validation, emphasizing hardware-efficient designs and algorithmic improvements. Key themes include overcoming challenges in mutual coupling effects, optimizing dynamic antenna configurations, and enhancing 3D imaging resolution. He has explored applications ranging from medical imaging to security systems, with a strong focus on real-time processing and experimental validation. Notable contributions include pioneering studies on reconfigurable metasurface antennas for radar and computational imaging, as well as algorithmic advancements in direction-of-arrival estimation and mixed-field source localization. His research bridges electromagnetic theory, signal processing, and practical hardware implementations, often addressing trade-offs between computational efficiency and accuracy. Molaei has published over 40 papers since 2021, with a focus on IEEE journals and conferences. His work frequently addresses challenges in signal reconstruction, sparse sampling techniques, and the integration of machine learning with traditional signal processing methodologies.