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Ahmed ElazabView profile

Research Fellow

• Machine Learning
• Deep Learning
• Medical Image Analysis
• +7 more

Ahmed Elazab serves as an Associate Researcher at Shenzhen University's School of Biomedical Engineering since January 2021, following a Postdoctoral Research Fellowship at the same institution from January 2018 to April 2020. He holds a Ph.D. in Pattern Recognition and Intelligent Systems from the Shenzhen Institutes of Advanced Technology, University of Chinese Academy of Sciences (2017). Education Ph.D. in Pattern Recognition and Intelligent Systems, Shenzhen Institutes of Advanced Technology, University of Chinese Academy of Sciences, China (2017) Research Focus Dr. Elazab's work centers on machine learning and deep learning applications in biomedical contexts, with specialized expertise in medical image analysis , brain anatomy analysis , and computer-aided diagnosis . His research integrates computer vision, bioinformatics, and data science to develop AI-driven solutions for complex medical challenges including neurodegenerative diseases and infectious outbreaks. Publication Trends Analysis of his recent publications (2020-2023) reveals a concentrated focus on deep learning for medical diagnostics, particularly in Alzheimer's disease staging from MRI data and COVID-19 detection from X-ray images. His work consistently incorporates domain-specific knowledge into neural architectures, with growing emphasis on generative models for medical image segmentation and vaccine development. Cross-cutting themes include handling multi-modal biomedical data and addressing real-world clinical constraints. Scientific Recognition Active Academic Editor for PeerJ Computer Science with 1,600 contribution points Reviewer for prestigious international journals across computer science and biomedical domains Author/co-author of over 80 peer-reviewed publications Academic Engagement Dr. Elazab maintains significant scholarly involvement through editorial work at PeerJ Computer Science, where he has handled manuscripts on deep learning applications in medical imaging since 2020. His extensive publication record demonstrates consistent research productivity, though specific grant funding or student supervision details are not documented in available sources. He contributes to multiple subject areas including Artificial Intelligence, Bioinformatics, and Computational Biology. Research Environment As part of Shenzhen University's School of Biomedical Engineering, Dr. Elazab operates within a multidisciplinary research ecosystem focused on AI-driven medical solutions. His work intersects with ongoing initiatives in medical image computing and computational diagnostics, leveraging institutional resources for biomedical data analysis without specified laboratory affiliations.

Harry HochheiserView profile

Assistant Professor

• Human-Computer Interaction
• Information Visualization
• Bioinformatics
• +7 more

Harry Hochheiser is an Assistant Professor in the Department of Biomedical Informatics at the University of Pittsburgh School of Medicine, where he also serves as Associate Director of the Biomedical Informatics Training Program. His academic career spans multiple disciplines at the intersection of computing and healthcare, with a strong focus on human-computer interaction, bioinformatics, and medical informatics. Dr. Hochheiser's research interests include human-computer interaction, information visualization, bioinformatics, universal usability, security, privacy, and public policy implications of computing systems. His work particularly focuses on NIH-funded projects related to bioinformatics research portals, visualization for review of chart records, and tools for aiding the discovery of animal models of human diseases. His research demonstrates a commitment to developing technology that addresses real clinical needs while considering user experience and ethical implications. His publication record shows consistent scholarly output across multiple domains, with recent work emphasizing biomedical informatics, machine learning applications in healthcare, epidemiological modeling, and academic publishing practices. His research demonstrates a strong interdisciplinary approach that bridges computer science with clinical medicine and public health, with particular attention to human-centered design principles in healthcare technology. Editorial Board Member, PeerJ - the Journal of Life & Environmental Sciences Editorial Board Member, PeerJ Computer Science Over 2,900 points on PeerJ representing 2,800 editorial contributions and 105 reviews Dr. Hochheiser has made significant contributions across numerous subject areas including Science and Medical Education, Science Policy, Statistics, Human-Computer Interaction, Computational Science, Bioinformatics, and many others. His work has practical implications for healthcare technology development, particularly in creating more effective, user-friendly systems that address real clinical needs while considering privacy and security concerns.

Noura Al MoubayedView profile

Associate Professor

• Explainable Machine Learning
• Natural Language Processing
• Optimisation
• +6 more

Dr. Noura Al Moubayed serves as an Associate Professor in the Department of Computer Science at Durham University, specializing in Explainable Machine Learning, Natural Language Processing, and Optimisation for high-dimensional data challenges. Education PhD from Robert Gordon University Post-doctoral positions at University of Glasgow and Durham University Research Focus Her work develops machine learning solutions for healthcare, social signal processing, cyber-security, and Brain-Computer Interfaces, specifically targeting noisy and imbalanced datasets through advanced NLP and optimization techniques. Current projects integrate deep learning with explainability frameworks to enhance real-world applicability. Publication Trends Recent publications (2020-2022) demonstrate consistent focus on improving question answering systems via bilinear pooling and paraphrasing techniques, alongside novel deep learning approaches for probabilistic topic modeling. These works bridge computer vision, NLP, and multimodal learning to address data imbalance challenges across healthcare and social computing domains. Academic Service PeerJ Computer Science Editorial Board Member 335 authorship points and 30 reviewer points on PeerJ platform