جست‌وجوی استاد و پژوهشگر

• Medical Statistics
• Epidemiology
• Health Services Research
• +۱۲ مورد دیگر

Dr. Matthew Barclay is a Principal Research Fellow (Statistician) in Cancer Healthcare Epidemiology at University College London's Behavioural Science and Health department. With expertise spanning medical statistics, epidemiology, and health services research, his work focuses on analyzing cancer registry data, primary care electronic health records, and healthcare claims data to improve understanding of cancer diagnosis and treatment pathways. Dr. Barclay's educational background includes: PhD in "The design of composite indicators of healthcare quality: a multi-method analysis" from University of Cambridge (2021) MSc in Statistics with Medical Applications from University of Sheffield (2015) MMath in Mathematics from Durham University (2011) Dr. Barclay's research spans the entire cancer diagnostic and management pathway, with particular focus on risk of cancer in primary care patients, cancer characteristics at diagnosis (including socio-demographic variations in staging), treatment patterns, and short-term outcomes. His methodological expertise lies in applied statistics, particularly in cohort design within electronic health record datasets and developing consistent data resources for research. His work frequently employs cancer registry data, primary care records, and healthcare claims to address critical questions in cancer epidemiology and healthcare quality. Analysis of Dr. Barclay's recent publications reveals a strong focus on cancer diagnosis pathways, particularly how symptoms present in primary care lead to cancer diagnosis. His work spans multiple cancer types but has particular emphasis on lung, colorectal, and breast cancers. Methodologically, his research combines epidemiological approaches with advanced statistical techniques applied to large-scale datasets including UK Biobank, cancer registries, and primary care records. International comparative studies through the International Cancer Benchmarking Partnership represent another significant strand of his recent work. Dr. Barclay advises postgraduate and PhD students on topics related to his research interests in cancer epidemiology and health services research. His collaborative network spans multiple institutions and countries, reflecting the international nature of his research on cancer diagnosis and treatment pathways.

• Biomedical Sciences
• Translational Research
• Tuberculosis Diagnostics
• +۵ مورد دیگر

Colleen Bannon Bushell serves as a Research Associate Professor in Biomedical and Translational Sciences at the University of Illinois. She also holds the position of Director of the Healthcare Innovation Program Office at the National Center for Supercomputing Applications (NCSA) and is an Affiliate at the Carl R. Woese Institute for Genomic Biology, including the Personalized Nutrition Initiative. Dr. Bushell's research spans multiple interdisciplinary domains at the intersection of biomedical science, data analytics, and clinical applications. Her work focuses on developing novel diagnostic approaches and biomarkers for various conditions including tuberculosis, cancer, and microbiome-related disorders. She applies advanced computational methods to analyze complex biological data, with particular expertise in cytokine profiling, label-free imaging techniques, and microbiome analysis. Her publication record demonstrates a strong focus on translating basic science discoveries into clinical applications. Recent work shows expertise in tuberculosis diagnostics using multiplexed cytokine profiling, cancer research using patient-derived xenograft models, and nutritional biomarker development through microbiome analysis. Her research bridges computational approaches with biomedical applications, particularly in the areas of infectious disease, oncology, and nutritional science. Dr. Bushell collaborates extensively across disciplines, working with researchers in statistics, computer science, microbiology, and clinical medicine. Her work has received attention across multiple platforms, with mentions in news outlets, blog posts, and patent applications related to her biomarker research. She directs the Healthcare Innovation Program Office at NCSA, indicating leadership in applying computational resources to healthcare challenges. Her affiliation with the Personalized Nutrition Initiative suggests involvement in research connecting dietary patterns, microbiome composition, and health outcomes.

• Intelligent Control Systems
• Fault and Anomaly Detection
• Virtual and Robust Sensors
• +۵ مورد دیگر

Dr. José Luis Calvo Rolle serves as a Professor in the Department of Industrial Engineering at the School of Engineering, Universidade da Coruña (UDC), specializing in Systems Engineering and Automation. His research focuses on intelligent control systems, fault detection, and virtual instrumentation within the Cybernetic Science and Technology Research Group. Teaches across multiple programs including Master's in Industrial Computing and Robotics, Textile Technology, and Occupational Risk Prevention Coordinates thesis supervision across Industrial Engineering and related disciplines His research spans intelligent control systems and optimization, with significant contributions in virtual sensors, fault detection, and AI-driven modeling for industrial applications. Current projects integrate machine learning with industrial processes for naval construction, wastewater treatment, and precision livestock farming, demonstrating cross-disciplinary impact from energy systems to agricultural technology. Recent publications reveal strong trends in applying deep learning to industrial metaverse frameworks, wastewater optimization, and livestock monitoring systems. His work bridges theoretical control engineering with practical implementations in energy management, naval manufacturing, and sustainable agriculture, frequently utilizing dimensionality reduction and one-class classification techniques. Dr. Calvo Rolle actively mentors students through thesis supervision across multiple engineering disciplines and coordinates research projects with diverse funding sources including the European Commission, Spanish National Research Agency, and industrial partners like Navantia and Telefónica. His laboratory work centers on the Cybernetic Science and Technology Research Group, developing testbeds for industrial automation, virtual instrumentation, and AI-driven monitoring systems. Current initiatives include digital twin implementations for naval manufacturing and smart energy management systems.

William E. Janesمشاهده پروفایل

استادیار

• Assistive Technology
• Rehabilitation Engineering
• Amyotrophic Lateral Sclerosis (ALS)
• +۴ مورد دیگر

William E. Janes is an Assistant Professor and Academic Fieldwork Coordinator in the Department of Occupational Therapy at the University of Missouri . Holding a PhD in Health and Rehabilitation Science, his work focuses on creating accessible assistive technologies through the AT-AT Lab to enhance independence for individuals with disabilities. Occupational Therapy Doctorate (OTD) – Washington University School of Medicine Master of Science in Clinical Investigation (MSCI) – Washington University School of Medicine Bachelor of Science in Psychology and Sociology – Southern Illinois University – Edwardsville His research spans ALS intervention , pediatric handwriting assessment , and 3D-printed assistive technologies , with recent publications addressing technology-driven solutions for neurodegenerative conditions and innovative educational practices. Janes has secured significant grants including a $760,000 award to advance ALS research and a $400,000 ALS Association grant for caregiver alert systems. Recognized as a 2025 AOTA Fellow , he leads initiatives in fieldwork educator competency and has contributed to professional standards through the University of Missouri Faculty Scholars Program (2017–2018). His teaching includes courses in Bioengineering Design and Evidence-Based Practice , while maintaining active memberships in the American Occupational Therapy Association and RESNA . As director of the AT-AT Lab , Janes champions low-cost technology adaptation , including recent projects making gaming more accessible for users with disabilities. His work bridges clinical practice , educational innovation , and community-focused technological development .

Emmanuel Morinمشاهده پروفایل

استاد

• Natural Language Processing
• Computational Linguistics
• Bilingual Terminology Extraction
• +۵ مورد دیگر

Emmanuel Morin is a Full Professor in Computer Science at the University of Nantes, France. He is affiliated with the Computer Science Department of Nantes Institute of Technology (IUT) and leads the Natural Language Processing (TALN) team at the Digital Sciences Laboratory of Nantes (LS2N UMR CNRS 6004). His research focuses on computational linguistics with particular emphasis on multilingualism and multimodality. His academic leadership includes serving as Head of the NLP team (2017-present), Co-responsible for the ATAL (Machine Learning and Natural Language Processing) option of the Computer Science Master (2017-present), and Director of the Nantes computer science training department (2014-present). He also co-edits the Traitement Automatique des Langues (TAL) journal and serves on the steering committee of ATALA since 2004. Current projects: Atlantic 2020, ALALA Project (2019-present), ANR ADDICTE (2017-present), and Atlanstic 2020 RAPACE Project (2016-present) Past projects: Labex CominLabs LIMAH (2014-2019), ANR CRISTAL (2012-2016), and European FP7-ICT TTC project (2010-2012) Professor Morin's research spans natural language processing with a focus on bilingual terminology extraction and comparable corpora analysis. His recent work (2020-2022) shows strategic expansion into medical informatics applications like FrenchMedMCQA, while maintaining his foundational work on domain adaptation of language models using graph-based networks. His publications demonstrate a consistent trajectory from early terminology extraction methods to contemporary applications in specialized language processing. He has supervised numerous PhD students, with current advisees including Kévin Espasa, Martin Laville, Merieme Bouhandi, and Antoine Caubrière. His past students have made significant contributions to the field of natural language processing, continuing his research legacy in academia and industry.

Nick Bassiliadesمشاهده پروفایل

استاد

• Semantic Web
• Ontologies
• Linked Open Data
• +۷ مورد دیگر

Nick Bassiliades is a Professor at the School of Informatics , Aristotle University of Thessaloniki , Greece. His academic roles include serving as President of the Digital Governance Committee and the Digital Transformation of Greek Universities Committee, as well as Director of the Web, Data, and Knowledge Engineering Sector. Education: B.Sc. in Physics, Aristotle University of Thessaloniki (1991) M.Sc. in Applied Artificial Intelligence, University of Aberdeen (1992) Ph.D. in Parallel Knowledge Base Systems, Aristotle University of Thessaloniki (1998) His research focuses on Semantic Web , Ontologies , Knowledge Graphs , and applications in Artificial Intelligence , eGovernment , and Intelligent Agents . Recent publications emphasize ontological frameworks for requirements engineering, explainable AI, and electric vehicle knowledge graphs. He actively contributes to scientific communities as a Senior Member of IEEE and ACM , and serves as Co-Editor-in-Chief for the International Journal of Artificial Intelligence in Business and Management . His work involves collaborations with the Intelligent Systems laboratory and projects like XR4DRAMA for disaster management.

Sinead O'Keeffeمشاهده پروفایل

پژوهشگر ارشد

• Radiation Dosimetry
• Optical Fiber Sensors
• Brachytherapy
• +۴ مورد دیگر

Sinead O'Keeffe is a Research Fellow at the University of Limerick in the Faculty of Science and Engineering , specifically within the Department of Electronic and Computer Engineering . Her research bridges the technical domain of optical fiber sensor development with critical applications in radiation therapy and sports medicine. Primary Research Themes Medical radiation dosimetry using optical fiber sensors Brachytherapy dose monitoring systems Sports injury prevention in Gaelic football and running Mental health literacy in rural farming communities Key Technical Contributions Development of scintillation-based dosimeters Characterization of perfluorinated polymer fibers 3D printed sensor systems for clinical and rehabilitation applications Interdisciplinary Applications Prostate cancer radiotherapy dose measurement Mental health intervention programs for athletes Work-family conflict analysis in Irish farming Email: sinead.okeeffe@ul.ie

Bashir Kazimiمشاهده پروفایل

پژوهشگر

• Deep Learning
• Computer Vision
• Materials Science
• +۳ مورد دیگر

Dr.-Ing. Bashir Kazimi is a group leader at the Materials Data Science and Informatics (IAS-9) department within the Institute for Advanced Simulation at Forschungszentrum Jülich. His work focuses on advancing deep learning and computer vision techniques for electron microscopy data analysis, enabling efficient material characterization. Expertise: Deep Learning, Computer Vision, Image Analysis Collaboration: Works closely with the Ernst-Ruska-Center (ER-C) for electron microscopy expertise Research Interests: Bashir develops and applies deep learning methods for tasks such as denoising, super-resolution, semantic segmentation, and tracking in electron microscopy. His applications span nanomaterial characterization, crystallographic defect identification, and orientation mapping. Scientific Trends: His recent publications highlight advancements in self-supervised learning, semantic segmentation of TEM images, and applications of deep learning to both materials science and archaeological monument detection in geospatial data. Scientific Achievement: Admitted to the Young Excellent Scientist Program (YESP) in 2024, supporting leadership development and scientific visibility Advising: Supervises Shrindhi Bhat , a PhD student in his group. He is involved in projects like FAST-EMI (Deep-learning assisted fast in situ 4D electron microscope imaging), with a focus on enhancing materials analysis through AI.

Morteza Zihayatمشاهده پروفایل

دانشیار

• Data Analytics
• Human-centered AI
• Decentralized AI
• +۶ مورد دیگر

Morteza Zihayat is an Associate Professor and Canada Research Chair (Tier 2) in Human-Centered Artificial Intelligence at Toronto Metropolitan University. He holds dual appointments in the Faculty of Engineering and Architectural Science (Department of Electrical, Computer, and Biomedical Engineering) and the Ted Rogers School of Management. Additionally, he serves as an Adjunct Professor at the University of Waterloo in Management Sciences and is a Faculty Fellow at IBM's Centre for Advanced Studies. Dr. Zihayat's educational background includes: PhD in Computer Science from York University (2016) MSc in Computer Engineering from University of Tehran (2011) Postdoctoral Research Fellowship at University of Toronto's Faculty of Information (2017) His research lies at the intersection of AI, security, and society with a focus on building fair and transparent AI systems. Dr. Zihayat's expertise spans human-centered AI, fair information retrieval systems, and blockchain-enabled AI infrastructures. His work emphasizes creating AI systems that are accountable and designed to serve the public good, with applications in healthcare, digital media, and social networks. Dr. Zihayat has received numerous accolades including the Canada Research Chair (Tier 2) in Human-Centered AI (2024), Dean's Outstanding Scholarly, Research, and Creative Activity Award (2023), Best Short Paper Award at ECIR (2023), and IBM CAS Faculty Fellowship (2021). His research has attracted over $1.7 million in external funding from agencies such as NSERC, Mitacs, and multiple industry partners including Toronto Transit Commission, The Globe and Mail, AT&T, and IBM. Dr. Zihayat serves as Associate Editor of the Computational Intelligence Journal and is an active reviewer for top-tier venues. He is also Co-director and Co-founder of the Digital Enterprise Analytics and Leadership (DEAL) Research Center.

Jignesh M. Patelمشاهده پروفایل

استاد

• Database Systems
• Query Optimization
• Data Analytics
• +۳ مورد دیگر

Jignesh M. Patel is a Professor in the Computer Science Department at Carnegie Mellon University, where he leads research on efficient data analysis methods. His work focuses on improving both system efficiency (e.g., high-performance data algorithms) and human efficiency (e.g., user productivity with data systems). Research Focus: Patel's group specializes in database systems, query optimization, hardware acceleration, and human-data interaction. Their interdisciplinary work spans: Transactional processing and real-time analytics Query optimization techniques Hardware-algorithm co-design Natural language interfaces for data systems Memory-efficient data processing Professional Activities: Co-founded four technology companies (Paradise, Locomatix, Quickstep, DataChat). Serves on program committees for premier conferences including SIGMOD and CIDR (as co-chair). Teaches database systems courses at CMU. Awards: Received Best Paper Award at DaMoN 2010 for work on cluster efficiency.

Gary D. Wuمشاهده پروفایل

استاد

• Gastroenterology
• Gut Microbiome
• Host-Microbial Interactions
• +۴ مورد دیگر

Dr. Gary D. Wu is the Ferdinand G. Weisbrod Professor in Gastroenterology at the University of Pennsylvania’s Perelman School of Medicine, where he serves as Co-Vice Chief for Research in the Department of Medicine and Director of the Penn Center for Nutritional Science and Medicine. He co-leads the Penn-CHOP Microbiome Program and directs multiple NIH-supported core facilities, including the Human-Microbial Analytic and Repository Core (H-MARC) and the Microbial Culture and Metabolomics Core. His research focuses on the interplay between diet, gut microbiota, and host metabolism, with contributions to high-impact journals in microbiome science and clinical gastroenterology. Education : A.B. in Chemistry (Cornell University, 1980); M.D. (Northwestern University Medical School, 1986); Honorary M.A. (University of Pennsylvania, 2002) Research Interests : As a physician-scientist, Dr. Wu investigates how dietary components shape gut microbiome composition and functionality, leading to metabolic and physiological consequences in the host. His work spans translational studies, computational biology, and molecular microbiology, emphasizing microbial metabolism, pathobiont dynamics, and microbiome-driven disease mechanisms. Current projects include dietary interventions for infection resistance and microbiome-based diagnostics for chronic diseases. Publication Trends : Recent articles highlight his expertise in gut microbiome ecology, dietary modulation of pathogens (e.g., Clostridioides difficile ), and host-microbe co-metabolism. Key subfields include nitrogen assimilation, metal availability in early-life microbiomes, and microbiome associations with systemic infections and kidney disease. Scientific Awards : Elected Member of the American Society for Clinical Investigation and the Association of American Physicians; Honorary M.A. from the University of Pennsylvania Labs & Teams : Dr. Wu directs multiple NIH-funded core facilities and co-leads the Penn-CHOP Microbiome Program, fostering collaborations between Perelman School of Medicine and Children’s Hospital of Philadelphia. His lab investigates microbiome-driven pathophysiology, with personnel specializing in molecular microbiology, computational biology, and clinical translational research.

• Health Data Science
• Artificial Intelligence in Healthcare
• Cancer Epidemiology
• +۵ مورد دیگر

Professor Lesley Anderson holds a Personal Chair in Health Data Science at the University of Aberdeen where she serves as Interdisciplinary Institute Director for Health, Nutrition & Wellbeing. She maintains a part-time role as Deputy Director of the Northern Ireland Cancer Registry. Her academic credentials include a PhD in Public Health (Queen's University Belfast, 2004), MPHe from University of Manchester (2006), BSc (Hons) in Biomedical Science (QUB, 2001), and PGCHET (QUB, 2009). Her research spans artificial intelligence evaluation in healthcare, cancer epidemiology, health data science, and global health initiatives. She established international collaborations across 5 continents and led the £25M Industrial Centre for AI in Digital Diagnostics (iCAIRD). Current work focuses on AI validation for medical imaging, myeloproliferative neoplasms etiology, and cancer detection in low-resource settings. Publications demonstrate strong emphasis on: 1) AI/ML clinical translation (orthopedics, oncology), 2) Cancer biomarker discovery (liquid biopsy, HPV), 3) Population health analytics (cancer epidemiology, risk factors), and 4) Healthcare pathway optimization. Recent works show increased focus on AI implementation frameworks and multimodal data integration. Major Awards: Data Driven Innovation Awards (2024-2025) Multiparty Collaboration Award (2022) STAR Recognition at QUB (2019-2020) BUPA Foundation Epidemiology Award (2005) Dr J.D. Williamson Prize (2007) She currently supervises 5 PhD students in health data science/AI topics and has mentored 15+ doctoral candidates. Secured £6M+ in active grants including NIHR funding for early cancer detection technology (EDITH project) and Blood Cancer UK support for MPN genomics research. Leads the UK-wide MOSAICC study on myeloproliferative neoplasms etiology. Directs research teams within the Aberdeen Centre for Health Data Science and maintains labs focused on AI validation and cancer epidemiology. Collaborates with NHS, industry partners (Kheiron Medical), and global research consortia including BEACON and OPTIMA.

John Kurhanewiczمشاهده پروفایل

استاد

• Magnetic Resonance Imaging (MRI)
• Cancer Imaging
• Molecular Imaging
• +۳ مورد دیگر

John Kurhanewicz, PhD is a Professor in Residence at the University of California, San Francisco (UCSF), holding joint appointments in Radiology and Biomedical Imaging, Urology, and Pharmaceutical Chemistry. He is a faculty member in the UCSF-UCB Bioengineering Graduate Group and directs the UCSF Body Research Interest Group, the Biomedical NMR Lab, and the Kurhanewicz Laboratory. His research focuses on advanced MRI techniques for prostate cancer diagnosis and therapy monitoring, particularly through hyperpolarized 13C metabolic imaging. He has led numerous NIH-funded projects and clinical trials, contributing to over 230 peer-reviewed publications and 19 book chapters. Education: B.S. in Chemistry from University of South Florida/New College (1982), Ph.D. in Physical Organic Chemistry from University of South Florida, Tampa (1987), and postdoctoral training in MRI/MRS at UCSF (1990). Research Interests: Prostate cancer imaging, molecular imaging development, hyperpolarized MRI technology, and translational applications of metabolic imaging. His work aims to improve diagnostic accuracy and personalized treatment approaches for prostate cancer patients through advanced imaging modalities. Key Achievements: Developed the multiparametric 1H MRI prostate cancer staging exam used at UCSF, led the first clinical trial of hyperpolarized 13C MRI, and mentored over 120 students and junior faculty. Awards include Phi Beta Kappa and the Outstanding Chemistry Alumnus award from USF. Grants & Funding: Principal Investigator on multiple NIH grants totaling decades of support, including projects on hyperpolarized MRI clinical translation and co-clinical imaging metadata standards. Labs/Teams: Directs the Biomedical NMR Lab and Kurhanewicz Lab, focusing on cutting-edge MRI and molecular imaging technologies. Collaborates extensively with oncology and engineering teams to advance imaging biomarkers.

Mohammad T. Khasawnehمشاهده پروفایل

استاد

• Healthcare Systems Engineering
• Operations Management
• Data Science
• +۳ مورد دیگر

Mohammad T. Khasawneh is a SUNY Distinguished Professor and Director of the School of Systems Science and Industrial Engineering at Binghamton University. He leads the Watson Institute for Systems Excellence (WISE) and the Healthcare Systems Engineering Center. His roles include Director of the Manhattan Graduate Program in Health Systems. Education: BS and MS in Mechanical Engineering from Jordan University of Science and Technology (1998, 2000), PhD in Industrial Engineering from Clemson University (2003). Research focuses on healthcare systems engineering, operations management, and data science. His work optimizes healthcare systems for improved patient outcomes and cost efficiency. Key areas include predictive analytics, hospital resource utilization, and clinical performance improvement. He has generated over $15M in external funding and led projects with U.S. hospital systems. Notable achievements include developing the Executive Master of Science in Health Systems and an MS in Healthcare Systems Engineering. His research has produced 60+ journal articles and 120+ conference papers. Awards include SUNY Chancellor’s Awards for Teaching (2011) and Scholarship (2021), University Awards for Graduate Director (2015) and International Education (2016). He is an IISE Fellow and holds honorary visiting professorships at Hebei University of Technology (China) and Vellore Institute of Technology (India). Grants and funding: $2.5-3M annually via WISE, $39M in software/equipment grants. Lab/Initiatives: Healthcare Systems Engineering Center, WISE, and multiple hospital partnerships.

• Data Analytics
• Healthcare Systems
• Predictive Modeling
• +۳ مورد دیگر

Dr. Nasibeh Azadeh-Fard is an Assistant Professor in the Department of Industrial and Systems Engineering at Rochester Institute of Technology (RIT), part of the Kate Gleason College of Engineering. She directs the Healthcare Analytics Lab, focusing on applying advanced analytics to healthcare data for decision-making. Her expertise includes data analytics, healthcare systems engineering, and risk analysis. Dr. Fard holds a B.S. in Information Technology Engineering from Iran University of Science and Technology, and M.S. and Ph.D. in Industrial and Systems Engineering from Virginia Tech. She has served as a visiting professor at RIT and a postdoctoral fellow at Clemson University’s Risk Engineering and System Analytics Center, collaborating with AIG on risk mitigation. Her research emphasizes predictive modeling, hospital readmission prediction, and machine learning applications in healthcare. Notable projects include analyzing sepsis risk, COPD patient readmissions, and stroke rehabilitation using wearable sensors. She has secured a $21,000 FEAD grant for AI-driven readmission prediction. Dr. Fard advises several PhD students, including Behrad Barghi, Chiara Duenas, and Marzieh Amiri-Shahbazi, who have contributed to peer-reviewed publications and academic milestones. The Healthcare Analytics Lab collaborates with institutions like Rochester Regional Health and UR Medicine to translate research into practical solutions. Dr. Fard teaches courses on data analytics, predictive modeling, decision analysis, and forecasting methods, emphasizing real-world engineering applications. Her work bridges academia and healthcare practice, aiming to enhance patient outcomes through data-driven strategies.