جست‌وجوی استاد و پژوهشگر

• Semantic technologies
• Ontology engineering
• Internet of Things
• +۱۰ مورد دیگر

Professor Trina Myers serves as the Head of School for the School of Information Technology at Deakin University's Faculty of Science Engineering and Built Environment. With extensive experience in academia and research leadership, she plays a pivotal role in shaping IT education and research directions at Deakin. She is also an active member of the Australian Council of Deans of ICT (ACDICT), having served as its immediate past President. Her educational background includes: Doctor of Philosophy in Computer Science from James Cook University Master of Business Administration from James Cook University Master of Information Technology from James Cook University Professor Myers' research focuses on semantic technologies, ontology engineering, Internet of Things, knowledge management, natural language processing, and human-computer interaction . Her work emphasizes interdisciplinary collaboration, bridging technology with fields such as healthcare, marine science, environmental conservation, and business. She has pioneered approaches in academagogy (academic gamification) to enhance online learning engagement, particularly for adult learners. Her IoT research has significant applications in healthcare space optimization, environmental monitoring, and resource management. Her recent publications demonstrate a strong trajectory in applying AI and IoT technologies to solve real-world problems, particularly in healthcare, education, and resource optimization. There's a clear pattern of interdisciplinary work connecting computer science with healthcare, education, and environmental science. Her research increasingly focuses on human-centered technology design, especially for vulnerable populations like adolescents with autism spectrum disorder. Her notable achievements include: Fellow of the Australian Computer Society (2023) Australian Awards for University Teaching (AAUT) Teaching Award (2020) Women in IT Professional Leadership Award Finalist (2020) Asia-Pacific International Triple E Entrepreneurial Educator of the Year Award (1st runner-up, 2020) Australian Computer Society, National Digital Disruptor ICT Educator of the Year (2019) Professor Myers actively supervises doctoral students across diverse research areas including gamification in language learning, brain tumor analysis using deep learning, AI in higher education, AI for refugee resilience, data integrity in edge environments, and quantum-driven satellite networking. She has secured significant research funding, including a recent grant for "Indiginizing ICT Curriculum: A Starter Framework for the Community of Practice" through the Australian Council of Deans of ICT. Her teaching philosophy emphasizes active learning methodologies, Process Oriented Guided Inquiry Learning (POGIL), blended learning, and collective intelligence approaches.

• Bayesian Statistics
• Computational Statistics
• Categorical Data Analysis
• +۶ مورد دیگر

Ntzoufras Ioannis is a Professor in the Department of Statistics at the Athens University of Economics and Business (AUEB), School of Information Sciences and Technology, where he has served continuously since 2004 (promoted to Professor in 2015). Previously, he held teaching positions at the University of the Aegean (2000-2004) and completed military service (1999-2000). Education B.Sc. in Statistics and Insurance Science (1994) M.Sc. in Statistics with Application in Medicine, University of Southampton (1995, with distinction) Ph.D. in Statistics, Athens University of Economics and Business (1999) Research Focus His work centers on Bayesian and computational statistics , specializing in categorical data analysis, statistical modeling, and variable selection methodology. He develops sophisticated models for applications in medical research (clinical trials, risk estimation), psychometrics (latent variable models), and sports analytics (football/basketball modeling), with emphasis on computational efficiency and real-world implementation. Publication Trends Recent publications (2023-2025) reveal three dominant trends: (1) Advanced Bayesian variable selection methods for high-dimensional data, (2) Sports analytics applications in football (goal modeling, competitive balance) and basketball (in-play performance), and (3) Development of specialized R packages (ssifs, PEPBVS) for statistical computation. His work consistently bridges theoretical innovation with practical domain applications. Scientific Awards Lefkopouleion Prize for Greece's best statistics thesis (1999-2000) PROSE Award Honorable Mention for 'Bayesian Modeling Using WinBUGS' (2010) Academic Leadership He has supervised graduate students across AUEB's Statistics, Business Analytics, and Data Science programs, and taught postgraduate courses at the University of Athens (Biostatistics), University of the Aegean (Business Administration), and Italian institutions (University of Pavia, Universita Cattolica, University of Bicocca-Milan). As General Secretary of the Greek Statistical Institute (2006-2007), he advanced national statistical initiatives. Research Community He founded and maintains grstats (http://grstats.forumotion.net/), Greece's primary online statistics community, facilitating collaboration among 1,200+ statisticians and data scientists through forums, workshops, and resource sharing.

Marylyn D Ritchieمشاهده پروفایل

استاد

• Computational Genomics
• Bioinformatics
• Pharmacogenomics
• +۸ مورد دیگر

Marylyn D Ritchie, PhD, is the Edward Rose, M.D. and Elizabeth Kirk Rose, M.D. Professor at the Perelman School of Medicine, University of Pennsylvania. She concurrently serves as Director of the Institute for Biomedical Informatics, Vice President for Research Informatics for the University of Pennsylvania Health System, Director of the Division of Informatics in the Department of Biostatistics, Epidemiology, and Informatics, and Vice Dean of Artificial Intelligence and Computing. Education: BS in Biology, University of Pittsburgh at Johnstown, 1999 MS in Applied Statistics, Vanderbilt University, 2002 PhD in Statistical Genetics, Vanderbilt University, 2004 Research Interests Dr Ritchie’s work integrates computational genomics , bioinformatics , pharmacogenomics , and systems genomics to advance precision medicine. She develops statistical and machine-learning approaches to dissect epistasis , genetic epidemiology , and evolutionary computation in large-scale biobanks, with a special focus on cardiovascular disease and Alzheimer’s disease . Her group is also pioneering translational informatics methods that incorporate social determinants of health and fairness metrics into AI-driven clinical decision support. Publication Trends In 2025 alone, Dr Ritchie co-authored more than fifteen high-impact studies spanning vision-language models for 3D CT , multi-omics Alzheimer’s risk prediction , fairness in neuroimaging AI , ancestry-specific pharmacogenomics , and cloud-based polygenic risk score platforms . The collective work highlights a shift from single-omics discovery to integrative, equitable, and clinically actionable models across diverse ancestries. Awards & Honors While specific named awards were not detailed in the text, Dr Ritchie’s endowed professorship and multi-institutional leadership roles signify sustained recognition. Grants & Advising Dr Ritchie leads large NIH, foundation, and industry-funded initiatives that support interdisciplinary teams of postdocs, graduate students, and data scientists. Her lab actively mentors trainees from UPenn’s Cell and Molecular Biology and Genomics and Computational Biology graduate groups. Laboratories & Teams She directs the Ritchie Lab (ritchielab.org), which develops open-source visualization tools such as PhenoGram , PheWAS-View , and Synthesis-View for genome-wide and phenome-wide data exploration. The lab operates within the Institute for Biomedical Informatics and collaborates closely with the Penn Medicine BioBank and multiple clinical departments to translate big-data discoveries into precision medicine workflows.

• Pneumonia
• Clinical Decision-Making
• Medical Informatics
• +۳ مورد دیگر

Dr. Barbara E. Jones serves as an Associate Professor in the Department of Internal Medicine at the University of Utah School of Medicine, with dual appointments in Pulmonary and Critical Care Medicine. Her clinical practice spans diverse healthcare settings within the Veterans Affairs system and academic medical centers, focusing on evidence-based adaptation of care to varied patient populations. Her educational background includes: M.D. from University of Washington School of Medicine B.A. in Philosophy from Dartmouth College Master of Science in Clinical Investigation (M.S.C.I) from University of Utah Postdoctoral Fellowship in Pulmonary and Critical Care Medicine at University of Utah Residency in Internal Medicine at University of Utah Dr. Jones' research centers on decision-making processes in pneumonia diagnosis and treatment, employing a tripartite informatics approach combining population analytics, cognitive behavior analysis, and clinical decision support systems. Her work specifically targets reducing diagnostic uncertainty and treatment variation across healthcare systems, with emphasis on equitable care delivery for diverse patient populations. Current projects investigate diagnostic discordance in community-acquired pneumonia, electronic surveillance for hospital-acquired infections, and machine learning applications for diagnostic error detection. Analysis of her 15 most recent publications reveals consistent focus on pneumonia management systems, with emerging emphasis on pandemic impacts on diagnostic practices and AI-driven quality improvement. Her work predominantly utilizes large VA healthcare datasets spanning 100+ medical centers, featuring mixed-methods approaches that integrate quantitative analytics with qualitative clinician experience assessment. Dr. Jones actively contributes to clinical guideline development and medical education through editorial work in major journals including Chest and Annals of Internal Medicine , where she frequently addresses controversies in pneumonia diagnosis and antibiotic stewardship. Her research program operates at the intersection of the University of Utah Health system and the Veterans Affairs national healthcare network, leveraging electronic clinical decision support implementations across diverse hospital settings including rural and critical access facilities. Current initiatives focus on real-time feedback systems for diagnostic performance improvement and automated surveillance for healthcare-associated infections.

Alton Russellمشاهده پروفایل

استادیار

• Decision analysis
• Cost-effectiveness analysis
• Simulation modeling
• +۴ مورد دیگر

Alton Russell is an Assistant Professor at the Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine and Health Sciences, McGill University. He serves as an Affiliate Investigator at the Research Institute of the McGill University Health Centre (RI-MUHC) and is affiliated with the Quantitative Life Sciences program. His research focuses on data-driven decision modeling to optimize healthcare resource allocation through methods in decision analysis, simulation, health economics, and machine learning. PhD in Management Science and Engineering (2021), Stanford University MSc in Management Science and Engineering (2018), Stanford University BSc in Industrial Engineering (Health Systems concentration) and Interdisciplinary Studies (Global Health and Sustainability concentration) (2014), North Carolina State University Russell's research program develops advanced models for blood safety, pediatric kidney disease management, opioid crisis interventions, and infectious disease surveillance. His lab (D3Mod) integrates individual-level data with machine learning and Bayesian statistics to address heterogeneity in patient populations and policy impacts. His work emphasizes open science practices, with publications and code archived via DOIs. Current research themes include personalized donor risk assessment, emergency service optimization, and harmonization of serosurveillance data. Russell teaches advanced decision modeling (EPIB 676) and economic evaluation of health programs (PPHS 528) at McGill.

• Machine Learning
• Artificial Intelligence
• Medical Imaging
• +۴ مورد دیگر

Elisabeth Wetzer is an Associate Professor in Machine Learning at the Department of Physics and Technology, UiT The Arctic University of Norway. Her research bridges artificial intelligence with healthcare applications, focusing on multimodal image registration, bias mitigation in AI, and physics-informed learning models. Current Role: Associate Professor, Machine Learning Group Research Themes: AI ethics, medical imaging, cross-modal representations, algorithmic fairness Her recent work explores technical challenges in PET imaging analysis and societal implications of AI bias. Collaborative projects span medicine, mathematics, and computer science disciplines. Key scientific contributions include: Physics-informed deep learning for PET image data Studies on multi-task learning efficacy in medical classification Research on gender bias in algorithmic systems She actively participates in diversity initiatives and public outreach, including presentations at Nobel laureate conferences and media engagements on AI ethics.

Dustin D. Frenchمشاهده پروفایل

استاد

• Health Economics
• Health Services Research
• Health Policy
• +۴ مورد دیگر

Dustin D. French, PhD, serves as Professor in Ophthalmology and Medical Social Sciences (Determinants of Health) at Northwestern University's Feinberg School of Medicine. His dual appointments bridge clinical ophthalmology with population health research, focusing on healthcare system optimization through economic and policy analyses. Dr. French's educational foundation includes: PhD from The Ohio State University (2001) His research program centers on health economics and outcomes research , with signature expertise in: Comparative and cost-effectiveness methodologies Health informatics and big data applications Health services research and policy evaluation Addressing health disparities in diabetic eye care and rural communities Recent publications reveal a strategic focus on AI-driven clinical decision support and resource optimization, with 2025 studies examining glaucoma identification algorithms, nursing home quality metrics, microhematuria diagnostic pathways, and pediatric obesity interventions. Dr. French's scholarly impact is recognized through prestigious awards: International Society for Pharmacoepidemiology Outstanding Reviewer Award (2016, 2010) Department of Veterans Affairs Distinguished Service Award (2016) International Society for Pharmacoepidemiology Distinguished Article Award (2006) His leadership in health services research extends to mentorship and grant-funded initiatives targeting healthcare quality improvement, particularly in diabetic eye care access for minority populations. Institutional affiliations include the Center for Diabetes and Metabolism, IPHAM's Center for Health Services & Outcomes Research, and NUCATS, where he contributes to translational research infrastructure.

Elizabeth Mayer-Davisمشاهده پروفایل

استاد

• Diabetes Epidemiology
• Type 1 Diabetes
• Type 2 Diabetes
• +۶ مورد دیگر

Elizabeth "Beth" Mayer-Davis is the Dean of The Graduate School and the Cary C. Boshamer Distinguished Professor of Nutrition and Medicine at the University of North Carolina at Chapel Hill. She holds joint appointments in the Gillings School of Global Public Health and the School of Medicine, where she previously served as Chair of the Department of Nutrition for eight years. As Dean of The Graduate School, she oversees more than 160 degree-offering programs and over 10,000 graduate students at UNC Chapel Hill. Dr. Mayer-Davis earned her educational credentials from prestigious institutions: Ph.D. in Epidemiology from the University of California, Berkeley (1992) Master of Public Health with honors from the University of Colorado School of Medicine (1986) B.S. in Dietetics from the University of Tennessee (1980) Dr. Mayer-Davis is an internationally recognized expert in diabetes research with a career focused on the epidemiology and natural history of type 1 and type 2 diabetes in children and adults. Her research addresses how nutrition impacts diabetes risk and complications, with particular emphasis on culturally and regionally diverse populations. Her primary current focus is on type 1 diabetes in youth and young adults, examining diabetes self-management and energy balance/weight management for individuals with type 1 diabetes. She employs innovative methodologies including large epidemiological studies, clinical trials, and adaptive intervention designs, often incorporating communication technologies to enhance patient-centered care. Analysis of Dr. Mayer-Davis's recent publications reveals consistent focus on diabetes epidemiology across diverse populations, with particular attention to youth-onset diabetes, health disparities by race and ethnicity, and innovative interventions for diabetes management. Her work frequently addresses the intersection of obesity and diabetes, particularly in type 1 diabetes where this comorbidity has been historically understudied. The research demonstrates methodological sophistication through the use of advanced statistical techniques, continuous glucose monitoring data, and adaptive trial designs to address complex questions in diabetes care. Dr. Mayer-Davis has received numerous professional honors and appointments: Cary C. Boshamer Distinguished Professorship at UNC President for Health Care and Education for the American Diabetes Association (2011) Member of the 2020 Dietary Guidelines Advisory Committee Appointee to President Obama's Advisory Group on Prevention, Health Promotion and Integrative and Public Health Co-director of the Nutrition Obesity Research Center funded by NIH Throughout her career, Dr. Mayer-Davis has been deeply committed to mentoring the next generation of researchers, having supervised dozens of graduate students. She has secured over $45 million in research funding, including as Principal Investigator for the Carolina site of the landmark SEARCH for Diabetes in Youth study and the Nutrition for Precision Health Consortium initiative. Her leadership extends to national scientific committees and professional organizations, where she has shaped diabetes research and care standards. She is particularly dedicated to promoting diversity, equity, and inclusion in graduate education and research. Dr. Mayer-Davis co-directs the UNC Nutrition Obesity Research Center, a NIH-funded center that has received continued support through multiple funding cycles. She also leads the Carolina site of the SEARCH for Diabetes in Youth study, a large multi-center national study that has been tracking diabetes incidence and trends in youth for over two decades. Her research team includes epidemiologists, nutritionists, statisticians, and clinicians working collaboratively to address critical questions in diabetes prevention and management.

Caroline Fordمشاهده پروفایل

استاد

• Cancer Cell Biology
• Gynaecological Cancer
• Ovarian Cancer
• +۶ مورد دیگر

Professor Caroline Ford is a leading researcher at the University of New South Wales, serving within the School of Women's and Children's Health and the Adult Cancer Program at the Lowy Cancer Research Centre. She leads the Gynaecological Cancer Research Group (GCRG), which focuses on understanding the development, spread, and treatment of gynaecological cancers, with particular emphasis on ovarian and endometrial cancer. Established in 2010 after international postdoctoral fellowships at the University of Toronto and Lund University, her laboratory has maintained consistent competitive research funding for over 13 years. Her research spans Cancer Cell Biology with specific expertise in ovarian cancer, endometrial cancer, endometriosis, metastasis mechanisms, ascites biology, and cell-free DNA analysis. Major projects include developing an early detection test for ovarian cancer and identifying key targets for anti-metastatic therapies. The GCRG works closely with clinicians at the Royal Hospital for Women and conducts studies involving biospecimen collection from women with gynaecological cancers. Professor Ford has pioneered innovative in vitro organotypic models of ovarian and endometrial cancer for pre-clinical drug testing. Her publication record shows consistent focus on ROR1/ROR2 receptors, Wnt signaling pathways, and cell-free DNA applications in cancer diagnostics. Recent work has emphasized liquid biopsy techniques using ascites fluid, 3D cancer models, and computational approaches to predict treatment outcomes. She has published extensively on the molecular mechanisms of gynaecological cancers and their clinical applications. Superstar of STEM (2017) Women's Agenda Award for Female Leader in Science, Medicine & Health (2018) Professor Ford actively supervises PhD students including Kate Gunther and Bonnie Werner, and co-supervises Zoe Phan. She convenes UNSW's General Education course on Personalised Medicine and developed Australia's first MOOC on this subject. She serves as Chair of the ANZGOG Uterine Tumour Type Working Group and Research Chair of Cure Cancer Australia. Her community engagement includes founding the STEMMinist Book Club and leading the 'Ovaries. Talk About Them' campaign to raise awareness about ovarian cancer.

Damiano Piovesanمشاهده پروفایل

دانشیار

• Protein function prediction
• Intrinsically disordered proteins (IDPs)
• Machine learning in bioinformatics
• +۵ مورد دیگر

Damiano Piovesan is Associate Professor in Bioinformatics (SSD BIO/10) at the Department of Biomedical Sciences , University of Padua , Italy. Since March 2022 he has held this rank, having previously served as Assistant Professor (2022) and PostDoc researcher (2019) in the same department. Education 2013 – PhD in Biotechnology, Pharmacology and Toxicology, University of Bologna 2009 – MSc in Bioinformatics, University of Bologna 2007 – BSc in Biotechnology, University of Bologna Research Focus Piovesan’s research integrates machine-learning approaches with structural bioinformatics to advance understanding of intrinsically disordered proteins (IDPs) and protein function prediction . He develops widely used resources such as MobiDB for disorder annotation, DisProt for functional curation of disordered regions, and RING for residue interaction networks. Additional interests include tandem repeat proteins , cancer-related IDP targets , and community benchmarking initiatives (CAFA, CAID, CAGI). Publication Trends His 2024–2025 output is dominated by updates to flagship databases ( InterPro , DisProt , MobiDB ), next-generation disorder predictors leveraging deep learning ( PredIDR , MobiDB-lite 4.0 ), and large-scale genomics challenges ( CAGI6 ). Across the decade, recurring themes include methodological advances in disorder prediction, creation of interoperable bioinformatics platforms, and rigorous benchmarking to ensure community-wide reliability. Scientific Awards No specific awards are listed in the provided materials. Advising & Grants No individual students or grant details are explicitly supplied; however, his leadership in multi-institutional consortia (e.g., InterPro, DisProt, CAFA) implies substantial supervisory and funding coordination roles. Labs & Teams Piovesan is affiliated with the BioComputingUP Lab ( https://biocomputingup.it/ ) at the University of Padua, a hub for computational biology and bioinformatics tool development.

Xiaobo Liمشاهده پروفایل

استاد

• Neuroscience
• Medical Imaging
• Machine Learning Applications in Neurology
• +۳ مورد دیگر

Xiaobo Li is a Professor in the Department of Bio-Medical Engineering at New Jersey Institute of Technology. Holding a Ph.D. in Computer Aided Geometric Design from the University of Birmingham and a B.S. in Automation from Nanjing University of Aeronautics, their research bridges computational methods with neuroimaging and psychiatric disorder analysis. Ph.D., University of Birmingham (Computer Aided Geometric Design, 2004) B.S., Nanjing University of Aeronautics (Automation, 1999) Dr. Li’s work focuses on applying machine learning and graph theory to understand brain network abnormalities in conditions like ADHD , schizophrenia , and traumatic brain injury . Their studies analyze structural-functional connectivity , reward processing , and gut-brain axis interactions using fMRI , fNIRS , and diffusion tensor imaging . Recent publications highlight their development of tools like the GAT-FD MATLAB toolbox for brain network analysis and their exploration of multimodal MRI in schizophrenia diagnosis. They also investigate the neurobiological effects of photobiomodulation and vision therapy interventions.

Gabriella Casalinoمشاهده پروفایل

استادیار

• Computational Intelligence
• eHealth
• Data Stream Mining
• +۵ مورد دیگر

Gabriella Casalino is an Assistant Professor at the University of Bari Aldo Moro, Department of Computer Science, and a key researcher at CILAB - Computational Intelligence Lab. Her work focuses on Computational Intelligence methods for interpretable data analysis, particularly in eHealth, Data Stream Mining, and eXplainable Artificial Intelligence (XAI) within medical and educational domains. She has contributed to innovative approaches in smartphone-based health monitoring, fuzzy logic applications, and remote vital sign detection via photoplethysmography. Education : Ph.D. in Computer Science, with advanced training at institutions like Universitat de Girona and Université de Mons. Research Trends : Recent publications highlight applications of evolving granular computing, neuro-fuzzy systems, and explainable AI in hypertension prediction, bipolar disorder monitoring, and educational data analysis. Key subfields include remote health monitoring, medical data streams, and hybrid AI models. Grants : Research funded by AIRC (Italian Cancer Research Foundation), focusing on computational methods for healthcare challenges. Labs & Collaborations : Active in CILAB, collaborating on projects involving mHealth solutions, cardiovascular risk assessment, and intelligent educational systems.

Kanwarpal Singhمشاهده پروفایل

دانشیار

• Optical Coherence Tomography
• Hyperspectral Imaging
• Confocal Microscopy
• +۶ مورد دیگر

Dr. Kanwarpal Singh is an Associate Professor in the Department of Electrical & Computer Engineering at McMaster University. His research focuses on developing novel endoscopic devices using advanced optical imaging techniques, including optical coherence tomography (OCT), hyperspectral imaging, and multiphoton microscopy. His work aims to improve imaging of internal organs for medical applications, particularly in gastrointestinal and cardiovascular diagnostics. Dr. Singh leads a lab dedicated to advancing biomedical technologies, with a strong emphasis on device innovation and clinical translation. He teaches courses such as ECE 6BB3 (Cellular Bioelectricity) and IBEHS 4QZ3 (Modelling of Biological Systems). His research interests span biomaterials, health technology, and imaging systems. He has developed endoscopic devices for longitudinal monitoring of inflammatory bowel disease and coronary artery crystal detection. His publications emphasize optical imaging advancements, including motion-corrected microscopy and three-dimensional luminal reconstruction. Dr. Singh collaborates on projects involving tissue engineering, drug delivery, and material science. His work bridges engineering and medicine, with applications in cardiology, dermatology, and neurology. Dr. Singh’s lab also explores elastography and Brillouin microscopy for biomechanical property assessment. He has contributed to portable OCT systems and smartphone-based imaging platforms. While no formal awards are listed, his extensive publication record reflects significant contributions to biomedical imaging and device development. His research addresses clinical challenges through cutting-edge optical technologies, aiming to improve diagnostic accuracy and patient outcomes.

Syed Ahmar Shahمشاهده پروفایل

دانشیار

• Signal Processing
• Machine Learning
• Biomedical Engineering
• +۵ مورد دیگر

Syed Ahmar Shah is a Senior Research Fellow (Associate Professor) and the Director of Innovation at the Usher Institute within the College of Medicine and Veterinary Medicine at the University of Edinburgh. He holds a tenured academic position and leads the DIME group (Data-driven Innovation in MEdicine). His work bridges biomedical engineering, data science, and clinical medicine, with a focus on improving healthcare through technological innovation. Dr. Shah completed his educational journey with a BEng in Electronics Engineering from GIK Institute of Engineering Sciences and Technology in Pakistan, followed by an MSc and DPhil (PhD) in Biomedical Engineering and Biomedical Signal Processing and Machine Learning, respectively, from the University of Oxford. His academic credentials reflect his interdisciplinary expertise spanning engineering, data science, and medicine. His research interests center around the application of advanced data analytics to healthcare challenges. Specifically, he focuses on signal processing for time-series analysis and filtering, machine learning for classification, regression, and clustering tasks, and the development of digital health systems for chronic disease management. His work particularly targets chronic respiratory conditions like COPD and asthma, where he applies data mining techniques to electronic health records to identify patterns and develop predictive models. Dr. Shah's publication portfolio includes over 60 peer-reviewed articles in prestigious journals such as The Lancet, Brain, BMJ Open, Thorax, IEEE Transactions, JMIR, and JACI. His recent work demonstrates a strong trajectory in applying artificial intelligence to predict asthma attacks, analyze long COVID outcomes, and develop tools for personalized COPD care, particularly for women. His research often involves large-scale data analysis from national healthcare databases across the UK, Brazil, and Scotland, enabling cross-country comparisons of disease patterns and healthcare system responses. Florence Nightingale Award for Excellence in Healthcare Data Analytics (2023) As an active supervisor, Dr. Shah is open to PhD supervision enquiries and has contributed to training the next generation of researchers at the intersection of data science and healthcare. His DIME research group serves as a hub for innovative projects that combine engineering approaches with clinical medicine to address pressing healthcare challenges. Dr. Shah also engages with industry through data science consulting, offering expertise in developing intelligent algorithms for businesses with large datasets, particularly in healthcare but extending to other domains as well.

• Human-Computer Interaction
• Auditory & Speech-Based Interfaces
• Accessibility Technologies
• +۳ مورد دیگر

Ian Pitt is a Lecturer in Usability Engineering and Interactive Media at University College Cork (UCC). He leads the Interaction Design, E-Learning and Speech (IDEAS) Research Group, focusing on multimodal human-computer interaction, auditory interfaces, and accessibility solutions for visually impaired users. Pitt holds a D.Phil from the University of York, followed by research fellowships at Otto-von-Guericke University in Germany before joining UCC in 1997. His research interests include speech-based interfaces, e-learning systems, and accessibility technologies for blind users. Key projects include the EU-funded ENABLE Network (2011–2014) and prototype development for UniWink. He has secured significant grants, including €72,009 from IRCSET for voice analysis research and €19,478 from the EU for ICT-supported learning initiatives. Pitt has advised numerous PhD students, including Flaithri Neff (2011), Emma-Kate Crowley (2014), and current candidates Aine Kearns and Patrick Egan. His publications span journals like International Journal of Game-Based Learning and conferences such as ICCHP and ACM SIGACCESS. He has contributed to committees for conferences like CHI and the Irish HCI conference. Teaching modules include Usability Engineering, Human-Computer Interaction, and Digital Media Development. His work emphasizes inclusive design principles, with projects addressing navigation systems for blind students and adaptive e-learning frameworks. Recent research trends focus on ICT-delivered aphasia rehabilitation, emotional BCI interfaces, and multimodal learning systems. Collaborations include international partners through EU grants, reflecting his global impact in accessibility and educational technology.