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Jonathan CookView profile

Research Fellow

• Malaria Eradication
• Emerging Infectious Diseases
• Structure-Based Vaccine Design
• +3 more

Dr Jonathan Cook is a Research Fellow at Jesus College, University of Oxford, affiliated with the Medical Sciences division. He holds a primary medical qualification from Queen’s University (Canada, 2021) and a PhD investigating viral immune evasion mechanisms under Prof Jeffrey Lee. His work was supported by the Vanier Canada Graduate Scholarship and recognized with the Stuart Alan Hoffman Memorial Prize. As an incoming Banting Fellow at the Jenner Institute, he focuses on vaccine design for infectious diseases. His research interests include malaria eradication, emerging pathogens, and structure-based vaccine strategies. **Education**: - PhD (Investigation of viral entry glycoproteins), University of Toronto, supervised by Prof Jeffrey Lee - Primary Medical Qualification, Queen’s University, Canada **Research Interests**: - Malaria Eradication : Developing vaccines targeting Plasmodium species. - Emerging Infectious Diseases : Designing rapid-response strategies for pathogens like SARS-CoV-2. - Structure-Based Vaccine Design : Leveraging protein structural data to enhance immunogenicity. **Awards**: - Vanier Canada Graduate Scholarships Programme (2017–2020) - Stuart Alan Hoffman Memorial Prize (2021) - Banting Fellowship (incoming) **Grants/Advising**: Works on Jenner Institute-funded projects. No listed advisees yet. **Labs/Teams**: Collaborates with Prof Lee's lab at the Jenner Institute and the University of Toronto's Medical Microbiology program.

• Neuroimaging
• Structural brain segmentation
• Multimodal population modeling
• +6 more

Mark Jenkinson is a Professor of NeuroImaging at the University of Oxford's Nuffield Department of Clinical Neurosciences and also holds positions at the University of Adelaide's Australian Institute for Machine Learning and the South Australian Health and Medical Research Institute (SAHMRI). He heads the Structural Modelling and Analysis Group at the FMRIB Centre, where his research focuses on multimodal population modeling and structural brain segmentation. Education: DPhil in Robotics Research (University of Oxford, 1999) BSc (Hons I) in Mathematical Physics (University of Adelaide, 1994) BE (Hons I) in Electrical and Electronic Engineering (University of Adelaide, 1993) Professor Jenkinson's research spans two major themes: multimodal modeling of populations to describe disease processes and apply to individual patient diagnoses, and structural segmentation and analysis of brain anatomy and pathology, particularly focusing on sub-cortical structures and lesions. His work integrates advanced computational methods with neuroimaging to develop tools for understanding neurological disorders. As the developer of key components of the FMRIB Software Library (FSL), he has significantly contributed to standard neuroimaging analysis pipelines used worldwide. His recent publications demonstrate a strong focus on deep learning applications in neuroimaging, uncertainty quantification in medical AI, and advanced segmentation techniques. There's a clear trend toward developing more robust, anatomically plausible models that preserve topological structures while improving diagnostic capabilities for conditions like multiple sclerosis, Huntington's, and Parkinson's diseases. Scientific Awards: Highly Cited Researcher (Clarivate Analytics 2018-2021, Thomson Reuters 2014-2016) ISMRM Outstanding Teacher Award (2009, 2014) Teaching Excellence Award, University of Oxford (2012) David Phillips Fellowship from BBSRC (2005-2010) Professor Jenkinson has supervised over 25 doctoral students whose work spans brain segmentation, connectivity analysis, and clinical applications of neuroimaging. His research is supported by significant grants including the Medical Research Future Fund (AU$2m), Wellcome Trust Centre for Integrative Neuroimaging (£11m), and NIH Human Connectome Project (US$30m), reflecting the high impact and translational potential of his work. As head of the Structural Modelling and Analysis Group at FMRIB, Jenkinson leads a team developing the FSL (FMRIB Software Library), one of the most widely used neuroimaging analysis packages globally. His group collaborates extensively with clinical researchers on applications ranging from multiple sclerosis to traumatic brain injury, translating computational advances into clinical practice.

• Medical Imaging
• Breast Cancer
• Diagnostic Imaging
• +8 more

Associate Professor Ernest Ekpo is affiliated with the University of Sydney's Sydney School of Health Sciences, within the Discipline of Medical Imaging Sciences. He holds academic roles as a Scholarly Teaching Fellow and Co-Director of the Medical Image Optimisation and Perception Group (MIOPeG). He is also an Associate Editor of the Journal of Medical Imaging and Radiation Sciences and a member of the Sydney Southeast Asia Centre and Cancer Research Network. Education: Earned BSc (Hons) in Radiography/Sonography from the University of Calabar, Nigeria, and a PhD from the University of Sydney. His research focuses on breast density, cancer biomarkers, medical image perception, radiation dose optimization, and radiology education. He explores applications in low-resource settings and emerging technologies like AI for diagnostic accuracy. Research interests include breast cancer treatment outcomes, image-based phenotypes, and improving imaging pathways for acute abdominal pain. Key themes are Cancer and Medical Imaging, with an emphasis on Women's Health and Chronic Disease. His work combines clinical practice, public health strategies, and technological advancements to enhance diagnostic efficacy and reduce unnecessary procedures. Articles from 2021–2024 highlight his contributions in mammography optimization, radiographer training, and AI-driven diagnostic tools. Recent grants include a 2024 project on CT examination education and a 2023 initiative to streamline breast cancer screening pathways. His awards recognize peer review excellence, early-career teaching, and research leadership. He supervises multiple students investigating breast density biomarkers, imaging modalities for dense breasts, and radiation dose management. His educational efforts aim to improve radiographers' capabilities in identifying urgent findings across CT and X-ray modalities, leveraging blended learning approaches.

• Wound Healing & Tissue Repair
• Pressure Ulcer Prevention
• Nursing & Midwifery Education
• +4 more

Prof. Zena Moore is a renowned academic and clinician serving as Professor and Head of the School of Nursing & Midwifery at RCSI, University of Medicine and Health Sciences. She holds adjunct professorships at Curtin University (Australia), Griffith University (Australia), Cardiff University (UK), Ghent University (Belgium), and Fakeeh College for Medical Sciences (Saudi Arabia). Her research focuses on wound healing, pressure ulcer prevention, and nursing education, with over 300 publications. She leads the Skin Wounds and Trauma (SWaT) Research Centre and chairs multiple international bodies including the European Pressure Ulcer Advisory Panel. Education: PhD in Wound Healing (RCSI), MSc in Leadership in Health Education (RCSI), MSc in Wound Healing (University of Wales), FFNMRCSI, and Diplomas in Management and Nursing. Research Interests: Wound pathophysiology, pressure ulcer risk assessment, technologies for early detection, and healthcare equity. Awards: 2022 Lifetime Achievement Award from World Union of Wound Healing Societies. Her work spans over 50 funded projects, including grants from Science Foundation Ireland and the National Health and Medical Research Council. She has supervised numerous research projects on topics like pressure ulcer prevention algorithms and eHealth interventions.

• Magnetic Resonance Imaging (MRI)
• Functional MRI (fMRI)
• Biomedical Imaging Physics
• +6 more

Dr. Gary Glover is a Professor of Radiology (Radiological Sciences Lab) at Stanford University , with courtesy appointments in Psychology and Electrical Engineering. His work focuses on the physics and mathematics of MRI, particularly rapid scanning methods using spiral k-space trajectories for functional brain imaging and multimodal neuroimaging (fMRI/EEG/fPET/fNIRS) combined with neuromodulation techniques like TMS and transcranial ultrasound. Academic Appointments: Radiology, Psychology, Electrical Engineering Professional Affiliations: Bio-X, Stanford Cancer Institute, Wu Tsai Neurosciences Institute Research Interests include: Development of blood oxygen level-dependent (BOLD) and viscoelastic contrast in MRI Functional MR Elastography for brain activation mapping Optimization of MR-ARFI for transcranial ultrasound guidance Automated spinal cord segmentation (EPISeg) using machine learning Scientific Awards : National Academy of Engineering (2013) Gold Medal, ISMRM (2000) Steinmetz Award, General Electric (1985) Lauterbur Lecture, ISMRM (2018) Recent Publications analyze: Fast fMRI sampling and spurious signal correction Dissociated patterns in default mode network anti-correlations Neural correlates of collaborative behavior in triadic fMRI Salience network contributions to depression pathophysiology

• Health IT
• Healthcare Analytics
• Healthcare Operations Management
• +5 more

Gaurav Jetley is an Assistant Professor in the Department of Computer Information Systems at Colorado State University. Education: PhD in Business Administration (Information Systems) from Muma College of Business, University of South Florida Research Interests: Focuses on healthcare technology, including health IT, healthcare analytics, and healthcare operations management. Employs data-driven methodologies, machine learning, and econometric techniques to solve complex problems in health information systems. Scientific Contributions: Recent publications highlight his work in EHR quality, racial bias in ICU pain measurement, and health IT workflow optimization, demonstrating interdisciplinary applications of business analytics in healthcare. Teaching: Instructs graduate and undergraduate courses in data mining and analytics.

• Biostatistics
• Longitudinal Studies
• Public Health
• +4 more

Denise Esserman is a Professor of Biostatistics at the Yale School of Public Health, where she joined the faculty in 2014. She is a member of the Yale Center for Analytical Sciences and collaborates with multiple departments at the Yale School of Medicine, including the Clinical and Translational Science Award Program, Patient-Centered Outcomes Research Institute, and the Cancer Center. Her research focuses on methodological aspects of clustered randomized trials and sample size calculations. Education: PhD in Biostatistics from Columbia University (2006) MS in Statistics from University of Georgia (2001) Dr. Esserman's research interests span several critical areas in biostatistics and public health methodology. She specializes in clustered randomized trials, with particular expertise in understanding how intraclass correlation coefficients (ICC) and other factors impact sample size calculations. Her work extends to longitudinal studies methodology, randomized controlled trial design, and sampling techniques. She has contributed significantly to statistical methods for clinical trials, healthcare data analysis, and public health research. Her interdisciplinary approach bridges theoretical statistics with practical applications in healthcare settings. Analysis of Dr. Esserman's recent publications reveals a strong focus on methodological innovations in clinical trial design and analysis, particularly for cluster-randomized trials. Her work spans healthcare applications including fall injury prevention in elderly populations, opioid use disorder treatment in international settings, pain management for hemodialysis patients, and validation of medical coding algorithms. She frequently employs advanced statistical techniques including Bayesian methods, mediation analysis, and methods for handling clustered data. Her research demonstrates a consistent commitment to improving the rigor and applicability of statistical methods in public health and clinical research. Dr. Esserman serves as a reviewer for several prestigious journals including the American Journal of Epidemiology, Arteriosclerosis, Thrombosis and Vascular Biology; Statistics in Biopharmaceutical Research; Clinical Trials; and Obesity. As a member of the Yale Center for Analytical Sciences, Dr. Esserman collaborates with numerous researchers across Yale University. Her current projects include the EQuIP trial (HIC ID 2000033355), where she serves as Sub Investigator with primary completion date of 08/31/2027, focusing on mental health and behavioral research for sexual minority women.

Ruurd JaarsmaView profile

Professor

• Orthopaedic Trauma
• Orthopaedic Paediatrics
• Biomechanical Engineering
• +1 more

Professor Ruurd Jaarsma serves as Clinical and Academic Director of Orthopaedics and Trauma Surgery for the Southern Adelaide Local Health Network, practicing at Flinders Medical Centre and Flinders Private Hospital. He holds full membership in Flinders University's College of Medicine and Public Health, Flinders Health and Medical Research Institute, and Medical Device Research Institute within the College of Science and Engineering, bridging clinical practice and academic research since relocating from the Netherlands in 2004. His educational credentials include: MD from University of Groningen, Netherlands (1994) Orthopaedic Surgeon certification from Dutch College of Orthopaedic Surgeons (2003) PhD from University of Nijmegen, Netherlands (2004) FRACS (Orth) from Royal Australasian College of Surgeons (2009) FA(Orth)A from Australian Orthopaedic Association (2012) Research centers on orthopaedic trauma, paediatric orthopaedics, and biomechanical engineering of implants, with specialized focus on rotational malalignment after long bone nailing. His work directly supports UN Sustainable Development Goal 3 (Good Health and Well-being) through trauma care innovation and surgical education. Current investigations integrate machine learning with fracture diagnostics to improve clinical decision-making. Recent publications demonstrate strong AI integration in orthopaedic trauma, featuring machine learning algorithms for scaphoid fracture probability estimation, deep learning classification of tibial plateau fractures, and open-source neural networks for distal radius fracture detection. Concurrent clinical trials address compartment syndrome diagnosis while biomechanical studies examine acetabular fracture outcomes, reflecting his dual focus on computational innovation and clinical validation. His scientific recognition includes: 2006 Burns Alpers Award for excellence in teaching from Flinders University As Director of Orthopaedic Training, he supervises Australian Orthopaedic Association accredited fellowships with registered interests in orthopaedic surgery, biomechanical engineering, and musculoskeletal medicine. His supervisory framework emphasizes translational research connecting biomechanical principles with surgical practice. External engagement includes active participation in SA State Trauma Committees, driving statewide improvements in trauma systems and surgical protocols. Professor Jaarsma maintains clinical-academic synergy through Flinders Medical Centre's orthopaedic department, where he leads trauma service development while directing research initiatives in the Medical Device Research Institute. His current projects focus on AI-driven fracture assessment tools and biomechanical optimization of implant systems, with ongoing collaborations across the Machine Learning Consortium and international orthopaedic networks.

Babak TaatiView profile

Associate Professor

• Computer Vision
• Healthcare Technology
• Artificial Intelligence
• +3 more

Babak Taati is an Associate Professor at the University of Toronto (UofT), affiliated with the Department of Computer Science, Institute of Biomedical Engineering (BME), and Rehabilitation Sciences Institute (RSI). He holds the Barbara G. Stymiest Chair in Rehabilitation Technology Research at UHN and is a Senior Scientist at KITE, UHN's research arm. He is also a Vector Institute Faculty Affiliate. His work focuses on applying computer vision and machine learning to healthcare challenges, particularly in rehabilitation technologies for aging populations, gait analysis, fall prevention, and dementia care. Taati leads the Aging team at KITE and is affiliated with the Intelligent Assistive Technology and Systems Lab (IATSL) and the Computational Vision group. Research interests include noninvasive monitoring of health conditions such as Parkinsonism, sleep apnea, and pain management in older adults. His contributions span datasets like the Toronto NeuroFace Dataset and TOAGA archive, emphasizing clinical applications. Taati has taught CSC420 (Image Understanding) repeatedly and has organized workshops on topics like AI in dementia care and ambient intelligence in healthcare. His awards include the TRI-UHN Best Paper Award (2017) and the AMS Healthcare Fellow in Compassion and Artificial Intelligence (2021). He has advised students like Michael Li and collaborates on grants involving federal initiatives (e.g., FedDev Ontario). His research bridges theoretical computer science with practical healthcare solutions, addressing unmet clinical needs through vision-based systems.

Jenni IlomakiView profile

Associate Professor

• Clinical Pharmacy
• Pharmacoepidemiology
• Public Health
• +3 more

Associate Professor Jenni Ilomaki holds a position at Monash University's Centre for Medicine Use and Safety. With expertise in clinical pharmacy, epidemiology, and public health, she leads a research group analyzing administrative claims data. Her work spans collaborations with governmental and non-governmental organizations globally, yielding over 150 peer-reviewed publications and $4 million in grants. She previously served as Chair of ASCEPT's Pharmacoepidemiology Special Interest Group and currently serves as Science Lead for the Monash Addiction Research Centre and Executive Editor of the British Journal of Clinical Pharmacology. Education includes a Bachelor of Science (Pharmacy) from the University of Kuopio (1999) and Master of Science (Pharmacy) from the University of Kuopio (2004). She completed a PhD in alcohol epidemiology at the University of Eastern Finland (2011) and a postdoc at the University of South Australia (2011-2014). Notable recognitions include Young Epidemiologist of the Year (2011) and the Ronald D. Mann Best Paper Award (2021). Key research focuses on quality use of medicines, medicine safety, large population studies, and innovative epidemiological methods. Major projects include developing clinical decision support tools for cardiovascular prevention, analyzing preventable hospitalizations in aged care, and investigating psychotropic medication trends in youth. Her work contributes to UN Sustainable Development Goals related to health equity and non-communicable disease reduction. Recent publications emphasize drug repurposing (e.g., SGLT2 inhibitors), opioid prescribing patterns, hip fracture outcomes, and dementia detection algorithms. She chairs multinational studies on stroke and myocardial infarction cost burdens, demonstrating interdisciplinary impact across pharmacology, epidemiology, and health economics.

Ghazal BargshadyView profile

Lecturer

• Affective Computing
• Artificial Intelligence
• Computer Vision
• +7 more

Dr. Ghazal Bargshady is a Lecturer at the University of Canberra , with expertise in Affective Computing , Artificial Intelligence , and Healthcare Technology . Her roles include teaching units such as Computer Vision, Data Analytics, and Soft Computing, as well as supervising PhD and Master by Research students in AI-driven projects for healthcare and road safety. Education: She earned her PhD in Artificial Intelligence and Computer Vision from the University of Southern Queensland in 2020. Research Interests: Dr. Bargshady specializes in Computer Vision Deep Learning Biosignal Processing Facial Expression Analysis Human Factors in AI Wearable Sensors Multimodal Data Fusion Brain–Computer Interfaces Her work addresses real-world challenges in pain assessment, depression recognition, and driver safety using cutting-edge AI models. Article Trends: Her recent publications focus on Transformer architectures , fNIRS signal analysis , multimodal pain detection , and depression severity estimation via facial video data. These studies highlight her contributions to AI in healthcare , transportation safety , and biomedical signal processing . Teaching Activities: Dr. Bargshady has lectured units including Programming for Data Science , Computer Vision , and Soft Computing , emphasizing practical AI applications.

Dian TjondronegoroView profile

Professor

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +7 more

Professor Dian Tjondronegoro is a leading academic at Griffith University's Department of Management within the Griffith Business School. He holds roles such as Acting Head of Department and Deputy Head (Research), and is affiliated with the Centre for Work, Organisation and Wellbeing, Griffith Asia Institute, and the Griffith Inclusive Future Beacon. His research focuses on AI ethics, eHealth systems, and digital governance, with over $9M in grants from bodies like the ARC and NHMRC. He has published 145+ peer-reviewed papers and leads initiatives like the 'Governing in the Digital Age' program. A Fellow of the Australian Computer Society and Senior Member of IEEE/ACM, he has won the Gold Disrupters Award (2019) and multiple teaching accolades. Education: PhD in Information Systems (Deakin University, 2005), BIS (QUT, 2001). Research Interests: AI, machine learning, healthcare innovation, responsible AI, workplace design, and digital economy strategies. His articles emphasize AI applications in healthcare monitoring, workplace productivity, and ethical surveillance. Recent work explores post-COVID workplace trends and AI-driven public health solutions. He advises on government policy and innovation through roles like Gold Coast Health's Digital Innovation Advisory Committee. His teaching includes courses on digital strategy and innovation management.

Daehyeok KimView profile

Assistant Professor

• Computer Networks
• Operating Systems
• Distributed Systems
• +6 more

Daehyeok Kim is an Assistant Professor in the Department of Computer Science at The University of Texas at Austin, where he co-leads the UT Networked Systems Research Group and participates in the Wireless Networking and Communications Group and 6G@UT. He serves as co-PI for the LDOS NSF Expeditions in Computing project, a major initiative rethinking operating systems through AI. His educational background includes a Ph.D. in Computer Science from Carnegie Mellon University under advisors Vyas Sekar and Srinivasan Seshan, where his dissertation introduced abstractions for elastic in-network computing. He also earned B.S. and M.S. degrees in Computer Science and Engineering from POSTECH, South Korea, followed by research scientist work at KAIST prior to his Ph.D. Kim's research centers on hardware-software co-design for cloud and edge data centers, targeting speed, efficiency, and resilience. Key projects include resource management for programmable infrastructure, robust cellular network design, end-to-end network transport frameworks, and learning-directed operating systems. His work bridges computer networks, operating systems, distributed systems, and 5G/6G technologies, with emphasis on virtualized radio access networks (vRAN) and edge computing challenges. Analysis of his recent publications reveals a dominant focus on enhancing 5G/6G infrastructure reliability—particularly in virtualized RANs—through innovations in failover mechanisms, integrity protection, and latency-sensitive resource allocation. His research consistently addresses critical industry pain points like sub-second availability requirements, fronthaul security vulnerabilities, and end-to-end service-level objective (SLO) guarantees for mobile-edge applications. Notable scientific awards include: NSF CAREER Award (2025) for advancing cloud hardware efficiency Microsoft Research PhD Fellowship (2019) Bronze Award at Samsung HumanTech Paper Awards (2018) Qualcomm Innovation Awards (2016) His grant portfolio features leadership in the $10M+ LDOS NSF Expeditions project and the NSF CAREER award, both driving transformative work in AI-integrated operating systems and resilient network infrastructure. These projects demonstrate strong industry-academia collaboration with Microsoft Research, wireless vendors, and cloud providers. Kim co-leads the UT Networked Systems Research Group, which operates within the Wireless Networking and Communications Group and 6G@UT consortium. These labs maintain a 5G/6G testbed for Open RAN validation and focus on solving real-world problems in cellular infrastructure, edge computing, and network security through close partnerships with industry leaders.

• Machine Learning
• Artificial Intelligence
• Virtual Reality
• +3 more

Simon Colreavy Donnelly is an Associate Professor in the Department of Computer Science & Information Systems at the University of Limerick. He is a member of the Interaction Design Centre and focuses on interdisciplinary research at the intersection of artificial intelligence, educational technology, and healthcare informatics. His work spans machine learning applications in medical data analysis, virtual reality (VR) and extended reality (XR) for inclusive education, and deep learning techniques in chemical analysis and spectroscopy. Research Interests: His primary areas of investigation include generative AI for education equity, semisupervised learning algorithms, virtual learning environments design, and the ethical deployment of immersive technologies in healthcare and palliative care. He also explores NMR spectroscopy analysis using deep learning and develops tools for nutritional content estimation through image processing. Collaborations: His recent collaborations span international teams addressing challenges in toxicity-free online discourse (PAN 2024), semisupervised learning distribution mismatches, and VR applications for post-pandemic blended learning. His work integrates computational methods with real-world applications in education, healthcare, and chemical analysis. Labs/Teams: Active within the Interaction Design Centre at UL, his research group develops practical solutions for accessibility in digital education and healthcare systems, emphasizing user-centered design principles for extended reality applications.

• Artificial Intelligence
• Machine Learning
• Medical Technology
• +7 more

Anis Yazidi is a Professor at Oslo Metropolitan University, affiliated with the Faculty of Technology, Art and Design and the Department of Information Technology. His research focuses on Artificial Intelligence, Machine Learning, Medical Technology, and IoT Security, with a particular emphasis on Applications of AI in Healthcare, EEG Signal Processing, and Digital Transformation. Active research projects include AI Mind (dementia diagnostics), Glycopathology in dry eyes, and Pain and mental distress analysis Completed projects: Digital hate speech analysis, AI in reproductive technology, Nano-antibiotics development His recent publications (2023-2025) demonstrate expertise in: Tsetlin Automaton algorithms for concept learning EEG classification using visibility graphs and vision transformers AI ethics frameworks for medical practice Deepfake detection methodologies Collaborative work spans institutions in Norway, Czech Republic, and international AI research communities.