Tianxi Cai, ScD, holds the John Rock Professorship in Population and Translational Data Sciences at the Harvard T.H. Chan School of Public Health and is a Professor of Biomedical Informatics at Harvard Medical School. She directs the Translational Data Science Center for a Learning Health System (CELEHS). Her work bridges clinical and basic science data to advance personalized medicine and disease understanding. Institution: Harvard University Departments: Biostatistics (T.H. Chan School) and Biomedical Informatics (HMS) Key Roles: Faculty member since 2002, NIH-funded researcher, and leader in EHR data analytics Research focuses on biomarker evaluation, predictive modeling, high-dimensional data analysis, and survival analysis. Collaborates with the I2B2 Center to integrate clinical and genomic data. Active in developing semi-supervised learning methods for noisy EHR data and real-world evidence generation. Funding : Recent grants include NIH projects on rheumatoid arthritis treatment response (R01AR080193, R21AR078339) and semi-supervised EHR denoising (R01LM013614). Co-leads initiatives on chronic disease endpoints using multi-source data (U01FD007929). Labs/Teams : Directs CELEHS and leads the Cai Lab, focusing on translational data science and machine learning applications in healthcare.
Richard J. Cook is a University Professor and Mathematics Faculty Research Chair in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds cross-appointments at the School of Public Health and Health Systems at the University of Waterloo and the Faculty of Health Sciences at McMaster University. Previously, he held a Tier I Canada Research Chair in Statistical Methods for Health Research from 2005 to 2019. His educational background includes: BSc in Statistics from McMaster University MMath in Mathematics from University of Waterloo PhD in Statistics from University of Waterloo Professor Cook's research focuses on developing and applying statistical methods for public health research. His primary areas of interest include the analysis of life history data, longitudinal data analysis, methods for incomplete data, clinical trial design, and multivariate analysis. His work provides critical methodological frameworks for understanding disease progression and evaluating interventions in complex health settings. He has made significant contributions to the development of multistate models for disease processes and methods for handling interval-censored data. His extensive publication record demonstrates consistent focus on methodological innovations addressing real-world health research challenges. Recent work emphasizes estimand specification in clinical trials, transportability of research findings, and causal inference methods. His research bridges theoretical statistics with practical applications in autoimmune diseases, transfusion medicine, and public health. Professor Cook has received significant professional recognition: Tier I Canada Research Chair in Statistical Methods for Health Research (2005-2019) Mathematics Faculty Research Chair at University of Waterloo His students have earned prestigious awards including multiple Pierre-Robillard Awards, ISCB Student Conference Awards, and ENAR Distinguished Student Paper Awards, with notable achievements like Dr. Shu (Joy) Jiang being named in the Forbes Top 30 Under 30 North America (2023) for Healthcare. Professor Cook has advised numerous graduate students throughout his career, with many going on to successful academic and industry positions. His research has been supported by various grants, and he collaborates extensively with researchers in rheumatology, transfusion medicine, and public health through affiliations with the Centre for Prognosis Studies in Rheumatic Diseases, the International Psoriasis and Arthritis Research Team, and the McMaster Centre for Transfusion Research. He leads a vibrant research team that includes research associates, post-doctoral fellows, and graduate students working on cutting-edge statistical methodology. His research group maintains strong connections with multiple institutions and research centers focused on health outcomes and disease progression.
Christopher Buckley is the Kennedy Professor of Translational Rheumatology and Director of Clinical Research at the Kennedy Institute of Rheumatology, University of Oxford. He holds concurrent roles as Director of NIHR Infrastructure for Birmingham Health Partners. His research focuses on fibroblast biology in rheumatoid arthritis (RA), stromal cell interactions, and translational medicine approaches to stratified therapy. He leads the Arthritis Therapy Acceleration Programme (A-TAP), advancing precision medicine strategies for immune-mediated inflammatory diseases. Educations: BSc Biochemistry, University of Oxford (1985) MBBS Medicine, Royal Free Hospital, London (1990) DPhil in Molecular Medicine (Wellcome Trust Fellowship) under Prof. John Bell (Oxford) Research Interests: Pathogenic fibroblast subpopulations in RA and systemic sclerosis Tissue-resident memory T cells in chronic inflammation Spatial transcriptomics of synovial and tendon tissues Pro-resolving fibroblast networks during inflammation resolution Development of biomarkers for disease flare/remission Awards & Leadership: MRC Senior Clinical Fellowship (2001) Arthritis Research UK Professorship (2002) Director, Birmingham NIHR Clinical Research Facility (2012-2017) Key Projects: Leading A-TAP's stratified pathology approach for drug development Investigating Wnt signaling in stromal inflammation Developing cellular atlases of joints using spatial transcriptomics
Yang Luo is a Kennedy Trust Senior Research Fellow in Data Science at the University of Oxford's Kennedy Institute of Rheumatology. His research bridges statistical genomics and computational immunology to unravel genetic contributions to immune-mediated traits, with a focus on the major histocompatibility complex (MHC) region. His work leverages large biobank datasets (UK Biobank, Biobank Japan), gene expression resources (GTEx), and proteomic data to decode molecular mechanisms linking genetic variation to disease risk. Specific interests include tuberculosis genetics, multi-ancestry polygenic risk scores, and single-cell eQTL modeling. Recent publications highlight expertise in HLA association studies, evolutionary immunogenetics, and disease-specific cell state dynamics. Key contributions include constructing a global HLA haplotype panel and developing novel statistical methods for admixed population genetics. Scientific Awards: Kennedy Trust Senior Research Fellow in Data Science His lab integrates computational and experimental approaches to translate genetic findings into clinical applications for immune disorders.
Anthony A Gatti is a Postdoctoral Scholar at Stanford University's Wu Tsai Human Performance Alliance and School of Medicine. His research integrates biomechanics , medical imaging , and machine learning to advance musculoskeletal health diagnostics, particularly focusing on knee osteoarthritis and exercise physiology. Education : Ph.D. in Rehabilitation Science (McMaster University, 2021), M.Sc. in Rehabilitation Science (McMaster University, 2015), B.Sc. in Kinesiology (McMaster University, 2013) His research develops automated tools for quantifying knee anatomy and integrating anatomical data with biomechanical models . These methods analyze acute responses to exercise and long-term joint degeneration, leveraging MRI , deep learning , and statistical shape modeling . Recent publications emphasize AI-driven segmentation , exercise-induced cartilage changes , and biomechanical simulations , spanning journals like Magnetic Resonance in Medicine and Arthritis & Rheumatology . Trends include machine learning validation for clinical predictions and open-source tool development for musculoskeletal analysis. Scientific Awards : CIHR Postdoctoral Fellowship (top 1%), Mitacs Accelerate Entrepreneur, Forge Student Start-Up Competition Winner, multiple scholarships from McMaster University He founded NeuralSeg , a company commercializing deep learning-based MRI segmentation technology. Collaborations include Stanford's Digital Athlete Moonshot Project with advisors like Scott Delp and Garry Gold.
Molly Maleckar is a Research Professor at the Computational Physiology Department of Simula Research Laboratory , Oslo, Norway. Her work bridges computational modeling, cardiac electrophysiology, and biomedical applications, with a focus on arrhythmia mechanisms, fibrosis modeling, and machine learning integration in cardiac risk prediction. Research Interests include: Computational Cardiology Ion Channel Dynamics Machine Learning in Medicine Excitable Tissue Modeling Cardiac Fibrosis Analysis Biomedical Simulation Scientific Contributions span 15+ publications (2018-2024) addressing atrial fibrillation, calcium handling, and AI-driven ECG analysis. Key collaborative projects involve patient-specific ventricular modeling and educational initiatives like the Simula Summer School in Computational Physiology .
Professor Ananya Choudhury serves as Chair and Honorary Consultant in Clinical Oncology at the University of Manchester, where she is also Co-Group Leader of the Translational Radiobiology Group within the Division of Cancer Sciences. She joined The Christie NHS Foundation Trust in 2008, specializing in urology and sarcoma, and has since focused on radiotherapy-related research in prostate and bladder cancers. Professor Choudhury is clinical lead for advanced radiotherapy, including the groundbreaking MRLinac project, and plays a key role in national radiotherapy research initiatives. Professor Choudhury earned her BA (Hons) in 1993, MB. BChir (Cantab) in 1995, and MA (Cantab) in 1997 from Trinity College, Cambridge. She completed her Clinical Oncology training at the Yorkshire Deanery from 2000-2008, during which she earned her MRCP in 2000 and F.R.C.R in 2004. She completed her PhD in 2008 through the University of Leeds and Princess Margaret Hospital in Toronto, Canada, where she studied the molecular epidemiology of DNA double strand break repair in bladder cancer. Professor Choudhury's research program focuses on optimizing and personalizing radiotherapy using advanced imaging technology to deliver high doses while minimizing side effects. Her work centers on prostate and bladder cancers, with particular interest in predictive biomarkers, hypoxia, and the integration of magnetic resonance imaging to improve treatment precision. She has pioneered research in radiotherapy dose optimization, biomarker development, and the identification of patients who would benefit most from different treatment approaches. Her extensive publication record demonstrates a strong focus on radiation therapy, particularly in genitourinary cancers. Recent work explores MRI-guided radiotherapy, hypoxia biomarkers, and personalized treatment approaches across multiple cancer types. She has made significant contributions to understanding how imaging technology can improve radiotherapy precision and effectiveness while reducing side effects, with several publications appearing in top journals through 2025. Professor Choudhury has received multiple prestigious awards recognizing her contributions to the field: Cancer Research-UK/Royal College of Radiologists Clinical Training Fellowship (2005) Fellowship for the 10th ECCO-AACR-ASCO Workshop on Methods in Clinical Cancer Research (2007) Outstanding Contribution, Greater Manchester Clinical Research Awards (2017) RCR Research Fellowship (2005) Research Fellowship, Princess Margaret Hospital, Toronto (2004) Professor Choudhury has supervised numerous doctoral and master's students across multiple cancer types, with current students expected to complete through 2024. She is Principal Investigator on multiple research grants, including 'Measuring tumour radioresistance to improve radiotherapy outcomes' and the 'MAESTRO Programme' as part of CRUK RadNet. Her research program is supported by significant funding from NIHR Manchester Biomedical Research Centre and other major funding bodies. As Co-Group Leader of the Translational Radiobiology Group, Professor Choudhury collaborates extensively with leading researchers including Peter Hoskin, Catharine West, Corinne Faivre-Finn, and Marcel van Herk. Her team is at the forefront of integrating advanced imaging with radiotherapy to improve cancer treatment outcomes, with active projects spanning from basic radiobiology to clinical implementation of novel radiotherapy techniques.
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.
Gary M. Shaw is the Rosemarie Hess Professor and Professor (Research) at Stanford University , with courtesy appointments in the Department of Epidemiology and Population Health and Department of Obstetrics & Gynecology - Maternal Fetal Medicine . He serves as Co-PI of the March of Dimes Prematurity Research Center at Stanford and PI of the California Center for Finding Causes and Preventives of Birth Defects . His research focuses on the Epidemiology of birth defects Gene-environment interactions in perinatal outcomes Nutritional factors in reproductive health . He has developed machine learning approaches for precision parenteral nutrition and predictive models for preterm birth, while investigating persistent metabolomic signatures following hypertensive pregnancy disorders. Shaw's recent work explores Climate change impacts on reproductive health Maternal-fetal immune interactions Epigenetic mechanisms in perinatal disease with applications of multiomics to neonatal intensive care units. As a member of Bio-X and the Maternal & Child Health Research Institute , he contributes to translational research networks while serving as Associate Editor for Birth Defects Research and American Journal of Medical Genetics . He supervises Med Scholar Project student Richard Liang Doctoral co-advisor for Saskia Comess and Richard Liang Master's advisor for Lenae Joe while leading the Division of Neonatology as Associate Chair for Clinical Research (2012-2025). His laboratory work integrates Metabolomic profiling Proteomic analysis Computational modeling Machine learning for biomedical data to advance neonatal care through precision medicine approaches.
Dr. Vera Deneer is an Associate Professor of Clinical Pharmacology at the Utrecht Institute for Pharmaceutical Research (UIPS) within the Faculty of Science at Utrecht University, specializing in the Division of Pharmacoepidemiology and Clinical Pharmacology since 2019. She also serves as a hospital pharmacist and clinical pharmacologist at the University Medical Center Utrecht (UMC Utrecht) at the Department of Clinical Pharmacy since 2017. Dr. Deneer holds significant leadership positions including Vice-Chair of the Medicines Evaluation Board (MEB-CBG) since 2019 and Chair of the Dutch Pharmacogenetics Working Group (DPWG). Dr. Deneer received her PharmD from Utrecht University in 1991 and her PhD from Groningen University in 2003, with research focused on clinical pharmacology and pharmacokinetics of antiarrhythmic drugs in atrial fibrillation. She completed clinical training in hospital pharmacy in 1994 and clinical pharmacology training in 1998. Prior to her current positions, she served as Head of the Pharmacogenetics, Pharmaceutical and Toxicological Laboratory at St. Antonius Hospital, Nieuwegein/Utrecht from 1998 to 2017. Her research focuses on personalized medicine through the study of biomarkers including genetic variants, patient characteristics, and clinical parameters to optimize drug treatment efficacy and safety. Her work primarily targets cardiovascular disease, lung cancer, and immune-mediated inflammatory diseases. She also investigates clinical reasoning and decision-making by pharmacists to improve medication management in clinical practice. Dr. Deneer serves as principal investigator for multiple research projects funded by The Netherlands Organisation for Health Research and Development. Dr. Deneer's recent publications demonstrate her leadership in pharmacogenetics guidelines development, with numerous Dutch Pharmacogenetics Working Group (DPWG) guidelines published in 2023-2025 covering gene-drug interactions for various medication classes including antidepressants, antipsychotics, anti-epileptics, and cardiovascular medications. Her work spans both clinical implementation research and educational aspects of pharmacy practice. Among her significant appointments, Dr. Deneer serves as Vice-Chair of the Medicines Evaluation Board (MEB-CBG) and Chair of the Dutch Pharmacogenetics Working Group (DPWG). She previously chaired a medical research ethics committee from 2012-2017 and serves on multiple national and hospital committees related to pharmacotherapy and drug safety. Dr. Deneer has been actively involved in mentoring pharmacy students and professionals, with recent publications focusing on clinical decision-making education for pharmacists. Her work bridges the gap between pharmacogenetic research and clinical implementation, with particular emphasis on optimizing drug treatment strategies for individual patients while minimizing adverse drug reactions.
Veronica J. Berrocal is an Associate Professor in the Department of Biostatistics at the University of Michigan School of Public Health. Her work focuses on developing statistical methods for spatial, spatio-temporal, and longitudinal data with applications in environmental health, atmospheric sciences, and medical fields including rheumatology and reproductive endocrinology, contributing to public health protection through research and EPA advisory roles. Her educational background includes: PhD in Statistics from the University of Washington (2007) MSc in Statistics from Michigan State University (2002) Dr. Berrocal specializes in creating statistical models for dependent data structures, particularly spatial and spatio-temporal frameworks. Her research addresses environmental determinants of health such as air pollution, weather patterns, built environment, and socio-economic factors, with direct applications in atmospheric sciences, environmental epidemiology, and medical domains like rheumatology and reproductive health. She develops hierarchical models for environmental risk prediction, calibrates geophysical models, and leverages complex data sources including social media for exposure assessment. Her recent publications (2016-2019) demonstrate consistent methodological innovation in spatial statistics applied to critical public health challenges. Key themes include nonstationary spatial prediction for environmental resources, distributed lag modeling of pollutant interactions, and advanced spatio-temporal frameworks for fMRI and urban pollution mapping. Her work bridges statistical theory with practical health impact assessments across atmospheric science, environmental epidemiology, and medical imaging domains.
Dr. Ulas Bagci is an Associate Professor at Northwestern University's Feinberg School of Medicine, Department of Radiology. He holds courtesy appointments in Biomedical Engineering (BME), Electrical and Computer Engineering (ECE) at Northwestern, and Computer Science at the University of Central Florida. As the director of the Machine and Hybrid Intelligence Lab, his research focuses on AI and machine learning applications in biomedical and clinical imaging. Education: BS: Bilkent University (2003) MS: Koç University (2005) Fellow: University of Pennsylvania (2009) PhD: University of Nottingham (2010) ISTP Fellow: NIH (2012) Research Interests: Dr. Bagci’s work spans artificial intelligence, machine learning, and their integration into medical imaging workflows. His lab develops algorithms for tumor segmentation, radiomics analysis, and ethical AI frameworks in healthcare. Notable projects include large-scale MRI segmentation of cirrhotic livers and predictive models for clinical outcomes in oncology and cardiology. Publications: His recent work emphasizes AI-driven solutions for challenges in radiology, including lung disease detection, pulmonary embolism mortality prediction, and ethical considerations in foundational AI models. His articles reflect a focus on bridging clinical needs with advanced computational methods. Lab & Affiliations: The Machine and Hybrid Intelligence Lab collaborates with the Robert H. Lurie Comprehensive Cancer Center. Research themes include federated learning, medical image synthesis, and AI ethics in clinical decision-making.
Olof Stephansson is a senior researcher and group leader at the Clinical Epidemiology Unit (KEP) , Karolinska Institutet , focusing on reproductive, perinatal, and pediatric epidemiology. His work utilizes Swedish healthcare registries and quality registers to investigate risk factors, interventions, and medical management during pregnancy and childbirth. Key research areas: maternal obesity, preeclampsia prediction, climate change impacts, neonatal neurodevelopment, IBD in pregnancy, and labor management Funded by: Swedish Research Council, NIH, FORTE, NordForsk, and Karolinska Institute Collaborates with: Stanford, Oregon Health, University of British Columbia, London School of Hygiene, University of Witwatersrand His group (2014-present) manages the Swedish Pregnancy Registry and conducts randomized trials like the Oneplus study on midwifery models. Recent publications (2024-2025) analyze maternal BMI effects on offspring sleep, labor duration risks, and vaccine safety in pregnancy.
Eva Gerdts is a Professor at the Clinical Institute 2, University of Bergen, and is affiliated with Haukeland University Hospital. She is a Member of the Norwegian Academy of Sciences and leads the Bergen Hypertension and Cardiac Dynamics Group. Her work is central to the Center for Research on Heart Disease in Women, established in 2020 with support from the Heart Foundation, Bergen Women's Health Association, and the Grieg Foundation. University: University of Bergen Affiliation: Clinical Institute 2 Research Group: Hypertension and Cardiac Dynamics Center: Center for Research on Heart Disease in Women Email: eva.gerdts@uib.no Her research focuses on heart disease in women , particularly as influenced by hypertension, aortic valve stenosis, obesity, and autoimmune diseases. She investigates sex-specific differences in cardiac strain, arterial stiffness, and myocardial remodeling. Her work emphasizes how male-based data cannot be extrapolated to women, advocating for gender-specific cardiovascular guidelines. Her recent publications span population studies like the Tromsø and Hordaland Health Surveys, clinical trials, and international collaborations. Key trends include sex differences in hypertension outcomes , cardiac effects of bariatric surgery , cryptogenic stroke in young adults , and inflammatory markers in autoimmune diseases . Her research integrates echocardiography, longitudinal data, and public health implications. Scientific recognition includes: Member of the Norwegian Academy of Sciences National Heart Association's Heart Research Prize 2022 She supervises multiple PhD and Master’s students, including Ester Kringeland, Sahrai Saeed, and Arleen Aune. She leads the PhD course NORHEART901 in Cardiovascular Imaging and teaches in medical education on hypertension, cardiac ultrasound, and women's heart health. Her research is supported by large-scale population studies and clinical collaborations, particularly through the NOR-SYS and SECRETO projects. She also organizes professional education on valvular disease and dyspnea. She leads or collaborates with several research teams: Bergen Hypertension and Cardiac Dynamics Group Center for Research on Heart Disease in Women NOR-SYS (Norwegian Stroke in the Young Study) SECRETO (Searching for Explanations for Cryptogenic Stroke in the Young) FATCOR Study (Fitness, Adiposity, and Cardiovascular Risk)
Professor Maria Eriksdotter is a leading academic in geriatrics and dementia research at the Karolinska Institutet , holding the Department of Neurobiology, Care Sciences and Society . She also serves as a Senior Consultant in Themes Inflammation and Ageing at Karolinska University Hospital Huddinge and previously as Dean of KI South (2019–2023). Her work spans translational research, clinical trials, and national registry development, with a focus on Alzheimer's disease, cholinergic therapies, and aging. Her research group pioneered NGF cell therapy for Alzheimer's patients, demonstrating safety and cognitive stabilization in clinical trials. She chairs the SveDem registry , tracking over 100,000 dementia patients to refine diagnostics and care. Studies from SveDem revealed mortality reduction with cholinesterase inhibitors and highlighted pandemic-era diagnostic delays. Recent publications analyze dementia subtypes , comorbidities , and precision medicine in neurodegeneration. Her work intersects neuroimaging , epidemiology , and public health policy , addressing ageism and improving geriatric care systems. Collaborations span Karolinska University Hospital , NSGene Inc , and international institutions.
