Narges Sharif Razavian is an Assistant Professor at NYU Grossman School of Medicine , holding appointments in both the Department of Population Health and Department of Radiology . She earned her PhD from Carnegie Mellon University and completed postdoctoral training at New York University's Courant Institute in Computer Science's Machine Learning group. Research focuses on applying Machine Learning and Artificial Intelligence to healthcare challenges, including Predictive Analytics for disease outcomes, Biomarker Discovery , and Medical Imaging analysis. Recent publications highlight her work on AI-driven diagnosis in oncology (lung and pancreatic cancer), hematoma expansion prediction in neurology, and real-time models for infectious disease outcomes (e.g., COVID-19). She utilizes Electronic Health Records (EHRs) and multimodal data to develop clinical decision support systems, with applications in public health surveillance and personalized medicine. Contact: Email | Phone: 212-263-2234 | Office: 227 East 30th Street, 6th Floor, Room 639, New York City
Zion Zibly, MD, MBA is an Associate Professor in the Department of Neurosurgery at Yale School of Medicine . He holds multiple leadership roles including Director of the Center of Neuromodulation , Director of the Center of Neurosurgical Cancer Pain , and Head of Stereotactic & Functional Neurosurgery and the Focused Ultrasound Institute . Previously served as Chair of Neurosurgery at Sheba Medical Center after graduating from Technion’s Faculty of Medicine (MD) and Coller School of Management (MBA). Research Interests: Specializes in Neuromodulation for movement disorders (Parkinson’s, tremors, dystonia), Deep Brain Stimulation , Gene Therapy for pediatric neurodegenerative conditions, Oncological Neurosurgery , and Neurological Pain Management . Combines Functional Neurosurgery with Focused Ultrasound technology. Scientific Contributions: Participated in pioneering Alzheimer’s brain stimulator procedures and Gene Therapy applications. Active member of the North American Association of Functional Neurosurgery and Israeli Neurosurgical Society . Clinical Expertise: Implantation of electrostimulators for Parkinson’s and essential tremor, treatment of Benign/Malignant CNS Tumors , and management of Neurological Pain Conditions . Affiliated with Yale Cancer Center and Center for Brain & Mind Health .
Ruth Etzioni is an Affiliate Professor in the Biostatistics Program and Public Health Sciences Division at the Fred Hutchinson Cancer Center. She leads the Etzioni Lab, focusing on cancer screening, early detection, and overdiagnosis analysis. Her work integrates statistical modeling, epidemiology, and clinical research to address critical questions in prostate and breast cancer control. Dr. Etzioni holds the Rosalie & Harold Rea Brown Endowed Chair and has received a $7.4M NIH Outstanding Investigator Award. Education: PhD in Statistics (Carnegie Mellon University, 1990), MS in Statistics (Carnegie Mellon, 1987), BS in Mathematics (University of Cape Town, South Africa). Research interests emphasize biomarkers, clinical trials, and epidemiological methods. She leads the Biostatistics Core for the Pacific Northwest Prostate Cancer SPORE and participates in the Cancer Intervention and Surveillance Modeling Network (CISNET). Her lab develops models to evaluate screening policies, quantify overdiagnosis, and inform healthcare disparities reduction strategies. Key achievements include groundbreaking work on prostate cancer screening's harm-benefit tradeoffs and contributions to multi-cancer early detection (MCED) frameworks. Recent studies address racial disparities in prostate cancer outcomes, metastasis trends, and the clinical utility of novel diagnostics like PSMA PET imaging. Awards include the Brown Endowed Chair (2020), NCI OIA Award (2023), and recognition for advancing cancer data science. Her lab collaborates with institutions globally and mentors students in biostatistics and translational data science.
Ruth Keogh is a Professor of Biostatistics and Epidemiology at the London School of Hygiene & Tropical Medicine (LSHTM), affiliated with the Medical Statistics Department within the Faculty of Epidemiology and Population Health. She is Co-Director of the Centre for Data and Statistical Science for Health (DASH) and serves as Departmental Research Degrees Coordinator. Her academic career has spanned roles from Lecturer (2012–2015) to Associate Professor (2015–2019) before attaining her current rank in 2019. Keogh holds advanced degrees including a DPhil in Medical Statistics/Epidemiology (University of Oxford, 2007), MSc in Applied Statistics (Oxford, 2003), and BSc in Mathematics and Statistics (University of Edinburgh, 2002). Her research focuses on causal inference, clinical trial emulation using real-world data, and applications in cystic fibrosis, infectious diseases, and public health. She leads projects on lung function trajectories, vaccine efficacy, and healthcare policy analysis. Her work integrates biostatistical methods with epidemiological studies, emphasizing rigorous analysis of observational data to inform clinical decisions. Notable areas include evaluating antibiotic treatments for cystic fibrosis patients, assessing diagnostic test accuracy for dengue and leptospirosis, and modeling vaccine effectiveness during the COVID-19 pandemic. She teaches courses in survival analysis, electronic health records, and health data science at LSHTM. Keogh has held leadership roles in the International Biometric Society and the STRATOS Initiative, and she has delivered keynote addresses at international conferences on trial emulation and biostatistical methods. Her contributions bridge methodological innovation and practical health challenges, with over 190 publications and active engagement in global health research networks.
Mike Kirby is a Professor at the Kahlert School of Computing, University of Utah. He also holds adjunct professorships in the Department of Bioengineering and the Department of Mathematics. His current roles include leadership in scientific computing and informatics initiatives, including former directorships of the Utah Informatics Initiative (2019-2023) and the Multi-Scale Multidisciplinary Modeling of Electronic Materials (MSME) Collaborative Research Alliance (2016-2022). He has extensive experience in strategic research initiatives, including serving as Assistant Vice President for Research (2024-2025). Education: Dr. Kirby earned a PhD in Applied Mathematics (2002) and MS in Computer Science (2001) from Brown University, and a BS in Applied Mathematics and Computer Science from Florida State University (1997). Research Interests: Focus on large-scale scientific computing, physics-informed machine learning, computational science and engineering, high-order numerical methods, and visualization. His work bridges applied mathematics and computer science to address real-world engineering challenges. Publications: Over 150 peer-reviewed articles, including high-impact contributions in journals like Journal of Computational Physics and SIAM Journal on Scientific Computing . Recent work emphasizes machine learning for differential equations, topology optimization under uncertainty, and multi-fidelity modeling. Awards: Recognized for leadership in computational science and informatics, including contributions to University of Utah’s Clery Compliance Program. Advising & Grants: Supervised over 50 graduate students and postdocs. Secured funding from NSF, DOE, and industry partnerships, totaling millions in research grants. Active in interdisciplinary collaborations across engineering, materials science, and medicine. Labs/Teams: Scientific Computing and Imaging (SCI) Institute, Utah Informatics Initiative, and the Center for Multiscale Modeling of Electronic Materials (MSME).
Teemu Roos is a Professor at the Department of Computer Science , University of Helsinki , and a Principal Investigator for the Complex Systems Computation Group under the Helsinki Institute for Information Technology. He serves as a Supervisor for the Doctoral Programme in Computer Science and leads multiple research initiatives, including Distributed AI in Supercomputing , AI & Kids , and Generation AI . Dr. Roos also holds a Docent title in Computer Science. His research spans Artificial Intelligence , Machine Learning , and Data Science , with a focus on AI education , graph neural networks , Bayesian modeling , and health informatics . He has pioneered tools like Elements of AI , a free online course now translated into 22 EU languages, and explores the ethical implications of AI-generated content in authorship and inventorship. The 15 most recent publications highlight applications in environmental forecasting (e.g., Mediterranean Sea via graph-based deep learning), healthcare (e.g., skin cancer detection with transfer learning), and social media analysis (e.g., explainable AI platforms for K-12 education). Methodologically, his work advances clustering algorithms , dimensionality reduction , and approximate nearest neighbor search . Scientific Awards: Cor Baayen Award (2009) Nokia Foundation Recognition Award (2019) Best Paper Honorable Mention Award (2013) ICT Influencer of the Year 2019 (Vuoden TiVi-vaikuttaja 2019) World Summit AI's Top-50 Innovators in 2020 Dr. Roos has supervised 2 doctoral students and contributed to 163 academic activities , including invited talks at MIT, University of Cambridge, and the Finnish Institute in Rome. He has secured funding from the Academy of Finland and the Strategic Research Council, focusing on projects like Fast AI-assisted Space Environment Prediction and Urban Exerciser .
Maciej A Mazurowski is an Associate Professor at Duke University School of Medicine, with dual appointments in the Department of Biostatistics & Bioinformatics and Radiology. He is also affiliated with the Department of Electrical and Computer Engineering and is a member of the Duke Cancer Institute. His research focuses on applying machine learning to medical imaging for improved diagnosis and treatment. Ph.D. in Computer Science from the University of Louisville (2008) Dr. Mazurowski's research emphasizes medical imaging , machine learning , and computer vision applications in radiology. His work includes automated segmentation , domain adaptation , prognostic modeling , and foundation models for MRI/CT analysis. His recent publications highlight trends in universal segmentation models (SegmentAnyBone, SegmentAnyMuscle), foundation models for MRI (MRI-CORE), and AI-driven diagnostic tools for breast cancer, glioblastoma, and thyroid nodules. Key challenges addressed include domain generalization , image harmonization , and ethical considerations in clinical AI. Incubation Award for innovative research commercialization Dr. Mazurowski has secured significant research funding from agencies including the National Institutes of Health , National Institute of Biomedical Imaging and Bioengineering , and American Roentgen Ray Society . His work spans CT segmentation , MRI analysis , and AI-based quality assessment across multiple imaging modalities.
Christin A. Knowlton, MD, MA serves as Associate Professor of Therapeutic Radiology at Yale School of Medicine, where she holds dual leadership roles as Vice Chair for Accreditation in Therapeutic Radiology and Medical Director of Smilow Cancer Hospital Care Center-Hamden. Her clinical practice focuses on delivering high-quality, patient-centered radiation oncology services with specialization in breast cancer, lung cancer, bone metastases, and thymoma treatments. Dr. Knowlton's educational background includes a BA from Oberlin College (1994), MA from New York University (1998), MD from SUNY at Stonybrook (2006), and residency training at Hahnemann University Hospital/Drexel University College of Medicine (2011). Her research program centers on optimizing radiation therapy protocols, with particular emphasis on hypofractionation techniques, stereotactic body radiation therapy (SBRT), and management of radiation-related toxicities including pneumonitis and esophagitis. Analysis of her 15 most recent publications reveals a consistent research trajectory focused on improving outcomes in thoracic and breast radiation oncology through dose optimization, toxicity prediction modeling, and innovative treatment approaches for oligometastatic disease. Her work frequently addresses the balance between therapeutic efficacy and radiation-induced complications, with growing emphasis on molecular risk assessment and personalized treatment planning. Smilow Luminary Award of Excellence in Patient Care (2019) National Comprehensive Cancer Network (NCCN) Fellows Recognition Program As a clinician-educator, Dr. Knowlton emphasizes the importance of patient communication and multidisciplinary teamwork, collaborating closely with nursing, physics, dosimetry, and therapy staff to deliver comprehensive cancer care. Her leadership in accreditation reflects her commitment to maintaining the highest standards in radiation oncology practice and education. Dr. Knowlton's clinical trial involvement, including the DD3 trial of dose-deescalated SBRT for centrally located lung cancer, demonstrates her active contribution to advancing evidence-based radiation oncology practice.
Todd A. Alonzo is a Professor of Research in the Department of Preventive Medicine at the University of Southern California . As Group Statistician for the Children's Oncology Group , he focuses on statistical methods for biomarker analysis, medical diagnostic testing, and clinical trial design in pediatric acute myeloid leukemia (AML). Education: B.S. in Statistics, California State Polytechnic University (1994) MS and PhD in Biostatistics, University of Washington (1997, 2000) Research Interests include: Development of statistical frameworks for diagnostic accuracy Genomic and proteomic profiling in AML Pharmacogenomic score systems for chemotherapy response Non-inferiority trial design in low-event-rate settings Health disparities in pediatric oncology Scientific Awards : Fellow, American Statistical Association (2018) Outstanding Teacher Award, International Society for Magnetic Resonance in Medicine (2017) NIH Predoctoral Cardiovascular Biostatistics Training Grant (1995) ENAR Biometrics Society Distinguished Student Paper Award (1999) WNAR Biometrics Society Best Student Oral Presentation (1999) Leadership & Service includes editorial board memberships (Biometrics, Pediatric Blood & Cancer, Biometrical Journal), reviewer for 30+ scientific journals, and roles on multiple Data Safety and Monitoring Boards. He served as President of the International Biometric Society Western Northern America Region (WNAR) in 2009.
Ping He is a Professor in the Department of Molecular, Cellular, and Developmental Biology (MCDB) at the University of Michigan in Ann Arbor. His research focuses on plant immunity mechanisms, particularly using Arabidopsis as a model system to study pathogen defense activation, signaling pathways, and the interplay between immunity and environmental stress responses. He also leads the Molecular, Plant-Microbe Interaction Laboratory, applying interdisciplinary approaches (genetics, biochemistry, cellular biology) to enhance crop resilience through foundational plant science discoveries. His work bridges plant biology and computational biology, with recent contributions to AI-driven medical imaging applications such as bladder cancer treatment response assessment, lung cancer early detection, and breast tomosynthesis denoising. These efforts emphasize integrating machine learning into clinical workflows and establishing best practices for AI in healthcare. Research Highlights: Plant immunity signaling and environmental stress crosstalk Radiomics and deep learning for cancer diagnosis/prognosis AI model validation and multi-institutional clinical trials Medical imaging artifact correction (e.g., motion blur, noise) Publications emphasize AI applications in oncology imaging, radiologist decision support systems, and multimodal data fusion. He has contributed to AAPM task group guidelines for AI in computer-aided diagnosis and advocates for rigorous quality assurance frameworks in medical AI deployment.
Maria Timofeeva is an Associate Professor in the Epidemiology, Biostatistics and Biodemography (EBB) department at the University of Southern Denmark (SDU), with additional affiliation at the Danish Institute for Advanced Study (DIAS). She holds an Honorary Fellow position at the University of Edinburgh since December 2019. Her research focuses on cancer prevention and prediction, particularly studying the effects of environmental and genetic factors on cancer risk and progression. Dr. Timofeeva earned her Dr.sc.hum in Epidemiology from Heidelberg University (2005-2009), with a dissertation on genetic polymorphisms as risk factors for early onset lung cancer. Prior to her current position, she worked as a Statistical Geneticist at the University of Edinburgh (2013-2019) and as a Postdoctoral Fellow at the International Agency for Research on Cancer (2009-2013). Her research interests center around understanding the genetics of cancer risk through multi-omic analysis. She leads several significant projects, including the Interdisciplinary Project on Adherence to Colorectal Cancer Screening, meta-analysis of factors associated with false-positive and false-negative FOBT results (registered in PROSPERO ID: CRD42022315767), and the COlorectal Cancer screening Among RElatives (CoCARE) twin-family study in Denmark. Her methodological expertise spans observational epidemiological studies (case-control, population-based cohort studies, twin studies), meta-analysis, umbrella reviews, and multi-omics data analysis. Analysis of her recent publications reveals a strong focus on colorectal cancer genetics, with particular emphasis on genome-wide association studies, Mendelian randomization approaches, and trans-ancestry analyses. Her work frequently leverages large datasets including the UK Biobank and international consortia, with applications in cancer risk prediction and understanding gene-environment interactions. Dr. Timofeeva has an extensive publication record with 73 publications listed in her profile. Her research has been cited across multiple platforms, with mentions in news outlets, social media, and academic readership platforms like Mendeley. She is actively involved in academic service, serving as a peer reviewer for journals including BMC Cancer and Scientific Reports, and participating in conferences such as the 26th Nordic Congress of Gerontology. She also serves on evaluation committees, including with the World Cancer Research Fund International (April-May 2024). Her teaching activities include courses on evidence-based drug utilization and biostatistics, as well as supervision of research projects on gene expression in twins. Dr. Timofeeva has engaged with the public through media contributions, including an interview titled 'Jeg vil forstå, hvorfor vi får kræft' (November 15, 2021), where she discussed understanding why we get cancer.
Dr. Nivee Pradip Amin serves as Clinical Assistant Professor of Medicine at Weill Cornell Medical College and Assistant Attending Physician at NewYork-Presbyterian Hospital, holding leadership roles as Associate Director for Consultative Cardiology and Director of the Women's Heart Program & Preventive Cardiology. Her clinical expertise spans general cardiology, preventive cardiology, and echocardiography with focus on high-risk cardiovascular prevention. Her educational credentials include: B.A., B.S. in International Studies and Business from University of Pennsylvania (2002) M.D. from Johns Hopkins University School of Medicine (2007) M.H.S. in Clinical Investigation from Johns Hopkins Bloomberg School of Public Health (2013) Research centers on sex-based disparities in cardiovascular outcomes, women's heart health across the lifespan, and preventive strategies for coronary/valvular disease. She investigates intersections between cardiology and obstetrics/oncology, emphasizing personalized risk assessment and early intervention to improve quality of life. Analysis of her publications reveals consistent focus on women's cardiovascular biomarkers (e.g., breast arterial calcium), obstetric complications as long-term risk factors, and disparities in revascularization outcomes. Her work leverages large-scale database analysis to address gaps in preventive care for high-risk populations including cancer patients and those with cirrhosis. Dr. Amin maintains active board certifications in Internal Medicine, Cardiovascular Diseases, Nuclear Cardiology, and Echocardiography through the American Board of Internal Medicine, and holds professional memberships in the American College of Cardiology, American Society for Preventive Cardiology, and American Heart Association. She provides inpatient consultative care on cardiac telemetry units while leading the Women's Heart Program, which integrates preventive cardiology services with gender-specific treatment protocols. Fluent in Spanish, she emphasizes culturally competent care tailored to individual patient needs across diverse populations.
Dr. Sotirios Stathakis is Chief of Physics at the Mary Bird Perkins Cancer Center (2023–Present) and Associate Director of the Medical Physics Division at the University of Texas Health Science Center San Antonio (2017–2022). He holds an Adjunct Professor position at Louisiana State University's Department of Physics and Astronomy (2023–Present). His expertise lies in radiation oncology and medical physics, with a focus on patient-specific quality assurance, dose verification, and advanced treatment techniques like adaptive radiation therapy and SBRT. Education: Ph.D., Medical Physics, University of Patras (2005) M.S., Medical Physics, University of Aberdeen (1997) B.S., Physics (minor in Mathematics and Computer Science), University of Waterloo (1995) Research Interests: Patient-specific quality assurance Daily dose verification Treatment planning techniques Adaptive radiation therapy Stereotactic body radiation therapy (SBRT) Automation and workflow optimization in radiation oncology Publications: Over 20 peer-reviewed articles since 2020, focusing on topics like Monte Carlo simulations, AI-driven beam analysis, and AAPM task group recommendations for IMRT verification. Key themes include improving dose accuracy, equipment validation, and clinical implementation of advanced radiation technologies. Grants & Awards: Not explicitly listed in provided text. Labs/Teams: Collaborates with institutions like Fox Chase Cancer Center, South Texas Veterans Health Administration, and LSU on medical physics research and clinical applications.
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.
Lawrence Staib is Professor of Radiology and Biomedical Imaging, Biomedical Engineering, and Electrical Engineering at Yale University. He serves as Director of Undergraduate Studies in Biomedical Engineering and is a member of Yale's Bioimaging Sciences division, Image Processing & Analysis Group, Yale Biomedical Imaging Institute, and Yale-BI Biomedical Data Science Fellowship program. Dr. Staib earned his A.B. in Physics from Cornell University (1982), followed by a Ph.D. in Engineering and Applied Science from Yale University (1990), and completed a postdoctoral fellowship at Yale School of Medicine (1991). His research focuses on developing advanced medical image analysis methods using machine learning and model-based approaches. Key research areas include neuroimaging applications for autism spectrum disorder classification, cardiac imaging analysis for strain and motion assessment, prostate cancer diagnosis and risk mapping, and innovative techniques for medical image segmentation with limited labeled data. Dr. Staib's work emphasizes uncertainty estimation in deep learning models, multi-modal image registration, and domain adaptation techniques to improve clinical decision support systems. His recent publications demonstrate a strong trend toward developing interpretable AI models for clinical applications, with particular emphasis on fMRI analysis for neurological conditions, cardiac motion analysis, and prostate cancer diagnosis. His work frequently addresses the challenge of limited labeled data in medical imaging through innovative self-supervised, semi-supervised, and few-shot learning approaches. Fellow of the American Institute for Medical and Biological Engineering (AIMBE) (2015) Distinguished Investigator Award from the Academy for Radiology & Biomedical Imaging Research (2017) MICCAI Fellow (2022) Medical Image Analysis Second Best MICCAI Paper Award (2005) ASNR Cum Laude Scientific Exhibit Award (2003) Dr. Staib serves on the editorial board of Medical Image Analysis and as Associate Editor of IEEE Transactions on Biomedical Engineering. His research is supported by NIH grants including the Autism Center of Excellence program. He leads the Image Processing & Analysis Group within Yale's Bioimaging Sciences division, collaborating extensively with James Duncan, John Onofrey, Xenophon Papademetris, and other Yale researchers on applications spanning neuroimaging, cardiology, and oncology. Current projects focus on developing robust AI models for clinical decision support with emphasis on uncertainty quantification and interpretability.
