Chris Beaulieu, MD, PhD
Professor, Radiology, Chief of Musculoskeletal Imaging
Three dimensional computer graphics representation of, medical imaging data, including clinical applications of virtual, colonoscopy and arthroscopy; interactive MRI evaluation of joint, motion.
Terry Desser, MD
Professor of Radiology (Abdominal Imaging)
Research interests include: machine learning algorithms for risk stratification of imaging findings in the thyroid gland and the abdomen; natural language processing of free text radiology reports; data mining of medical records for correlation with radiology findings and outcomes.
Email | 650-723-8463
Bao H. Do, MD
Clinical Assistant Professor (Affiliated) Radiology
Natural language processing of imaging and biomedical text in electronic health records, building real-time decision support systems using qualitative and quantitative features of imaging data, retrospective data mining of imaging text corpus in population studies, leveraging informatics for early cancer detection
Ahmed El Kaffas, PhD
I develop quantitative ultrasound methods to characterize tissues. The overarching aim is to relate quantitative imaging parameters from different ultrasound-based imaging modes to biomarkers that can be used for disease surveillance, diagnosis and treatment monitoring/characterization.
Olivier Gevaert, PhD
Assistant Professor, Medicine - Biomedical Informatics Research
My lab focuses on biomedical data fusion: the development of machine learning methods for biomedical decision support using multi-scale biomedical data. Previously we pioneered data fusion work using Bayesian and kernel methods studying breast and ovarian cancer. Additionally, we developed computational algorithms for the identification of driver genes using multi-omics data.
Safwan Safar Halabi, MD
Clinical Assistant Professor, Radiology - Pediatric Radiology
Research interests include clinical and imaging informatics, clinical outcomes and population health, clinical data mining, workflow efficiencies (Lean and Six-Sigma), patient and physician-centered communication, mobile and telemedicine/teleradiology, and fetal imaging (advanced US and MRI techniques).
Email | (650) 723-0705
Aya Kamaya, MD
Associate Professor of Radiology (Body Imaging)
Research interests include hepatobiliary imaging and HCC screening, surveillance, and diagnosis; quantitative evaluation of enhancement to help predict risk of malignancy using contrast-enhanced US and perfusion CT; thyroid cancer imaging and risk stratification.
David Larson, MD
Associate Professor, Pediatric Radiology
Associate Chair of Performance Improvement, Department of Radiology
Quality measurement and improvement in radiology, CT radiation dose optimization, probability modeling of likelihood of acute appendicitis in children based on ultrasound
Charles Lau, MD, MBA
Clinical Associate Professor, Diagnostic/Interventional Radiologist
Research interests include clinical decision support tool development on mobile platforms, effective user interface design for clinical software, and use of time-study methods in clinical radiology practice environment to improve performance and workplace satisfaction.
Ann Leung, MD
Professor, Radiology, Diagnostic Radiology, Chief of Thoracic Imaging
High-resolution computed tomography of the thorax,, particularly its application in the setting of acute lung disease in the immunocompromised host; quantitative assessment of abnormalities, using spiral CT; and enhancement characteristics of lung cancers on, CT and MRI
Email | 650-725-0541
Ruijiang Li, PhD
Assistant Professor (Research) of Radiation Oncology (Radiation Physics)
Our lab focuses on the development of quantitative imaging biomarkers for prognostication and prediction of therapy response in cancer. We integrate multi-modality imaging (such as PET/CT, MRI) and employ advanced image analysis and statistical/machine learning tools to aid the discovery of novel imaging characteristics with clinical relevance.
Email | 650-721-4449
Ying Lu, PhD
Professor, Biomedical Data Science and, by courtesy, Radiology (Molecular Imaging Program)
Research interests include biostatistics, clinical trials, statistical evaluation of medical diagnostic tests, radiology, osteoporosis, meta-analysis, and medical decisoin making.
Email | 650-736-8300
Matthew Lungren, MD, MPH
Assistant Professor, Pediatric Radiology
Research interests include natural language processing and statistical data modeling for predicting imaging test outcomes, data mining as an approach for epidemiologic problem solving, building integrative clinical support tools, and deep learning for medical image analysis.
Email | 650-723-3043
Parag Mallick, MD
Assistant Professor, Radiology
Our general hope is to apply systems biology's complimentary computational and experimental methods in hopes that experimental results motivate large-scale computational studies, which initiate new experimental explorations. We hope this synergistic combination will provide insight into the relationship between molecular phenomena and organismic phenomena.
Email | 650-723-4551
Andrew Ng, PhD
Associate Professor, Computer Science and Electrical Engineering
Research interests include AI, Machine Learning, Deep Learning, and the applications of these techniques to healthcare. Particular interest in conversational agents (e.g., Melody the Medical Assistant), medical imaging, and machine learning from EHR.
Email | 650-725-2593
David Paik, PhD
Director, Imaging Science at Elucid Bioimaging Inc.
Dr. Paik's research interests lie at the intersection of radiology, molecular biology and informatics. He focuses on developing and validating computational methodologies for extracting useful information content from anatomic, functional and molecular images, drawing upon image processing, computer vision, computer graphics, computational geometry, machine learning, biostatistics, modeling and simulation.
Bhavik N. Patel, MD, MBA
Assistant Professor of Radiology (Body Imaging)
Research interests include investigating quantitative CT imaging markers for oncologic and non-oncologic body applications; using novel techniques, such as dual-energy and texture analysis, to achieve the former; developing predictive models to aid in medical decision making specific to a particular patient’s disease process.