Terry Desser, MD
Professor of Radiology, Abdominal Imaging
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.
Bao H. Do, MD
Clinical Assistant Professor, 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
Instructor of Radiology
Quantitative ultrasound methods to characterize tissues. The 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 of Medicine, Biomedical Informatics Research
Biomedical data fusion: the development of machine learning methods for biomedical decision support using multi-scale biomedical data. Pioneered data fusion work using Bayesian and kernel methods studying breast and ovarian cancer. Additionally, developed computational algorithms for the identification of driver genes using multi-omics data.
Ann Leung, MD
Professor of Radiology, Thoracic Imaging
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
Ruijiang Li, PhD
Assistant Professor (Research) of Radiation Oncology (Radiation Physics)
Development of quantitative imaging biomarkers for prognostication and prediction of therapy response in cancer. Integrate multi-modality imaging and employ advanced image analysis and statistical/machine learning tools to aid the discovery of novel imaging characteristics with clinical relevance.
Parag Mallick, PhD
Associate Professor of Radiology (Canary Center)
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.
Andrew Ng, PhD
Adjunct Professor of Computer Science and Electrical Engineering
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.