Affiliated Faculty

Christopher Beaulieu, MD, PhD
Professor of  Radiology, 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.

Robert D. Boutin, MD
Clinical Professor of Radiology, Musculoskeletal Imaging
Adding value to routine radiology exams by translating advancements from fields of artificial intelligence and imaging informatics into clinical use.

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 Associate Professor of 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

Sergios Gatidis, MD
Associate Professor of Radiology, Pediatric
Medical imaging informatics, machine learning for medical image analysis, pediatric radiology, hybrid imaging, clinical data integration, and clinical translation of AI methods.

Olivier Gevaert, PhD
Associate Professor of Medicine, Biomedical Informatics & Biomedical Data Science
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. 

Aya Kamaya, MD
Professor of Radiology, Body Imaging
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.

Nishita Kothary, MBBS
Professor of Radiology, Interventional Radiology
Interventional Oncology: Percutaneous and transarterial interventions for diagnosis and treatment of primary and metastatic tumors (lung, liver and renal); Research Interest: Gastrointestinal and Hepatic Oncology

David Larson, MD, MBA
Professor of Radiology, Pediatric
Quality measurement and improvement in radiology, CT radiation dose optimization, probability modeling of likelihood of acute appendicitis in children based on ultrasound

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
Associate Professor 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. 

Ying Lu, PhD
Professor of Biomedical Data Science 
Biostatistics, clinical trials, statistical evaluation of medical diagnostic tests, radiology, osteoporosis, meta-analysis, and medical decisoin making.

Parag Mallick, PhD
Associate Professor of Radiology, Cancer Early Detection
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.

Bruno Passebon Soares, MD
Associate Professor of Radiology, Pediatric
Implementation of new clinical MRI protocols in pediatric neuroimaging with the goal of making data accessible to several research initiatives.

Killian M. Pohl, PhD
Associate Professor of Psychiatry and Behavioral Sciences
Computational neuroscience aimed at identifying biomedical phenotypes to improve the mechanistic understanding, diagnosis, and treatment of neuropsychiatric disorders.

Emily Tsai, MD
Clinical Associate Professor of Radiology
Lung cancer screening, interstitial lung disease characterization, biomarkers, CT-guided procedures and radiology-pathology correlation, opportunistic screening, clinical applications of AI/machine learning