Satish E Viswanath
Biography
I am an Associate Professor in the Departments of Pediatrics and Biomedical Engineering at Emory University, with secondary appointments in the Department of Biomedical Informatics. I am also a Research Biomedical Engineer at the Atlanta VA Medical Center. The primary focus of my research has been developing new artificial intelligence, radiomics, and machine learning schemes, applied to problems in computer-aided diagnosis and detection, disease characterization, as well as quantitative evaluation of response to treatment; across adult and pediatric conditions. I have authored over 60 peer-reviewed journal publications, 120+ conference papers and abstracts, 1 book chapter, as well as delivered over 100 invited talks and panel discussions both in the US and abroad. I have 10 issued patents in the areas of medical image analysis, computer-aided diagnosis, radiomics, and pattern recognition. I also serve as the Associate Editor or Editorial Board Member for 9 leading international peer-reviewed journals, and as Program Committee Member or Area Chair for 3 major medical imaging conferences. My lab’s research has also been funded via federal and state funding including the DOD/CDMRP, the VA, the NIH (NCI, NIBIB, NINR, NHLBI), as well as private foundations. My honors include election to Senior Member of the National Academy of Inventors, the Institute for Electrical and Electronic Engineers (IEEE), and the International Society for Optics and Photonics (SPIE), the Fulbright Specialist Award, in addition to multiple awards from Society of Imaging Informatics in Medicine (SIIM), and Crain’s Cleveland Business.
Education
- PhD, Rutgers University, 2012
- MSc, Medical Imaging, University of Aberdeen, 2005
- BE, Information Technology, University of Mumbai, 2004
Research Interests
Developing artificial intelligence (AI) schemes to assist the clinician towards enabling precision medicine approaches requires development of objective markers that are predictive of disease response to treatment or prognostic of longer-term patient survival. The solutions being developed in my group in this regard involve designing novel computational imaging methods which can capture biologically relevant and clinically intuitive measurements from a variety of data types, spanning radiological imaging, digital pathology, gene & protein expression, as well as spatial transcriptomics. Uniquely, we attempt to integrate information across multiple length scales of biomedical data by spatially resolving and cross-linking imaging (macro-scale) with molecular and pathology (micro-, nano- scales) data. Toward clinical translation, we not only ensure our AI models are reproducible across institution- or scanner-specific variations but also interrogate the inner workings of our AI tools to gain a deeper, interpretable understanding of what they capture and how they work. Our methods are being designed for oncological and non-oncological conditions, spanning both adult and pediatric populations. Specific problems addressed via the new computerized imaging markers we have developed include: (a) predicting response to treatment to identify optimal therapeutic pathways, as well as (b) evaluating therapeutic response to guide follow-up procedures; in the context of clinical applications in colorectal and renal cancers, digestive diseases, as well as pediatric conditions.