Research in Computational X: SCI research in Computational X focuses on bringing computational thinking using translational, transdisciplinary and co-design approaches to leverage computing and data to address important and challenging problems in science, engineering, medicine, and humanities. Specific examples of X include fluid dynamics, biomechanics, electrophysiology, bioelectric fields, and neuroimaging. Research areas span novel algorithms and methods, large-scale scientific computing and data analytics, and visualization, graphics, and imaging. As with all research at SCI, translation through the development and deployment of innovative and robust software packages is integral to this research.

Manish Parashar

– Computational and Data-Enabled Science and Engineering
– Parallel & Distributed Computing
– Extreme-scale Computing and Data Management
– Autonomic Computing

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Chris Johnson

– Inverse Problems
– Computational Electrophysiology

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Rob MacLeod

– ECG Imaging
– Cardiac Disease
– Computational Electrophysiology

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Jeff Weiss

– Computational Biomechanics

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Orly Alter

– Computational Biology

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Tamara Bidone

– Computational Models
– Simulations of Biological Systems
– Multi-Physics Models of Cancer Cells

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Amir Arzani

– Cardiovascular biomechanics
– Biotransport
– Scientific machine learning

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Karli Gillette

– Cardiac electrophysiology
– Patient specific models
– Precision medicine

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Guang Tian

– Land use and transportation planning
– Travel behavior and built environment
– Sustainability and climate
– Urban data science and analytics (statistics, GIS, machine learning, AI, etc.)

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Gianfranco Doretto

– Computer Vision
– Machine Learning and AI
– Biomedical and Health Data Analysis
– Image and Video Understanding

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