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Computational X


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
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
Chris Johnson
– Inverse Problems
– Computational Electrophysiology
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Rob MacLeod
Rob MacLeod
– ECG Imaging
– Cardiac Disease
– Computational Electrophysiology
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Jeff Weiss
Jeff Weiss
– Computational Biomechanics
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Orly Alter
Orly Alter
– Computational Biology
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Tamara Bidone
Tamara Bidone
– Computational Models
– Simulations of Biological Systems
– Multi-Physics Models of Cancer Cells
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Amir Arzani
Amir Arzani
– Cardiovascular biomechanics
– Biotransport
– Scientific machine learning
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Karli Gillette
Karli Gillette
– Cardiac electrophysiology
– Patient specific models
– Precision medicine
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Research


Center for Integrative Biomedical Computing (CIBC)
CIBC is dedicated to producing open-source software tools for biomedical image-based modeling, biomedical simulation and estimation, and the visualization of biomedical data. The Center works closely with software users and collaborators in a range of scientific domains to produce user-optimized tools and provides advice, technical support, workshops, and education to enhance user success.
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Weiss Biomechanics Lab
Our laboratory focuses on developing and applying experimental and computational methods, primarily in the area of biomechanics, to address research questions in musculoskeletal science and cardiovascular mechanics. Our research is multidisciplinary. Students learn both experimental and computational techniques in biomechanics and related fields to address questions in their desired area of research.
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Alter Lab
We develop quantum mechanics-based multi-tensor AI/ML, which, as we experimentally validated, is uniquely able to discover accurate, precise, clinically actionable, and mechanistically interpretable predictors from small-cohort, noisy, and multi-dimensional, multi-omic data.
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Computational Biomechanics Group
Our research group focuses on studying the mechanisms behind initiation and progression of different diseases with a particular focus on cardiovascular disease. Particular attention is given to developing computational models that can capture the multiscale and multiphysics nature of cardiovascular disease. Another key focus of our lab is developing new scientific machine learning models with a broad range of applications. Our work spans a variety of disciplines such as computational fluid dynamics (CFD), computational nonlinear structural mechanics, scientific machine learning, mass transport, dynamical systems, medical imaging, mechanobiology, and multiscale modeling.
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Computational Electrocardiology Group
Our research seeks to apply mathematical and computational tools to understand physiological and pathophysiological processes in the field of cardiology. The CEG focused on research domains such as electrocardiographic imaging, cardiac digital twinning, body surface mapping, myordial ischemia, ventricular arrhythmias, and other cardiac pathologies using tools such as simulation, uncertainty quantification, large animal experimental models, machine learning, and shape analysis.
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