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.

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.

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.

The Center for Extreme Data Management Analysis and Visualization (CEDMAV) focuses on theoretical and algorithmic research, systems development, and tool deployment for dealing with extreme data.

The SCI Institute oneAPI Center of Excellence team is extending its Intel GVI work to pursue new high-performance visual computing methods that utilize oneAPI cross-architecture programming, which delivers performance and productivity, along withprovides the ability to create single source code that takes advantage of CPUs, GPUs and other accelerator technologiescan be deployed across a variety of architectures.

Interests at the VDL include the process of designing and developing visualizations, visualization for biology, visualization frameworks, and, more generally, visualization of big, heterogeneous, and complex datasets.

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.

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.

TDAVIS focuses on topological data analysis, data visualization, and computational topology. They work on combining topological, geometric, statistical, data mining, and machine learning techniques with visualization to study large and complex data for information exploration and scientific discovery. Some of their current research activities involve the analysis and visualization of high-dimensional point clouds, scalar fields, vector fields, tensor fields, networks, and multivariate ensembles.

U.S. National Science Foundation Cyberinfrastructure Center of Excellence (CI CoE). NSF CI Compass provides expertise and active support to cyberinfrastructure practitioners at U.S. National Science Foundation (NSF) Major Facilities in order to accelerate the data lifecycle and ensure the integrity and effectiveness of the cyberinfrastructure upon which research and discovery depend.

The National Science Data Fabric (NSDF) pilot builds a testbed experimenting with critical technology needed to democratize data-driven sciences by constructing a CI platform designed for equitable access. In particular, NSDF experiments with key technologies that empower user communities to develop their solutions and support domain-specific requirements while avoiding duplication of technology.

The National Data Platform, or NDP, is a federated and extensible data ecosystem to promote collaboration and innovation on top of existing data and cyberinfrastructure capabilities.

Science Data Exchange (SciDx) is a comprehensive software stack built on NDP-POP (Point of Presence), empowering users to transform and utilize data in real time. SciDx aims to bridge gaps in data sharing and collaboration by offering a robust platform for managing and accessing data easily.