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School of Medicine
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We specialize in unconventional, multi-disciplinary approaches to studying the heart at the intersection of applied mathematics, physics and computer science. We focus on theory development that leads to new technology and value delivery to the society. Currently we have three research programs:
1. Precision Medicine
To develop a quantitative approach to personalized risk assessment for stroke and dementia based on patent-specific heart anatomy, function and blood flow.
Disciplines: Cardiac Hemodynamics; Medical Imaging Physics; Continuum Mechanics; Computational Fluid Dynamics
2. Information Theory
To quantify and perturb cardiac fibrillation that emerges as a macro-scale behavior of the heart from micro-scale behaviors of inter-dependent components.
Disciplines: Cardiac Electrophysiology; Spiral Wave; Information Theory; Complex Networks
3. Artificial Intelligence
To develop artificial intelligence algorithms to predict the future risk of heart attack, stroke and sudden... death, and to assist surgical interventions to prevent these outcomes.
Disciplines: Medical Imaging Physics; Artificial Intelligence; Robotically Assisted Interventions
The Shenderov Lab focuses on the elucidation of the mechanisms of immune response and resistance to immunotherapy in Prostate Cancer. This has led to clinical and basic research investigating the presumptive checkpoint inhibitor B7-H3.
In pursuit of understanding biomarkers or resistance and response, and regulatory molecules of immune response, we utilize artificial intelligence, immunogenomics, and spatial proteomics and transcriptomics in the laboratory and at the bedside using clinical trial correlative samples.
Andreia Faria's Laboratory focuses on investigating brain functions using MRIs. We develop and apply methods for processing and analyzing diverse MRI modalities in order to characterize distinctive brain patterns and to study multiple conditions, including neurodegenerative diseases, psychiatric disorders, and stroke. We use artificial intelligence to develop tools for brain MRI segmentation and quantification, promoting the means to perform reliable and reproducible translational research.
The Johns Hopkins Laboratory of Computational Intensive Care Medicine (LCICM) has been established to gain knowledge on the mechanisms of critical illness and injury, with the aim of identifying novel methods to treat patients admitted to the intensive care unit (ICU). Members of the lab apply mathematical and statistical models, artificial intelligence, and domain expertise to unravel patterns in data from sources such as electronic health records, high-frequency physiological recordings, and medical imaging. These patterns are resolved into health signatures that can be leveraged for classification and prediction. The overarching goal is to enhance the precision, efficacy, and outcomes of care delivered to critically ill patients.
Directed by Debraj “Raj” Mukherjee, MD, MPH, the laboratory focuses on improving access to care, reducing disparities, maximizing surgical outcomes, and optimizing quality of life for patients with brain and skull base tumors.
The laboratory achieves these aims by creating and analyzing institutional and national databases, developing and validating novel patient-centered quality of life instruments, leveraging machine learning and artificial intelligence platforms to risk-stratify vulnerable patient populations, and designing novel surgical trials to push the boundaries of neurosurgical innovation.
Our research also investigates novel approaches to improve neurosurgical medical education including studying the utility of video-based surgical coaching and the design of new operative instrumentation.