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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
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The Connor Laboratory focuses on understanding the neural algorithms that make object vision possible. The goal of our research is to explain the neural basis of visual experience and contribute to designs for more powerful machine vision systems and brain-machine interfaces.
The Fernando Pineda Lab uses advanced computational and mathematical techniques to devise solutions to problems in modern biology and public health. We develop analysis algorithms, software tools and models that help biologists characterize biological systems. For example, we have studied the life cycle of the Plasmodium falciparum parasite, which is responsible for more than 1 million malaria deaths per year, with the goal of treating and preventing the disease. We are developing an ab initio computational strategy for finding rapidly evolving ncRNA genes in the parasite.
The Linda Smith-Resar Lab primarily investigates hematologic malignancy and molecular mechanisms that lead to cancer as well as sickle cell anemia. Recent studies suggest that education is an important and effective component of a patient blood management program and that computerized provider order entry algorithms may serve to maintain compliance with evidence-based transfusion guidelines. Another recent study indicated that colonic epithelial cells undergo metabolic reprogramming during their evolution to colorectal cancer, and the distinct metabolites could serve as diagnostic tools or potential targets in therapy or primary prevention.
Research in the Quantitative Imaging Technologies lab — a component of the Imaging for Surgery, Therapy and Radiology (I-STAR) Lab — focuses on novel technologies to derive accurate structural and physiological measurements from medical images. Our team works on optimization of imaging systems and algorithms to support a variety of quantitative applications, with recent focus on orthopedics and bone health. For example, we have developed an ultra-high resolution imaging chain for an orthopedic CT system to enable in-vivo measurements of bone microstructure. Our interests also include automated methods to extract quantitative information from images, including anatomical and micro-structural measurements, and shape analysis.
The mission of the Transplant and Oncology Infectious Diseases (TOID) Center is to expand institutional expertise in clinical and academic activities focused on infectious complications in transplant (solid organ and stem cell) and oncology patients at Johns Hopkins medical institutions. Key efforts include developing standardized algorithms for the prevention and treatment of infections in these vulnerable patients and to establish an expanded infrastructure to facilitate clinical and translational studies at TOID. Current research projects focus on diagnostics for invasive fungal infections and specialized studies of the pathogenesis of candidiasis and aspergillosis.
Research in the Wojciech Zbijewski Lab — a component of the Imaging for Surgery, Therapy and Radiology (I-STAR) Lab — focuses on system modeling techniques to optimize the x-ray CT imaging chain. We’re specifically interested in: 1) using numerical models to improve the task-based optimization of image quality; 2) exploring advanced modeling of physics in statistical reconstruction; 3) using accelerated Monte Carlo methods in CT imaging; and 4) conducting experimental validation of such approaches and applying them to the development of new imaging methods.
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