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.

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.

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.

The Mathioudakis lab is focused on developing and evaluating clinical decision support systems, technology, and mHealth for diabetes prevention and management. Our lab leverages large electronic medical record databases and uses machine learning algorithms and artificial intelligence to identify patterns in clinical care associated with optimal clinical outcomes. We are interested in understanding the role that advanced diabetes technologies can play in improving health outcomes for patients with diabetes. Our lab has published extensively on outcomes related to diabetes prevention and diabetes management and outcomes. Based on data from our long-term (over 10 year) clinic-based prospective cohort study from the Johns Hopkins Multidisciplinary Diabetic Foot and Wound Clinic, we have published extensively on clinical predictors and outcomes of patients with diabetic foot ulcers, focusing specifically on the role that glycemic control plays in patients with this complication. Healthcare disparities exist throughout medicine, but are particularly prominent in diabetes; our lab has evaluated healthcare inequities in diabetes outcomes and is developing and evaluating strategies to overcome them. In addition to identify optimal management approach to diabetes and its complications, our lab is interested in development and evaluation of innovative technology approaches to diabetes prevention.