Radiology Research Team
The research team at NYU Langone’s Department of Radiology comprises more than 100 research faculty and staff, including physicists, mathematicians, computer scientists, biomedical engineers, electrical engineers, radiologists, biostatisticians, population health experts, imaging technologists, radiochemists, radiopharmacists, and clinicians practicing various subspecialties of medicine.
Our culture is broadly interdisciplinary and highly collaborative. We strive to incentivize collaboration in and among functional groups rather than separation of investigator-led labs into project-specific silos.
We expect and encourage our researchers to share expertise and cross-pollinate ideas. Our department generously invests in common resources, such as the radiofrequency (RF) engineering core, the supercomputing core, and the preclinical imaging core, which are available to all team members.
Our researchers maintain wide-ranging collaborations with clinicians in the Department of Radiology, colleagues throughout NYU Langone, and scientists at scores of institutions around the world. Our laboratories frequently host visiting scientists, and we in turn dispatch members of our team as scientist envoys to far-flung research centers. Our breadth of expertise is matched by our diversity of origin, with research faculty and staff representing more than 26 nationalities.
Our team has particular strengths in research areas that support the goal of making imaging faster, more intelligent, and more informative. We excel at physics-informed artificial intelligence for imaging, rapid acquisition and complex reconstruction of MRI data, specialized RF hardware development, biophysical modeling of tissue microstructure, and multimodal, multisensory approaches to imaging, which include PET/MR, multinuclear MRI, and MR spectroscopy.
We are highly experienced in the formation and operation of translational “matrixed” research teams, in which medical, academic, and industrial cooperation serves to identify and iterate promising new technologies more quickly. This model dramatically reduces the timeline to successfully translate our most promising research to the clinic.