The Saha Lab.

Zaman’s Lab focuses on the design and development of novel cutting-edge multi-mode imaging systems to overcome current limitations in clinical systems. Most recent research project is involved with the design and developed of a multimode catheter-based imaging system called a Circumferential Intravascular Radioluminescence Photoacoustic Imaging (CIRPI) for early detection of thin-cap-fibro-atheroma (TCFA), the underlying causes of coronary artery disease, one of the leading causes of morbidity and mortality in the USA and worldwide. Further, the CIRPI system characterizes the plaques based on disease tissue compositions to unravel their complex structures. This CIRPI system integrates optical, photoacoustic, radioluminescence and ultrasound imaging. We seek to better understand the underlying causes of the disease mechanisms. We are dissecting the role of TCFA perturbations on vascular wall processes during atherosclerosis progression. Our lab also studying novel molecular imaging methods to study coronary arterial disease, carotid stenosis, and myocardial ischemia in subcellular level.

The Sharma Lab is interested in investigating intermediary metabolism utilizing carbon-13 stable isotope tracers in conjunction with magnetic resonance spectroscopy (MRS), magnetic resonance imaging (MRI), and mass spectrometry (MS).

Translational biophotonics for noninvasive detection of systemic disease.

The Liu Lab is Interested in developing and evaluating novel therapies, notably targeting tumor vasculatures.

We investigate genetic and molecular basis of phenotypic diversity observed in nature by using a range of methodologies such as whole genome sequencing, fluidics, long-term evolution experiments, and large-scale combinatorial mutagenesis. 

We aim to globally understand how the physical and chemical properties of materials affect interactions with biological systems in the context of improving therapies.

We are driven by the belief that the spatial organization of tissue provides a powerful  window into cell-cell interactions, a crucial component of disease progression and response.

The main research focus of the Otwinowski lab is on developing computational and statistical
methods and protocols for macromolecular structure determination using X-ray crystallography.

We develop the theory and application of deep learning to improve diagnoses, prognoses and therapy decision making.