Research Areas

Rutgers University, 2011. Image analysis algorithms for whole-slide tissue biopsy samples; quantitative image feature set design for biomedical disease states in tissue images; segmentation algorithms for tissue regions and structures; non-linear dimensionality reduction methods for high-dimensional image feature data; supervised and unsupervised classification methods for identifying disease state and prognosis on imaging; active learning methods for efficient training of supervised biomedical image classifiers; multi-target classifiers for identifying targets in the presence of confounders.

Assistant Professor, University at Buffalo. Research focus in my lab spans three inter-related fields: chronic pain, depression and inflammation. We investigate the regulation of cytokine production and release from cells of the nervous system and the immune system, as well as interactions among pro-inflammatory cytokines, such as tumor necrosis factor (TNF), adrenergic, cholinergic, and opioid responses during chronic pain. We also study the peripheral macrophage, a major source of TNF during inflammation. Specifically studying neurotransmitter (i.e., norepinephrine) regulation of TNF production in the periphery is enhancing our knowledge of how the brain controls a peripheral inflammatory lesion. Experiments in our laboratory further focus on how brain-derived pro-inflammatory cytokines, such as TNF, function as modulators of brain-body interactions during neuropathic pain and depressive behavior and how brain-TNF is involved in the mechanism of action of antidepressant drugs that are used to treat both disorders. The overall goal is to advance knowledge of, and therapeutic efficacy for pain, depression, neuro-inflammation and drug addiction. Toward these goals, our research uses both cell systems and animal models to test these hypotheses. Colleagues and I use a combination of imaging techniques to localize cytokine production and identify cell types, bioassays and ELISA (enzyme-linked immunosorbent assays) for pharmacological and functional analyses, electrophysiological (brain slice stimulation) and molecular methods for our studies.

Yale University, 1984. Control of vascular remodeling; the role of hemodynamics in vascular remodeling, especially during the development of cerebral aneurysms; endothelial responses to blood flow and matrix stiffness; flow-induced endothelial signals regulating vascular smooth muscle behavior .

Professor and Chair, University of Pennsylvania. Computational advances offer the promise of enabling the quantitative analysis of structural data at all levels of scale. In Anatomy, imaging and mechanical biosensors can be aligned with computational tools to evaluate large multidimensional data sets gleaned from the human organism. In a parallel approach high-resolution cellular imaging methods including histology, super resolution optical, and electron microscopic examination can be married with the new analytics of machine vision and machine learning. The computational analysis of structure offers incredible new tools with which to quantitatively mine the data within both macroscopic structure (101) and microscopic (10-6 to -9) worlds and integrate those data with other modes including molecular and cell biology information. In our work we seek to use quantitative histological image analysis for modeling complex biological systems. We do this starting with a fundamental hypothesis which is that a high-resolution image is a self-organizing set of data that uniquely represents all of the genes, all of the molecules, and all of the cells captured at one point in time. In other words, a histological image is what it is for very specific reasons and those reasons are the relationships amongst the genomics, epigenomics, proteomics, metabolomics, and all the "omics" that go into making that image. The promise of quantitative histological image analysis lies in the hypothesis that the linkages relating all of the molecular events contributing to an image are still extant and minable.

University of Pittsburgh, 2010. Assistant Professor. Our group focuses on the rapidly growing area of cellular mechanotransduction; more specifically, the role that mechanical forces play in regulating cellular function. We are mainly interested in understanding the effects and molecular mechanisms by which a changing vascular stiffness modulates vascular smooth muscle cell (VSMC) biology and mechanics observed in many types of pathologies, such as those seen in vascular and cardiovascular diseases. We have shown that a stiff microenvironment contributes to pathological cell behaviors such as increased cell stiffness, proliferation, and motility in VSMCs. Our areas of biomechanical expertise include atomic force microscopy cells, biomaterials, and tissues, and the use of engineered stiffness-tunable 2D hydrogels and 3D scaffolds to model physiological and pathological vessel stiffness in vitro. Our group is also interested in developing and optimizing nanophotonic and optoneuronal platforms to optically control and monitor neuronal activity and networks of individual neurons, using 2D and 3D models of the human cerebral and vascular organoids.