My research investigates the intricate dynamics of endosomal trafficking and its essential role in maintaining cellular homeostasis and preventing a broad range of pathological conditions. To decode the “GPS-like” guidance systems that direct endocytic cargo, my lab utilizes quantitative data science methodologies, including high-content bio-image analysis and automated tracking to quantify organelle trajectories and spatial distribution. We integrate our experimental findings with computational frameworks to understand how ion transporters, specifically NHE9, modulate luminal pH to dictate cellular outcomes. By applying robust statistical inference and comparative pathway analysis to large-scale datasets, we aim to transform descriptive cell biology into a predictive understanding of endosomal dysfunction and identify novel molecular targets for therapeutic intervention.
Please describe one or two of your most interesting projects.
1. Mapping the Spatiochemical Signal of Endosomal Routing
This project focuses on quantifying how luminal pH acts as a critical spatiochemical signal that dictates the destination of endocytic cargo. By utilizing high-resolution live-cell imaging, optical tweezers, and automated particle tracking, we generate extensive datasets of organelle trajectories and force dynamics. We analyze these trajectories to correlate chemical fluctuations with cargo routing efficiency. Our work provides the precise quantitative biological parameters required to inform and validate computational models of intracellular transport, helping to define how cells maintain homeostasis through organized spatial distribution.
2. Predictive Modeling of the Tumor-Immune Secretory Interface
This research utilizes a data driven framework to quantify the non linear dynamics of cellular communication within the tumor microenvironment. The project integrates multi parametric datasets including intracellular chemical gradients and protein expression levels to map a 3D response surface of the immune tumor interface. By applying advanced computational modeling to these experimental inputs, the work identifies secretory thresholds where subtle physiological shifts trigger significant increases in suppressive vesicle output. This approach moves beyond traditional descriptive biology by employing sensitivity analysis to predict high leverage intervention points. The objective is to establish a computational roadmap for disrupting the education of immune cells by transforming qualitative biological observations into a predictive and targetable landscape for future therapeutic strategies.
What makes you excited about your data science and AI research?
I am most excited by the power of data science to transform complex biological behaviors into predictable, actionable frameworks. My work leverages high-content imaging and multi-parametric datasets to decode the spatiochemical signals that govern cellular transport and communication. I find it incredibly compelling to use computational modeling and sensitivity analysis to identify precise thresholds where subtle physiological shifts trigger significant functional changes. By turning qualitative observations into quantitative maps, we can uncover high-leverage intervention points that were previously invisible, moving us toward a truly predictive and targetable landscape for human health.