Senior translational scientist bridging in-vivo oncology, pancreatic cancer biology, and preclinical drug development — from mechanism to medicine.
I lead translational research at the intersection of metabolism, immunology, and cancer — designing the in-vivo models and screening programs that move promising molecules toward the clinic.
I'm a senior scientist with deep expertise in in-vivo drug screening, solid tumor oncology, and preclinical efficacy studies. My career has centered on the hardest questions in pancreatic cancer — how obesity, chronic stress, and inflammation converge to drive tumor initiation and progression.
As co-head of the Pancreatic Cancer Translational Laboratory at UCLA, I combine sophisticated murine models with computational biology and digital pathology to identify actionable targets and repurpose approved drugs as chemo-preventive agents. Earlier, I played pivotal roles developing the anti-metastatic compound Metarrestin and the immunomodulatory peptide RP-182.
Complex murine model generation (inducible KrasG12D), high-throughput phenotypic screening, target deconvolution, flow cytometry, and tumor microenvironment modulation.
Python and R for genomic/transcriptomic analysis of FASTQ sequencing data, statistical computing, experimental modeling, and data visualization — accelerated with cloud and AI tooling.
Advanced QuPath workflows for quantitative digital pathology, automated IHC quantification, and biomarker evaluation (e.g., GLP1R).
My research program spans the biology of pancreatic cancer, the development of first-in-class therapeutics, and the computational tools that make discovery faster.
Investigating how diet-induced obesity and chronic stress converge on CREB phosphorylation to accelerate pancreatic cancer — and how approved drugs like metformin, statins, and beta-blockers might interrupt early carcinogenesis.
Preclinical development, screening, and target deconvolution of novel oncology compounds — including the anti-metastatic agent Metarrestin (ML-246) and the CD206-targeting host-defense peptide RP-182.
Characterizing macrophage-driven acinar-to-ductal metaplasia and inflammatory cytokine production to uncover immunotherapeutic vulnerabilities and reshape the pancreatic tumor microenvironment.
Beyond wet-lab research, I design and build computational tools — native applications and interpretable machine-learning pipelines that turn raw biomedical data into decisions.
A Mac app for studying pancreatic pathology slides. It lets you view huge microscope images, mark up regions by hand, and train AI models that learn to spot early pancreatic lesions (PaNIN) on their own.
One simple, fast method for reading medical signals. It breaks any waveform into its up-and-down segments, turning the shape into features a computer can learn from — applied to two problems: reading heart tracings (ECG) and spotting seizures in brain activity (EEG).
Research and decision-support work — not a medical device.
From the National Cancer Institute to Harvard Medical School and UCLA — a career built on translational rigor.
Published in Science Translational Medicine, Molecular Cancer Research, Scientific Reports, and more.
Full author record available on PubMed.
Awarded to compare acinar- and ductal-cell-driven PDAC development promoted by obesity — using inducible KrasG12D models to identify markers for patient stratification and prevention.
Principal InvestigatorOpen to research collaborations, preclinical study partnerships, and scientific advisory conversations in oncology.