Bioinformatics and Computational Modeling
June 2021 - Present | Woburn, MA
- Developed multi-omics analysis pipelines, enabling reproducibility and scalability for disease
characterization studies using public clinical datasets, refining MPS
chip models to capture critical disease phenotypes and stages.
- Engineered robust, scalable data pipelines with Nextflow and Python to efficiently process large-scale
datasets—enhancing reproducibility and accelerating downstream analyses for data-driven insights.
- Developed machine learning approaches and ODE-based mechanistic models to simulate drug kinetics in
Liver and multi-organ Chips, optimizing IVIVC and IVIVE outcomes for preclinical programs.
- Applied network inference and pathway-level analytics to interpret omics signatures and elucidate
mechanisms of action for metabolic and hepatic diseases.
- Developed a transcriptomic metric integrating clinical and in vitro data, supporting model development
and validation to improve pre-clinical to clinical translation by ensuring the presence of relevant
hallmarks.
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For more details, feel free to explore my
CV.
Bioinformatics Research Scientist @ GSK
Pre-Clinical Vaccines Lab
June 2020 - December 2020 | Rockville, Maryland
- Conducted single-cell transcriptomic analysis to investigate immunological response in a
self-amplifying mRNA vaccine platform
- Processed and analyzed long-read NGS data (PacBio) to characterize mRNA vaccine constructs, applying
tools like Minimap2 and SAMtools in a Linux-based analysis environment.
- Partnered with immunologists, molecular biologists, and biostatisticians to interpret large-scale
datasets, effectively merging computational insights with experimental validation.
- Enhanced reproducibility through rigorous data visualization and spike-in controls (ERCC standards),
resulting in high-fidelity gene expression profiles for downstream functional analyses.
Jan 2021 - May 2021 | Atlanta, GA
- Benchmarked multiple scRNA-seq tools for RNA velocity and gene
regulatory network inference, revealing tool-specific trade-offs in speed, sensitivity, and accuracy.
- Developed an end-to-end single-cell RNA-seq pipeline integrating quality control, normalization, and
advanced analytics, reducing processing time while enhancing data consistency.
- Identified critical limitations and best-practice guidelines for robust gene regulation inference,
driving methodological improvements and more interpretable results.
August 2019 - May 2020 | Atlanta, GA
- Developed and optimized an ETL pipeline to analyze the ribonucleotide incorporation in Saccharomyces
cerevisiae DNA, enabling comprehensive downstream analysis.
- Built a machine learning model leveraging the transformed dataset to predict the firing times of
autonomously replicating sites (ARS), providing novel insights into DNA replication processes.
- Collaborated with lab researchers to validate computational findings, bridging in silico predictions
with in vitro results for enhanced reproducibility.
- Piloted an iterative approach to refine the model's predictive accuracy, laying the groundwork for
future optimization in replication research.
