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Data Scientist (Bioinformatics)

IVIVT Non-Clinical Safety GSK

Enhanced spatial transcriptomics data analysis, facilitated integration of Spatial transcriptomics data, and developed machine learning models for biomarker identification.

• Developed neural network models for liver carcinogenicity prediction
• Developed an interactive analytics and visualization platform

Computational Research Assistant

Invenio Lab Penn Medicine

Led analytical studies on COVID-19, contributing to four peer-reviewed publications.

• Conducted data analysis on serum and urinary biomarkers
• Developed scripts for data ingestion and analysis

Research Assistant

Zhou Lab Children's Hospital of Philadelphia

Contributed to the development of the ‘SeSAMe’ package for DNA methylation data analysis.

• Developed tests for package functionalities

B.S. & M.S.

Drexel University

Biomedical Engineering

• Bioinformatics
• Neuroengineering
• Bioimaging

A disturbed balance between blood complement protective factors (FH, ApoE) and common pathway effectors (C5a, TCC) in acute COVID-19 and during convalesce.

Scientific Reports

Persistent Depletion of Neuroprotective Factors Accompanies Neuroinflammatory, Neurodegenerative, and Vascular Remodeling Spectra in Serum Three Months after Non-Emergent Cardiac Surgery.

Biomedicines

Unbiased analysis of temporal changes in immune serum markers in acute COVID-19 infection with emphasis on organ failure, anti-viral treatment, and demographic characteristics.

Frontiers in Immunology

Dynamic changes in central and peripheral neuro-injury vs. Neuroprotective serum markers in COVID-19 are modulated by different types of anti-viral treatments but do not affect the incidence of late and early strokes.

Biomedicines

Longitudinal urinary biomarkers of immunological activation in covid-19 patients without clinically apparent kidney disease versus acute and chronic failure.

Scientific Reports

Meddibia

• Led a cross-disciplinary team to win a $5000 prize at the Philly CodeFest hackathon.
• Engineering ML pipelines for fine-tuning pre-trained neural networks to detect and classify skin lesions.
• Developed a natural language chat interface using GPT-3 to extract input features for a disease prediction model.

MLGO: Machine Learning for Predicting GO Enrichment

• Developed sophisticated pre-processing pipelines to perform differential expression analysis on over 11,000 RNA sequencing datasets.
• Developed and optimized an autoencoder for reconstructing fold-chaange values computed across datasets.

HESI GTTC Professional Development Award

Awarded a $1000 professional development grant.

Philly CodeFest Collaborative Team Award

Awarded for collaborative team efforts in developing Meddibia during Philly CodeFest.