Shraddha Piparia, Ph.D.

Computational Genomics & Machine Learning

Machine learning for pharmacogenomics and patient stratification

I am a computational biologist with a background in computer science, currently working at the intersection of machine learning and genomics. My research focuses on understanding disease heterogeneity and treatment response using genotype and molecular data, with applications in asthma. I am particularly interested in learning meaningful representations from high-dimensional biological data and in building models that are robust, interpretable, and clinically relevant. Across projects, my goal is to better understand why patients with the same diagnosis can experience different outcomes and how computational methods can help uncover that structure.

Experience

Postdoctoral Scientist — University of California San Diego, USA (Nov 2021 – Present)

Computational genomics and pharmacogenomics research focused on disease heterogeneity, patient stratification, and treatment response modeling in asthma and immune-mediated disease.

Developed machine learning pipelines for large-scale genotype, transcriptomic, and clinical datasets.
Built representation learning models (VAEs) to learn latent genotype embeddings while accounting for ancestry confounding.
Performed endotype analysis to characterize molecular and clinical heterogeneity in asthma, including inhaled corticosteroid (ICS) response.
Designed reproducible workflows (PLINK, R, Python, HPC environments) for large-scale genomic analysis.
Collaborated with clinicians and domain scientists to translate statistical findings into biologically interpretable insights.

Application Developer — Oracle India Private Limited®, Telangana, India (Jun 2013 – Jul 2016)

Software engineer working on Oracle Cloud Enterprise applications (Oracle Project Portfolio Management).

Designed and implemented production features for Oracle Cloud enterprise applications.
Developed Build Verification Tests (BVTs) to improve release reliability and software quality.
Built a machine learning–based sentiment analysis tool using enterprise communication data (Oracle Social Network) to improve search relevance and project outcome prediction.

Education

Ph.D., Computer Science — University of North Texas, USA

Focus: Machine learning and probabilistic modeling for discovering structure in complex, event-driven data (CRFs, neural sequence models).