Bioinformatics · Machine Learning · Data Analytics · Cancer Genomics

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I work at the intersection of bioinformatics, machine learning, and data analytics — building computational tools to make sense of high-dimensional biological data, from single-cell transcriptomes to gene regulatory networks, in pursuit of a clearer picture of how disease develops.

I'm currently a Computational Biology Researcher at the Ontario Institute for Cancer Research (OICR), where I use single-cell transcriptomics to investigate the developmental origins of medulloblastoma, a pediatric brain cancer.

I recently graduated with a Honours Bachelor of Science at the University of Toronto, specializing in Bioinformatics and Computational Biology, with minors in Computer Science and Immunology.

• Languages: Python · R · SQL · Bash/Shell · Java · C · JavaScript
• Bioinformatics: Bulk & single-cell RNA-seq analysis · Differential expression · Genomic data analysis
• Data Analysis: pandas · NumPy · Excel · Power BI · Data cleaning & transformation · Data visualization
• Machine Learning: Supervised & unsupervised learning · Feature engineering · Classification · Clustering · Model evaluation
• Tools: Git · Docker · Linux/Unix · Jupyter

See the full list on my skills page →

SeedBench-Bio — Python, LLM Evaluation Benchmark testing whether prompt design, not just model choice, changes an LLM's error-detection recall and reasoning quality on seeded bioinformatics review tasks.

EEG Seizure Detection — Python, ML Signal-processing pipeline benchmarking models and engineered EEG features on pediatric seizure data, using leave-one-patient-out cross-validation to test whether performance holds on patients never seen in training.

Medicare Genomic Testing Analytics — SQL, Power BI, Excel, Python Business and healthcare analytics dashboard testing whether Medicare genomic testing utilization aligns with state-level cancer incidence, built on a two-round CPT/HCPCS crosswalk and a data quality investigation that traced a 10x state-level outlier to a CMS billing-location attribution artifact.

BMP7 Signaling in Medulloblastoma — R, Bioconductor, Cytoscape Recreated and extended published analysis of BMP7-driven oncogenic signaling: differential expression, GSEA, and pathway enrichment mapping from bulk RNA-seq data.

ExprCompareR — R, Shiny Interactive omics data reporting tool integrating RNA-seq and protein-expression datasets across human tissues for functional genomics and precision oncology research.

Pathogenic SNV Prediction Tool — Python, Bash, ML Machine learning classifier predicting pathogenic vs. benign SNVs using ClinVar labels, annotated across 13,000+ variants with regulatory genomic features.

See the full list on my projects page →

• Computational Genomics
• Machine Learning for Biology
• Single-Cell & Spatial Omics
• Sequence-to-Function Models
• Cancer Genomics & Brain Tumours
• Precision Medicine
• Tumour & Phenotype Characterization
• Stem Cell Biology
• Antibody Engineering

📫 [email protected] 💼 linkedin.com/in/tanaya-datar 🌐 tanayadatar.vercel.app