Data Engineer · Researcher · Chicago
Building data infrastructure that scales and research that matters.
I'm Chaitanya, a data engineer at Egen and a researcher in clinical AI and graph neural networks. I design ETL pipelines and cloud-based bioinformatics systems, publish research in medical AI, present at IEEE conferences, and maintain open-source Python packages including cloudfit.
Currently building
Open source
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2026
cloudfitCloud-agnostic machine type recommender for batch and bioinformatics workloads. Multi-package OSS ecosystem (scoring engine, GCP provider, FastAPI service, Gradio UI) with a multi-region bundled snapshot and a live demo on Hugging Face Spaces.
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2026
samplesheet-parserFormat-agnostic parser for Illumina
SampleSheet.csvfiles. Auto-detects IEM v1 vs. BCLConvert v2, validates index integrity with Hamming distance checks, and converts or merges sheets across mixed sequencing fleets.
Selected
Recent research
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2026
AI-Driven Early-Warning System to Predict Multi-Organ Deterioration in Critical Care Patients
Temporal deep learning with cross-organ attention to detect ICU deterioration a median of 6.2 hours before conventional clinical detection, across five organ systems. Presented at IEEE ICHI 2026, Session 16.
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2025
AI-driven drug repurposing: a graph neural network and self-supervised learning approach
Computational drug discovery using GNNs and self-supervised pretraining over biomedical knowledge graphs.
Selected
Writing
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2026
Why I built cloudfit
The gap between free cloud sizing tools (Compute Optimizer, GCP Recommender, Azure Advisor) and what I actually needed for new batch and bioinformatics workloads. Why I built an open-source recommender that doesn't need historical metrics from a running workload.
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2025
Understanding recommender systems: the engine behind personalized experiences
A primer on collaborative filtering, content-based, and hybrid approaches to recommendation. Why personalization engines work the way they do, and where they break.
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2021
An introduction to explainable AI and explainable boosting machines
A primer on XAI fundamentals and how EBMs combine accuracy with interpretability.
Let's build or research something together.
Open to conversations about data engineering, clinical AI, and cloud architecture. Also available for research collaboration, conference talks, and speaking invitations.