class ArunBoddapati(BioinformaticsScientist):
def __init__(self):
self.role = "Independent AI Strategy Consultant & Systems Architect"
self.experience = "9 years · computational genomics → clinical impact"
self.education = ["MS Bioinformatics, Indiana University",
"MS Biomedical Sciences, Symbiosis University"]
self.focus = ["Multi-Omics", "Immunology", "Infectious Disease",
"Rare Disease Diagnostics", "GenAI for Biology"]
self.currently = "Building RAG + agentic AI for variant interpretation"
def mission(self):
return "Transform biological complexity into computational clarity."- Independent AI Strategy Consultant & Systems Architect — building agentic AI tooling, advising clinical-AI startups, and leading proteomics research across concurrent roles
- Strategy Advisor to Mamidi Health — led the commercial launch of AIVA, an AI variant-interpretation platform cutting genetic diagnostics from weeks → minutes
- Designed an LLM prompt-engineering framework at CDC that cut Nextflow pipeline development time by 20–80%
- Benchmarked AIVA's RAG-enabled variant interpretation at a 94% case-solve rate and 80.5% F1 vs. Exomiser, LIRICAL, BIAS-2015 & InterVar
- Co-authored 15+ peer-reviewed papers in Cell, Immunity, and Science Advances
- Top 8 of 80+ teams worldwide in WHO's global mock Mpox response
| Cell · Immunity · Science Advances | 94% case-solve · 80.5% F1 · RAG | CDC SARS-CoV-2 wastewater pipeline | LLM-driven pipeline automation |
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AI / Machine Learning
Languages
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Pipelines & Infrastructure
Analysis & Reporting
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Multi-Omics & Genomics
| Area | What I do |
|---|---|
| Transcriptomics | Bulk & Iso-Seq RNA-seq, single-cell, spatial, CITE-seq, novel transcripts, alt-splicing, RBPs |
| Genomics | Whole genome / exome sequencing, variant calling & ACMG interpretation |
| Proteomics | Mass spec, targeted NULISA-Seq & Olink panels |
| Metagenomics | Variant analysis, molecular epidemiology, phylogenetics |
| Epigenomics | ChIP-seq, MeRIP-seq |
AI / ML for Life Sciences
- LLM workflow design — prompt-engineering frameworks for automated pipeline generation
- RAG architectures — vector embeddings + semantic search for variant interpretation (AIVA)
- Agentic systems — MCP-based tool-calling, multi-agent pipeline builders
- Deep learning — autoencoders for biomarker discovery (silicosis risk, 80% accuracy)
- Model evaluation — feature selection & validation for domain-specific biomedical tasks
Leadership & Mentorship
- Led cross-functional teams of 3–7 FTEs across bioinformatics, data engineering & translational science
- Mentored junior scientists and CDC trainees via monthly Nextflow + cloud engineering workshops
- Delivered $100K+ cost savings and 20–80% efficiency gains through AI-driven automation
- Stakeholder management & technical roadmap development with senior/executive leadership
|
AI-native, end-to-end Nextflow pipeline builder — a team of specialised LLM agents (Claude, Gemini, litellm-compatible).
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Intelligent validation tool blending LLMs with rule-based static analysis for nf-core & custom Nextflow compliance.
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RAG-based AI agent that analyzes Nextflow pipelines for nf-core guideline compliance.
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CDC Nextflow pipeline for national SARS-CoV-2 wastewater surveillance & reporting.
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Interactive ML dashboard with example neural networks for hands-on learning.
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My personal site — bioinformatics, AI, and the work behind it.
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15+ peer-reviewed publications — a few highlights:
- Dulski J, Boddapati AK, et al. (2025). Targeted plasma proteomics uncover proteins associated with KIF5A-linked SPG10 and ALS spectrum disorders. HGG Advances.
- Hoang TN, Boddapati AK, et al. (2021). Baricitinib resolves lower-airway macrophage inflammation in SARS-CoV-2-infected rhesus macaques. Cell.
- Routhu NK, incl. Boddapati AK (2021). A modified vaccinia Ankara vector-based vaccine protects macaques from SARS-CoV-2. Immunity.
- Holla P, incl. Boddapati AK (2020). Shared transcriptional profiles of atypical B cells in malaria, HIV, and autoimmunity. Science Advances.
"Transforming biological complexity into computational clarity"