DESIGNED · DEVELOPED · ANALYZED DR· SRINIVAS V KODURU · PhD Dr.K RESEARCH & INNOVATION
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Research Domain Distribution

// About Me

From Bench to Bedside — The Story

Expertise Proficiency
// Career Journey

Research Experience

19 years spanning 5 institutions — from bench science to diagnostic innovation

// Impact

Leadership Impact

// Research Focus

Currently Working Projects

// AI Tooling Practice

Agentic AI & LLM-Assisted Development

Daily builder — Claude Code, Codex, LangChain, AutoGen, CrewAI, RAG

⚡ Claude Code builder Codex pair-programming LangChain · AutoGen · CrewAI RAG · Vector DBs MLOps · Locked-ML

I am an active daily builder of agentic AI solutions with Claude Code and Codex-style LLM-assisted development, shipping production artifacts into regulated clinical environments. Through this practice I have delivered Flask scientific web applications for diagnostic reporting and end-to-end ML pipelines (random forest, SVM, deep neural networks with SHAP interpretability) integrated into FDA / CLIA-grade automated clinical reporting. I build RAG-based literature workflows over PubMed, UniProt, TCGA, GEO, and ChEMBL, and agentic AI workflows on LangChain, AutoGen, and CrewAI for hypothesis generation, target prioritization, market-opportunity identification, and experimental-design support. I also automate regulatory-document generation aligned to FDA 510(k) preparation and ship data-visualization dashboards to clinical and research stakeholders. The convergence of life-sciences AI/ML, regulatory rigor (CLIA / CAP / NYSDOH / 21 CFR Part 820 / IVDR), and agentic development methodology is the operating model I bring to every pharma R&D, biotech, or precision-medicine engagement.

Flask Diagnostic Dashboards
Production-grade Flask web applications surfacing locked-ML classifier outputs (classification score, confidence interval, actionable recommendation) per patient sample, integrated with CLIA / CAP-compliant chain-of-custody.
Locked-ML Classifier Deployment
Frozen / locked ML models (random forest, SVM, deep neural networks, SHAP interpretability) deployed under FDA / CLIA-regulated automated clinical reporting infrastructure. The highest-bar version of MLOps in healthcare.
Agentic AI Workflows
LangChain, AutoGen, and CrewAI agents built for hypothesis generation, literature synthesis, target prioritization, and experimental-design support — reasoning, planning, tool execution, and iterative refinement in scientific domains.
RAG over Scientific Databases
Vector-embedding + retrieval pipelines over PubMed, UniProt, TCGA, GEO, ENCODE, ChEMBL. Agent skills designed for molecular biology, genomics, biomarker discovery, and competitive-intel research.
AI-Assisted Regulatory Documentation
Automated drafting and assembly of regulatory documentation packages for FDA 510(k), CLIA, CAP, and NYSDOH CLEP submissions — SOPs, validation protocols, analytical / clinical performance documentation, QMS documents.
Codex Pair-Programming Practice
LLM-assisted development used to translate research-grade workflows into production-grade pipelines — Python, R, Nextflow, AWS, HPC, Docker. A core part of my daily build process.
Claude Code Codex LangChain AutoGen CrewAI RAG / Vector DBs Flask PyTorch TensorFlow scikit-learn SHAP Python R / Bioconductor Nextflow AWS Docker Git / GitHub MLOps Locked-ML FDA 510(k) CLIA / CAP / NYSDOH 21 CFR Part 820 ISO 13485 CLSI EP05/06/07/17
// Regulated Lab & Quality

CLIA · CAP · ISO · NYSDOH · GMP

Biomarker development, FDA & regulatory document preparation, and GMP quality — from bench to regulatory dossier

// AI/ML Showcase

Cancer Prediction Models

Transcriptomics & Advanced Data Analysis

Decision Tree — Gene-Based Cancer Classification
Deep Neural Network — Multi-Layer Classifier

AI/ML Pipeline Architecture

Per-Sample Classification Dashboard

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ROC/AUC with 95% CI
All five cancer panels separate cases from controls with AUC 0.96–0.99; the PROSTest panel leads at 0.994.
2D ivis Embedding
Clear cluster separation confirms distinct molecular signatures between disease states.
Multimetric Radar — Model Comparison
The neural network leads on all six metrics (AUC 0.997), ahead of XGBoost and Random Forest.
SHAP Waterfall Plot
SHAP values reveal which genes drive each prediction, ensuring clinical interpretability.
Threshold — Sensitivity Analysis
Moving the decision threshold trades sensitivity for specificity; the dashed line marks the balanced operating point (≈ 0.48).
Model Calibration Curve
Well-calibrated predictions ensure reported confidence scores align with actual outcomes.
LOOCV Performance Comparison
Confusion Matrix — Cancer Test Model
Gene Expression Profiles Across Cancer Types

Gene Expression Bubble Matrix — Multi-Cancer Panel

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High
Oncogene Tumor Suppressor Signaling
// Expertise

Technical Skills

// Peer-Reviewed Publications

Peer-reviewed Publications

View All on Google Scholar View All on PubMed
// Conference Presentations

Peer-Reviewed Abstracts

// Intellectual Property

Patents

// Education

Academic Background

// Contact

Get In Touch

Let's Collaborate

Interested in biomarker development, AI/ML diagnostic models, NGS data analysis, or transcriptomics consulting? I'm open to collaborations, consulting opportunities, and research partnerships.

// Global Reach

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