Profile

varshith vishnu

9959658000cat@gmail.com · +917780418552 · vishnu-varshith-portfolio.lovable.app · urnextdoor.com/users/9959658000cat · github.com/Vishnuvarshith91

Education

Annamacharya institute of science and technology rajampet - B. Tech, AI&ML
2022 – 2026
Rajampet · Grade: 8.56

Projects

SENTINEL CONSOLE — Multi-Agent AI Order Supervisor github.com/Vishnuvarshith91/Sentinel-Console.git
  • Architected a durable, hierarchical multi-agent system — a TinyLlama classifier paired with a
  • Qwen2.5 supervisor — orchestrated via Temporal workflows to autonomously monitor and resolve
  • order-lifecycle events end-to-end.
  • Engineered a deterministic rule-based fallback layer guaranteeing 100% demo uptime during LLM
  • outages, plus a live Next.js ops dashboard streaming workflow state, timelines, and agent memory in
  • real time.
CARDIODIAGNOSE — Cardiovascular Risk Prediction github.com/Vishnuvarshith91/CardioDiagnoise.git
  • Built a clinical ML pipeline on 12 patient-vital features; CatBoost and LightGBM hit 91.6%/92.4%
  • accuracy at 12ms/8ms inference — production-ready for real-time decision support.
  • Integrated SHAP explainability into a Streamlit dashboard, letting physicians trace risk scores to
  • individual biomarkers and boosting trust in simulated clinical trials.
HYBRID RAG SYSTEM — Document Question Answering github.com/Vishnuvarshith91/Hybrid-RAG-Model.git
  • Architected a hybrid retrieval pipeline (FAISS dense + BM25 sparse + neural re-ranking), cutting
  • hallucination rate ~30% vs. naive RAG on 50-page benchmarks.
  • Deployed a fully offline local-LLM backend via Ollama, achieving sub-2s end-to-end responses on an
  • 8GB consumer machine.
YOLO POTHOLE DETECTION — Real-Time Road Defect Detection github.com/Vishnuvarshith91/YOLO26-Pothole-Detection.git
  • Fine-tuned YOLO on a 2,000-image custom dataset, reaching 0.7823 mAP at 30 FPS; shipped as a live
  • Streamlit app for road-maintenance teams.
  • Fused RoBERTa + VADER text sentiment with facial-expression CNN into one Flask REST API,
  • delivering real-time cross-modal emotion scores at 84% accuracy.

Publications

CardioDiagnose: A Custom Machine Learning Model for Real-Time Cardiac Risk Diagnosis Using CatBoost, LightGBM, and Deep Learning
Catalyst Convene - Kolkata with International Conference of Reminiscent Research on Artificial Intelligence (ICRRAI 2K25) · Sep 27 2025

Languages

  • English - Fluent