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.
MULTIMODAL SENTIMENT ANALYSIS
github.com/Vishnuvarshith91/Sentimental-analyzer.git
- 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