I’m Venkata Nivas Linga, a pre-final year B.Tech student in AI & Data Science at Vishnu Institute of Technology. I’m passionate about software development, data science, machine learning, and quantum computing, and I enjoy building projects that combine practical engineering with strong problem-solving.
I’ve worked on real-time control and edge-AI projects using Python, C, Raspberry Pi, and NVIDIA Jetson, and I’ve also explored Qiskit, forecasting, and full-stack development. Beyond projects, I actively practice DSA, participate in hackathons, and prepare for internships and competitive opportunities.
I like turning ideas into working systems, whether that means writing clean backend code, analyzing data, or experimenting with new tech. My goal is to grow as a strong software engineer while staying curious across AI, cloud, and emerging technologies.
- 🎓 Currently pursuing B.Tech in AI & Data Science @ Vishnu Institute of Technology (CGPA: 8.9)
- 💼 Former Software Developer Intern @ Vishnu Foundation TBI — built real-time control systems & edge-AI vision pipelines
- 🏆 Finalist, Smart India Hackathon 2025 — built QuMail, a quantum-secure email system using IBM Qiskit
- 🥇 7× Hackathon Winner (institute & external)
- 🧩 Solved 500+ DSA problems across LeetCode, CodeChef & GeeksForGeeks
- 🌱 Deep interest in LLMs, RAG pipelines, and applied Machine Learning
- 📫 Reach me at venkatanivaslinga@gmail.com
|
React.js · Node.js · MongoDB · WebRTC · OpenAI API Full-stack MERN platform with real-time chat/video via WebRTC supporting 200+ concurrent users at 99.5% uptime. Integrated Redis caching + load balancing (↓30% crashes, ↑45% load speed) and an OpenAI-powered recommendation engine (↑35% engagement, ↑25% session duration). CI/CD via GitHub Actions + Vercel cut release time by 90%. |
Python · Prophet · SARIMAX · FastAPI Built an end-to-end demand forecasting pipeline using Rossmann historical sales data, advanced time-series models, and a FastAPI inference layer for business-ready predictions. Focused on trend decomposition, holiday effects, seasonality, and deployment-friendly forecasting workflows. |
|
Python · Scikit-Learn · Pandas · NumPy Developed a machine learning classification pipeline for survival prediction with feature engineering, preprocessing, and model evaluation. Explored passenger demographics and travel attributes to improve predictive performance. |
NumPy · Pandas · Transformers · Qiskit High Performance Classical Detection: Fused Feature MLP Neural Network achieves 95.20% accuracy and 0.9872 ROC AUC on the HC3 dataset (170,898 text samples). Quantum Classifier Pipeline: Implements 4-qubit and 8-qubit Variational Quantum Circuits (VQC) and Quantum State Fidelity Kernel Classifiers (QSVC), reaching 0.7811 ROC AUC. |
May 2025 – Aug 2025 · Bhimavaram, India
- Built Python modules on Raspberry Pi for real-time control and data processing in smart dental equipment
- Deployed OpenCV-based face, mask, and object detection on NVIDIA Jetson Nano, Orion Nano, and AGX Orion for edge-AI TBI products
- Built a real-time object detection system using YOLOv3 + OpenCV, detecting 80 object classes from live video
| Certification | Issuer |
|---|---|
| OCI 2025 Certified Data Science Professional | Oracle |
| DBMS & R | NPTEL |
| Data Analytics & Machine Learning | HCLTech |
| Quantum Fundamentals & Quantum Algorithms | WISER |
| Software Engineer Intern | HackerRank |
- Co-Champion, Vishnu Student Success Centre — mentored students via academic and tech workshops
- Organizer, VFTBI — built the registration platform for a 2,000+ footprint event
