🧮 Mathematics & Computing · 🤖 AI/ML · 🔬 Scientific Computing · ⚙️ Software Engineering
🎓 B.Tech @ NIT Warangal · Class of 2029
I'm a Mathematics & Computing student who likes turning math, data, and ambitious ideas into working software. I explore machine learning, scientific simulation, developer tools, and systems programming—usually by building something, breaking it, and figuring out why. 😄
- 🔭 Working on: AI/ML projects, scientific software, and open-source tools
- 🧠 Interested in: machine learning, algorithms, probability, and systems engineering
- 🧪 My approach: build a baseline → run experiments → measure results → improve
- 🌱 Currently exploring: model evaluation, transformers, cyclone-track forecasting, and Rust
- 🤝 Open to: thoughtful collaborations, research discussions, and open-source contributions
💡 I like understanding how things work under the hood—not just making them work once.
An open-source, community-driven collection of reusable AI skills, prompts, workflows, agent definitions, templates, and integrations. The goal: make useful resources for AI-assisted development easier to discover, reuse, and improve.
A research prototype for exploring North Indian Ocean tropical cyclones. Includes historical track data, statistical forecasting baselines, held-out-season evaluation, and a FastAPI service. Neural forecasting models are still untrained, so implemented results and planned work are kept clearly separate.
✨ Lumex AI
An AI and data-focused project exploring ways to turn complex datasets into useful analysis and insights.
🦀 Rustalis
An experimental Rust-first operating-system project targeting x86_64. Work includes the no_std kernel foundation, bootloader integration, serial logging, and a QEMU development workflow—with kernel subsystems growing step by step.
📚 Saarthi
An academic resource platform that organizes learning materials by department, semester, and course to make student resources easier to find.
Explore my other work in machine learning, dimensionality reduction, numerical methods, simulation, and algorithms. There are always more things to learn and build!
| 🧩 Area | 🔧 Tools & topics |
|---|---|
| Programming | Python · C++ · JavaScript · TypeScript · Rust |
| Machine learning | PyTorch · TensorFlow/Keras · scikit-learn · model evaluation |
| Data & scientific computing | NumPy · pandas · SciPy · Matplotlib · MATLAB · Jupyter |
| Backend & web | FastAPI · Node.js · Express · React |
| Developer workflow | Git · GitHub · Linux · testing · reproducible experiments |
| Foundations | Probability · statistics · linear algebra · algorithms · numerical methods |
- 🧱 Start simple: establish a baseline before adding complexity.
- 📏 Measure, don't guess: separate experimental results from assumptions.
- 🧼 Keep it understandable: readable code and useful documentation matter.
- 🔁 Iterate: test, learn, improve, repeat.
- 🌍 Share the journey: learn in public and welcome constructive feedback.
Have an interesting idea, a tricky bug, or a project that could use another pair of eyes? I'd be happy to chat. 👀
- 💼 LinkedIn: rishi-gupta-85bab0232
- 📧 Email: cellrishi@gmail.com
- 🐙 GitHub: @cellrishi-code
🚀 Building things, asking questions, and learning one commit at a time.