I'm a Computer Science graduate with a strong foundation in algorithmic thinking — built through years of competitive programming — and hands-on experience building full-stack web applications using the MERN stack.
My current focus is the intersection of computation and cognitive science: understanding how machine learning systems can model intelligent behavior, and applying that curiosity toward research.
- 🎓 B.Tech in Computer Science & Engineering
- 💡 Interests: Machine Learning, Cognitive Science, Human-AI Interaction, NLP
- 🔬 Actively preparing for research-oriented programs in AI/ML/CogSci
My logical foundation. 1,000+ problems taught me how to break any complex problem into sub-problems — the core skill of an engineer and a researcher.
| Platform | Problems Solved | Focus Areas |
|---|---|---|
| LeetCode | 330+ | 170+ Medium, 35+ Hard — Arrays, Trees, Graphs, DP |
| Codeforces | 590+ | Graphs, Dynamic Programming, Number Theory |
| Combined | 1,000+ | Consistent solving over 3 years |
Core algorithm areas:
- Graph Algorithms — BFS, DFS, Dijkstra, Topological Sort
- Dynamic Programming — Knapsack, Bitmask DP, Interval DP
- Data Structures — Segment Trees, Fenwick Trees, Disjoint Sets
- Number Theory & Combinatorics
🔗 LeetCode Profile · Codeforces Profile
Languages │ C++ · JavaScript · Python · SQL
Frontend │ React.js · HTML5 · CSS3 · Tailwind CSS
Backend │ Node.js · Express.js · REST APIs
Databases │ MongoDB · MySQL
Tools & Workflow │ Git · GitHub · Postman · VS Code
CS Foundations │ Data Structures & Algorithms · OOP · OS · DBMS · Computer Networks
Exploring │ Machine Learning · scikit-learn · NumPy · Pandas
My path has been a deliberate progression:
Phase 1 — Logic first: Competitive programming gave me the discipline to reason under constraints and decompose problems systematically. Solving 1,000+ problems isn't a hobby — it's a method of thinking.
Phase 2 — Logic applied: Moving to full-stack development showed me how algorithms live inside real systems. Building production-grade apps taught me about tradeoffs, scalability, and design.
Phase 3 — Deeper questions: Now I want to ask why — Why do certain computational structures resemble how humans reason? How can we build AI systems that don't just predict, but understand? These questions drive my interest in Cognitive Science and Machine Intelligence research.