Computer Science student building practical software across full-stack development, NLP, computer vision, and intelligent applications. Focused on robust system architectures, deterministic data extraction pipelines, and frequency-domain algorithms.
A focused look into my academic background, technical philosophy, and core strengths.
I am currently pursuing my B.Tech in Computer Science and Engineering (Core) at Vellore Institute of Technology (VIT Vellore). My work centers on creating high-performance full-stack applications and integrating applied computer vision with natural language processing heuristics.
Rather than relying on generic boilerplate templates, I focus on building systems with depth — from engineered multi-format document pipelines using PyMuPDF & Tesseract OCR to frequency-domain digital steganography with Discrete Cosine Transforms (DCT) and Huffman Compression.
My technology stack spans Python, React, TypeScript, FastAPI, Flask, relational database design with MySQL / SQL, and cloud fundamentals on AWS (EC2 & S3).
Flagship full-stack and algorithmic projects engineered from first principles. Explore interactive live simulations and architectural breakdowns.
A categorized inventory of languages, frameworks, cloud primitives, and computer science fundamentals.
A disciplined, four-stage engineering lifecycle applied to every system from concept to deployment.
Break down the problem, study edge cases, identify core technical requirements, and define expected inputs and failure boundaries.
Choose an appropriate architecture, plan data flows, evaluate algorithmic time/space trade-offs, and select the right technology stack.
Develop the application with clean, modular, and maintainable implementation. Emphasize type safety, clean separation, and clear APIs.
Test edge cases with unit suites, debug bottlenecks, measure performance, improve usability, and iterate continuously.
Active practice in data structures, arrays, strings, and computational complexity.
Practicing programming problems across CodeChef and HackerRank. Building strong foundations in data structures, space/time complexity analysis, array manipulations, and string tokenization patterns.
# Huffman Binary Tree Construction for StegoLab
import heapq
from collections import Counter
def build_huffman_tree(text: str) -> dict:
if not text:
return {}
# 1. Frequency calculation
freq = Counter(text)
heap = [[weight, [char, ""]] for char, weight in freq.items()]
heapq.heapify(heap)
# 2. Iterative node reduction
while len(heap) > 1:
lo = heapq.heappop(heap)
hi = heapq.heappop(heap)
for pair in lo[1:]:
pair[1] = '0' + pair[1]
for pair in hi[1:]:
pair[1] = '1' + pair[1]
heapq.heappush(heap, [lo[0] + hi[0]] + lo[1:] + hi[1:])
return dict(heapq.heappop(heap)[1:])Formal academic training at VIT Vellore and professional Java certification.
Demonstrates proficiency in Java syntax, object-oriented principles, control structures, exception handling, and core standard library fundamentals.
Programming Languages: C, C++, Java, Python, TypeScript
Web Development: HTML5, CSS3, JavaScript, React JS, Tailwind CSS, Bootstrap, Flask, FastAPI
Databases: MySQL, SQL
Cloud: AWS (Basics – EC2, S3)
Computer Vision & NLP: OpenCV, Image Processing, NLP, OCR, Tesseract, PyTesseract, PyMuPDF
Concepts: Object-Oriented Programming, DBMS, Data Structures & Algorithms, Huffman Encoding, Steganography, REST APIs
Tools: Git, GitHub, VS Code, Vite, Pytest
Certifications: Java Foundations Associate – Oracle
Languages: English: C1 | Telugu: C2 | Hindi: C1 | German: A2