About Me_
I'm a builder. I lead the development of full-stack web platforms for real-world clients and craft ML/NLP models that solve complex problems — from legal case similarity to deepfake detection. Whether optimizing frontend performance or deploying transformer models, I execute fast and with purpose.
Projects_
🤖 ELI5 Bot
Explain complex topics at multiple levels using LLMs, Wikipedia, and voice I/O.
🎭 Deepfake Detector
Detect fake videos using MTCNN, ResNet50, and GRU-based video processing.
🌐 Website Projects
Production-grade websites for events, startups, and conferences.
⚖️ Legal Engine
Find and summarize similar legal cases using Doc2Vec and Transformers.
📊 HCT Survival Prediction
Bias-aware survival prediction using LightGBM and fairness techniques.
📈 Stock Predictor
Predict Real stock prices using Various ML Algos and real-time data.
🤖 J.A.R.V.I.S
Voice-enabled AI assistant powered by GPT-3 and speech APIs.
Skills_
Achievements_
🏆 Winner, SIH - Sep 2023
Led a team to develop a winning solution for real-world challenges, ensuring project alignment and timely delivery.
🏆 Winner, IRIS Hackathon - Feb 2024
Contributed to an innovative project—handled ideation, coding, and final presentation.
🏅 Winner, IH Internal Hackathon
Built a fully functional prototype in limited time with team collaboration.
🐍 Winner, Best Python Project (BTech CSE)
Won 1st place in the Python Project Competition by Dept. of Computer Science & Technology.
🧠 Technical Head, E-Cell
Led the technical wing, overseeing website launches and digital strategy.
🚀 Organizer, RIDE STARTUP EXPO 2024
Organized a university-level startup expo connecting founders, students, and investors.
🧑🚀 NASA Space Apps Challenge
Collaborated globally to solve space and Earth challenges using tech.
Publications_
Sentiment Analysis of Hazardous Events and Disasters
Applied NLP techniques to analyze Twitter data and understand public sentiment before, during, and after disasters. The Research addressed the gap in traditional disaster assessment by developing a sentiment analysis framework using lexicon-based tools and machine learning models (VADER, TextBlob, SVM, Naive Bayes). The goal was to provide real-time, actionable insights to aid disaster response and emotional well-being tracking.