Finalist of a 30-hour offline hackathon held at Thadomal Shahani College Of Engineering, Bandra, Mumbai.

Participated in Xylem Ignite International Hackathon, collaborating with diverse teams to solve real global challenges.

Participated in Devignite, Navi Mumbai's first offline hackathon organized by Smt. Indira Gandhi College of Engineering.

Secured 5th prize at Anveshna Mumbai 2023, sponsored by Synopsys.

ElderConnect was selected as a finalist project at HACKX 2023, organized by S.F. Institute of Technology.

Won the international project competition at PICT, Pune (Impetus and Concepts) with trophy, certificate, cash prize, and vouchers.

First prize in the project competition at Loknete Gopinath Munde College of Engineering, Nashik.

Participated in the divisional level project competition organized by IEEE Bombay at Godavari College of Engineering, Jalgaon.

Python pipeline ingesting sensor and operational data with time-series alignment and rolling, rate-of-change and threshold features. Predictive and anomaly-detection models trained on chronological splits to avoid leakage, improving early issue detection by 16%.

RAG pipeline over engineering documents: text extraction and cleaning, overlapping chunking, embeddings, vector index with metadata, similarity search, and response validation against source context to limit unsupported answers.

Reproducible training pipeline with MLflow tracking and model registry, Dockerized inference and CI/CD. Production monitoring with drift detection against the training baseline, threshold-triggered retraining, and promotion only after offline validation.

Python-based AI project built for Interface.ai, applying machine learning and LLM techniques to a real-world conversational AI use case.

Real-time ASL recognition system using VideoMAE, PyTorch and VideoAug on a 20GB dataset with 239 signs. Achieved ~98% classification accuracy and built a multi-sign inference pipeline for long-form videos.

Implemented a GPT-style LLM with custom BPE tokenization, multi-head attention, sampling (temperature, top-k, top-p), gradient clipping and a full training pipeline.

Full MLOps pipeline for phishing / network threat detection with data ingestion, validation, transformation, training, MLflow tracking, batch prediction, Docker, CI/CD (GitHub Actions) and AWS (S3 + EC2) deployment.

Machine learning system to detect fraudulent credit card transactions, handling highly imbalanced transaction data with feature engineering and classification models.

Production-style abstractive text summarization system using Hugging Face Transformers (T5/BART), with modular YAML configs, logging, and a stage-wise NLP pipeline.

Cold email automation tool using Groq, LangChain and Streamlit. Scrapes a company’s careers page, extracts job listings and generates personalized cold emails with portfolio links from a vector DB.

End-to-end classification pipeline to predict mobile price category using EDA, preprocessing, model training and a SageMaker-ready script for cloud training and deployment.

Speech emotion recognition model that classifies voice recordings into seven emotions: anger, disgust, fear, happiness, pleasant surprise, sadness and neutral, using audio feature extraction and deep learning.

Built a self-driving car model with behavioral cloning, road sign detection and real-time decision-making using CNNs, TensorFlow/Keras, Flask, Socket.IO and Udacity’s open-source simulator.

Cross-platform Flutter (Dart) application built as a hackathon solution to support people with neurological and mental-health needs through accessible, AI-assisted features.

Platform to connect elders with volunteers via one-to-one and group video calls, emotion recognition with AI, and event scheduling to reduce loneliness and improve engagement for elders.

Flutter (Dart) mobile app that helps students explore career paths, opportunities and guidance resources in one place.

Hands-on notebooks implementing neural networks and generative AI concepts, from feed-forward and deep networks to generative models.

Practice repository covering MLOps workflows: experiment tracking, pipeline structuring, packaging, and deployment of machine learning models.

Collection of Jupyter notebooks covering core machine learning algorithms, preprocessing and model evaluation, written as a practical reference.

Implementations of core data structures and algorithms in Python, including linked lists, stacks, queues, trees and graphs.

Python solutions to LeetCode problems covering arrays, strings, dynamic programming, graphs and more, for interview preparation.