Differentiable optical lens simulator for end-to-end computational imaging.

Developed an end-to-end ML system on Azure to predict loan defaults, leveraging advanced data preprocessing, feature engineering, and machine learning models to optimize accuracy. This project includes a comprehensive suite of tools and techniques for robust financial risk assessment, deployed to enhance decision-making for high-risk exposures.

[AAAI 2022] Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semantics.

End-to-end PSF extraction for 3D microscopy

Deep Negative Volume Segmentation - automated 3D CT segmentation of body joints for dentistry

Supported Models: MobileNet [V1, V2, V3_Small, V3_Large] (Both 1D and 2D versions with DEMO, for Classification and Regression)

A Fully Automatic Method for Predicting Contact Maps of RNAs by Evolutionary Coupling Analysis

End-to-End ETL Pipeline for Film Data Crawling from Ohitv

End-to-end data engineering pipeline with real-time streaming, cloud processing, and analytics. Built with Apache Kafka, Spark, AWS Glue, and Snowflake using Apache Iceberg tables.

CI/CD pipeline to deploy Node.js app to AWS using Terraform, Ansible, and GitHub Actions. Fully automated deployment on every push to main branch

End-to-end Fraud Detection MLOps pipeline integrating MLflow, FastAPI, Streamlit, Docker, Kubernetes, Prometheus, and Grafana for real-time fraud prediction, experiment tracking, and monitoring.

This project uses machine learning to predict customer churn in the banking sector. It covers the end-to-end process, from data ingestion, validation, and transformation to model training and deployment using FastAPI. The system includes real-time predictions and provides an API for customer churn analysis.

Analyzed a multicategory e-commerce store using big data techniques on a Kaggle dataset with the help of AWS EC2, AWS S3, PySpark, AWS Glue ETL, AWS Athena, AWS CloudFormation, AWS Lambda and Power BI!

Detect U.S. housing market bubbles using macroeconomic signals. Forecast HPI, score speculative risk, and visualize insights using a fully modular, cloud-native GCP pipeline.

⚡ E-commerce Data Engine Processing 10,000+ records with a custom Python pipeline. Includes advanced data imputation and interactive Business KPI dashboards. 📈

This project is an end-to-end MLOps pipeline for a network security system that detects phishing and malicious activities using machine learning. It automates data ingestion, preprocessing, model training, and deployment while leveraging AWS S3 for model storage and GitHub Actions for CI/CD. The system includes realtime monitoring & a web interface

A cloud-based tool for sentiment analysis in reviews about restaurants on TripAdvisor

An end-to-end Business Intelligence (BI) pipeline designed to process and analyze 141 million IMDb records for deriving insights on movies, ratings, and global cinema trends. The project demonstrates large-scale data engineering, ELT automation, and dashboard-driven analytics.

This tutorial walks through the process of building an end-to-end service. It covers setting up a conda environment, creating functions, exposing it through an API, and running the API locally, how to dockerize the service using Dockerfile and docker-compose, and finally, how to access and interact with the containerized service.

This RAG (Retrieval-Augmented Generation) pipeline is used to enable intelligent question answering over unstructured receipt documents. It combines OCR, semantic search, and large language models to extract, store, and retrieve relevant information from receipts, allowing users to query their data using natural language.