A New Chapter

Designed relational database schema with key, constraints and normalization, optimizing hospital data management and query efficiency. Implemented complex SQL queries using joins, subqueries, aggregations (GROUP BY, HAVING), and OLAP functions (ROLLUP, CUBE, RANK) to extract and analyze hospital data.
Optimized relational database schema by applying normalization techniques, indexing, and constraints to improve query efficiency and data integrity

Developed triggers and stored procedures to automate hospital appointment scheduling, enforce business rules (doctor availability, valid time slots), and track historical changes through an audit log system.
Created user-defined functions (UDFs) to validate doctor-hospital relationships and determine the next available appointment slot, optimizing scheduling workflows and preventing conflicts.

Designed and implemented a distributed database system for a globally expanding food delivery platform by applying horizontal and vertical fragmentation, ensuring efficient data allocation across North America, Europe, and Asia.
Optimized database scalability and availability by implementing data replication, partitioning strategies, and Amazon RDS-based multi-region replication, reducing query latency and ensuring fault tolerance.

Developed a cloud-based pipeline using Azure Data Factory to extract, transform and load structured and semi-structured

data into Synapse Analytics. Integrated Blob Storage for retrieval of large datasets while optimizing storage costs.

Enhanced query performance and business intelligence by optimizing Azure Synapse SQL Pools with partitioning, indexing

and materialized views, reducing execution time by 40%. Integrated Tableau for real-time analytics, enabling advanced reporting and data-driven decision-making.

Worked with different teams, including business leaders and product owners, to gather and understand requirements for the AI-Powered Food Inventory Management System. Created clear user stories and acceptance criteria that aimed to reduce food waste from 18% to 10%, potentially saving Boston University approximately $1.3 million annually. Coordinated closely with the development and quality assurance teams during project meetings—like planning sessions and daily check-ins—using Azure DevOps to manage tasks and track progress, while helping to improve software delivery processes, contributing to a projected payback period of 3.9 years for the project.

Implemented and automated data operations to Extract, Transform & Load (ETL) an unstructured large 4GB taxi database with 15 million rows of data after data cleaning and validating, into a staging table first and then structured relational database using DDL to eventually create a Data Mart (Dimensional Data) for Online Analytical Processing.

Achieved increase in query performance by applying PL/SQL and T-SQL to create partitions, stored procedures, window functions and CTE to substantially tune and reduce query processing time by 4 to 6 seconds.

Employed Naïve Bayesian, Boruta, and CFS feature selection techniques on a large dataset of 300 dependent variables, post preprocessing to ensure and consistency of the data before applying 6 different machine learning classifiers in R. Applied hyper-parameter tuning to classifiers to achieve a True Positive Rate (TPR) exceeding 80% in predictive modeling, showcasing exceptional proficiency in handling imbalanced raw data within a comprehensive data mining framework.

Developed and implemented methodologies for optimizing portfolios, using python, utilizing dividend yield and market volume weighting, which improved risk-adjusted returns by around 15% over a 5 year back testing period.Created a more precise forecasting model with a 20% decrease in portfolio variance by incorporating time series analysis for over 100 tickers`, optimizing portfolio rebalancing strategies and designing an extensive stock data analysis pipeline.

This innovative database design project was tailored to streamline and enhance the user experience in booking hotels and stays. With a focus on user-friendly interfaces and efficient data management, our system aims to simplify the process of finding and reserving accommodations for a relaxing staycation. Through meticulous design and optimization, "BookMyStaycation" promises a seamless platform, empowering users to discover their ideal getaway destinations effortlessly.

I used Python to use the concepts I learned in the Information Structures course to develop this straightforward but engaging game. This game had an obstacle course tailored to the age group chosen, as well as three stages for three important age groups. The hardest part of this job was not using any libraries at all, even though I had done some quite advanced coding that included using machine learning. Everything had to be coded from scratch.