About me

Results-oriented ML/Data Engineer with over 6 years of experience developing machine learning models and scalable data pipelines.

Proven expertise in leveraging cloud services for optimized data processing and creating impactful dashboards. Known for collaborating with cross-functional teams, mentoring peers, and driving data strategy. I am dedicated to solving real-world problems using ML, focusing on search relevance, recommendations, fraud detection, and time-series forecasting. Experienced in microservice-based architecture, scalable AI applications, and secure software development.

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That's what I look like

Python

SQL

AWS

Databricks

Spark

Airflow


My work

Some of My Projects

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Tic Tac Toe

A basic two player TIC-TAC-TOE game board

A basic two player Tic-Tac-Toe game board developed as a side project.

Tech Used
  • HTML5/CSS3
  • javaScript
  • sweetalert
  • jQuery

  • Features
    The game is played on a 3x3 grid . The game is played between player one who plays "X" and player 2 who plays "O". Players take turns putting their marks on empty squares. The first player to get 3 marks in a row (up, down, across, or diagonal) is declared the winner.

    View Site View Code

    Head, Toe, Knees & Shoulder

    HTKS game developed using a NAO robot.

    This project was done in a team of 5 including myself. Our goal was to make therapy more friendly and engaging for the child. I worked on the user interace to give instructions to the robot. Never having worked before with Kinect Studio, it was a learning experience to work with it and appreciate the technology. It was quite a challenge to perfect the accuracy on the Kinect. This project was done as part of our Human-Computer Interaction class. A paper was also presented at the 9th ACM International Conference on Pervasive Technologies Related to Assistive Environments in 2016.

    Tech Stack
    • Java
    • JPA
    • JSP
    • Python
    • JavaScript
    • C#
    • Kinect Studio

    View Paper ACM Website

    Mav Appoint

    An Academic Advising System for departments across UT Arlington

    The Academic Advising System is designed for departments across the University of Texas at Arlington to provide a friendly, efficient and user-friendly advising appointment system to assist students and advisors. The Academic Advising System helps students and the advisors to schedule advising appointments and eliminate the necessity of long queues. The system can place a new appointment, modify and delete an existing one as well as screen the synchronized calendar.

    Tech Stack:
    • Java
    • JavaScript
    • JSP
    • HTML5
    • CSS3
    • MySQL

    View Code

    My Interests / Hobbies

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