About Me
I am passionate about data analytics and data engineering, especially how leveraging big data and advanced analytics can optimize processes, uncover valuable insights, and drive impactful decision-making. I am pursuing a Bachelor of Science in Data Engineering at Texas A&M University, expecting to graduate in 2026.
Currently, I am interning as a Software Developer at Dematic, where I develop software using Spring Boot, Maven, and Java to integrate PLC controllers with Autonomous Mobile Robots, streamlining warehouse automation workflows. Additionally, I enhance dataset readability and performance for storage selection software by refactoring disparate datasets using MySQL. My role also involves performing comprehensive software testing for storage selection logic and leveraging UA Expert for efficient data read/write operations to PLC controllers, thus enhancing system reliability and performance.
I am seeking opportunities to gain real-world experience and contribute to meaningful projects as an intern. My passion to learn and stay updated with the latest advancements in data and computer science technologies is truly what drives me.
Professional Experience
Developed software using Spring Boot, Maven, and Java to integrate logic controllers with Autonomous Mobile Robots, streamlining warehouse automation workflows. Enhanced dataset readability and performance for storage selection software by refactoring disparate datasets using MySQL. Performed comprehensive software testing for storage selection logic and leveraged UA Expert for efficient data read/write operations to logic controllers, enhancing system reliability and performance.
Developed a Python script achieving 1.38% error in image distortion removal and contributed to a C++ visual odometry pipeline. Presented DinoV2's impact on feature tracking for visual odometry at A&M Science Symposium to a non-technical audience.
Deployed Google Cloud Cortex Framework for SAP and developed Looker dashboards for intuitive data visualization. Acquired expertise in data engineering, complemented by certifications in Google Analytics, machine learning, and Looker.
Led project scope, timeline, and deliverables for a machine learning initiative predicting song popularity. Implemented an advanced k-nearest neighbor algorithm achieving a 14-20% accuracy in song prediction.
Successfully launched and scaled a e-commerce platform for coveted sneakers and apparel, achieving consistent annual sales growth. Managed all aspects of the business, including inventory management, customer service, and marketing.
Personal Projects
Implemented ETL processes on raw Uber data using Google Cloud Platform Storage, Python, Compute Instance, Mage Data Pipeline Tool, BigQuery, and Looker Studio to enable advanced data analytics and dashboard creation
Utilized the Random Forest machine learning algorithm to predict wins and losses in men's NCAA basketball games based on trivial factors, and constructed the corresponding dataset
Developed an machine learning model incorporating a Long Short-Term Memory Network to accurately predict stock closing prices, leveraging Numpy, Pandas, and Matplotlib for data handling and visualization
Contact Me