I am Himanshu Kiran Garud, a Master's student in Computer Science at UNC Charlotte and over two years of professional experience as a software engineer. As a Software Engineer Intern at EPRI, I developed interactive web applications using Vue.js and TypeScript, ensuring 95% cross-device compatibility and creating dynamic graph visualizations with Observable Plot, reducing rendering time by 20%. At Persistent Systems, I optimized ETL pipelines, automated workflows, and migrated systems from GraphQL to SQL, improving runtime by 30% and reducing AWS billing time by 65%. During my internship at Eastro Control Systems, I built a fault-tolerant web application for employee work-hour management, optimized database performance, and enhanced operational efficiency by 30%. Proficient in Python, Java, Vue.js, and AWS, I am passionate about solving complex problems and delivering impactful, user-focused solutions.
Master of Science in Computer Science
I have completed coursework in Algorithms, Data Structures, Software Engineering, Mobile Application Development, and Artificial Intelligence.
Bachelor of Engineering in Computer Engineering
As a undergraduate student I built a strong foundation in core computer science concepts such as Data Structures, Algorithms, Database Management Systems, and Operating Systems. During my time here, I gained hands-on experience in software development, problem-solving, and team collaboration through various academic projects and internships.
- Developed EPRI subscriber website using Vue.js and
TypeScript, ensuring 95% cross-device compatibility.
- Created dynamic graph visualizations with D3.js and
ObservablePlot.js, accurately plotting complex scientific data
and reducing rendering time by 20%.
- Created YAML feeds for Renaissance Learning (US) reducing
AWS billing time by 65% and improving performance by 40%.
- Migrated from GraphQL to SQL server, reducing runtime by 30%
and boosting data processing efficiency using AWS, SQL and
Shell scripting.
- Automated ETL pipelines, optimized queries, and improved
processing speed by 25% with Python and Google BigQuery
(DBT).
- Conducted research to analyze common query patterns, leading
to managing a subsidiary project and automating 100+ data
feeds for Oscar Health (US), optimizing AWS billing time.
- Developed a web application with fault tolerance to ensure
reliable work-hour updates and approvals for 50+ employees,
using Python Flask for backend and HTML/CSS/JavaScript for
frontend.
- Designed a normalized SQLite database schema with indexing
strategies to optimize query performance and scalability,
improving operational efficiency by 30%.
- Developed Deep Learning models (3D CNNs) for dynamic 3D
human mesh reconstruction using commercial mmWave radar with
point cloud data, achieving a 2.47 cm average error in vertex
localization.
- Integrated VTrig-74 sensor for real-time data collection,
enabling precise mesh formation.
Java
C/C++
Python
Node
VueJS
Streamlit
SQL
PostgreSQL
Google BigQuery
GitHub
Postman
JIRA
