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Software Engineer

Hello, I am Ved Chugh

I do |

About

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More about me

Hello, I am a recent University of Maryland Computer Science graduate with a focus on Data Science and Machine Learning. Post-graduation, I expanded into front-end development to effectively visualize data insights. Currently a Data Analyst at State Farm, I bring nearly four years of tech industry experience. Outside work, I pursue reading and film photography – hobbies rooted in childhood explorations of the woodlands near my home. My drive stems from relentless curiosity and dedication to impactful work, qualities I aim to sustain throughout my professional and personal life.

Experience

state farm logo

Software Engineer

Visa

JavaReactDockerPythonSQL

Jul, 2025 - Present

  • Developing and mantaining a full-stack, enterprise-level foreign exchange platform using Java, Angu- lar, and REST APIs, facilitating millions in global daily trades with high reliability and throughput.
  • Optimized backend systems (Java, Docker, SQL) for performance, readability, and maintainability, improving database operations and long-term system efficiency.
  • Automated testing frameworks using Selenium and Cucumber for BDD (Behavior Driven Development)
state farm logo

Data Engineer

State Farm

SQLReactAmazon Web ServicesAmazon RedshiftPythonPower BI

May, 2023 - Jul, 2025

  • Currently engineering a SAS/DB2 to Python/AWS Redshift migration pipeline for 20+ terabytes of on-premises data reporting using Python, SQL, and Power BI, improving scalability and dramatically reducing reporting time likely by over 75%.
  • Reduced query runtime by >40% using SQL optimizations and AWS Spice, allowing reports to be available and ready on-demand in AWS Quicksight.
  • Automated and modernized repetitive tasks using PowerShell and Python scripting, eliminating errors on repetitive tasks, and saving hours of manual work.
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Tutor

Code Ninjas

JavaScriptPythonJavaHTMLCSS

Aug, 2018 - Aug, 2021

  • Programmed activities using JavaScript, Java, and Python to ensure learning and increase student attendance while collaborating with peers.
  • Created summer curriculums leveraging HTML and CSS to optimize design on internal pages.
  • Fostered problem-solving skills and digital literacy in young learners—demonstrating adaptability, clear communication, and a commitment to nurturing talent in STEM.

Skills

Hover over skill for proficiency

Power BI

80%

PyTorch

70%

Amazon Redshift

70%

HTML

50%

JavaScript

50%

Python

90%

Git

80%

Node

30%

SQL

85%

CSS

40%

React

60%

GraphQL

35%

Docker

60%

Tailwind CSS

65%

SAS

70%

NumPy

65%

Seaborn

60%

Pandas

55%

Amazon Web Services

50%

Java

80%

Projects

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1 of 4: Frontend Training

ReactCSSJavaScriptHTMLTailwind CSSGraphQL

A diverse portfolio of frontend projects showcasing technical growth and modern tool proficiency built using vanilla JavaScript, React, CSS, and Next.js. Concepts include component-based architecture, responsive design, and CMS integration. Continuously expanding my toolkit to align with industry demands, I emphasize clean code and user-centric solutions using industry tools. This portfolio is actively evolving—click the image to explore real-time updates on my growth.

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2 of 4: Data Science Crash Analysis

PythonSQLSeabornGit

Engineered a data analysis pipeline using Python (Pandas, SQL) and machine learning (scikit-learn’s Random Forest) to identify predictors of U.S. car crashes. Delivered actionable insights via interactive visualizations that simplified complex trends. Collaborated using Git for seamless version control, ensuring reproducibility. Our results underscored environmental factors’ impact on collision rates. Explore the project’s code by clicking on the image.

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3 of 4: Computer Vision Movie Analysis

SQLNumPyPandasPyTorch

In college, filmmaking enamored me. Since, graduating I've become obsessed with merging my degree in computer science with my studies in filmmaking. To start, I designed and implemented a neural netowrk from scratch using PyTorch to identiy movie eras based solely on poster visuals. Later, I implemnted transfer learning wth a pretrained ResNet, fine tuning layers for era-specific pattern recognition. However, data exploration was the focus of this project, and allowed me to get better insight into the workings of a digital image. In the end, this project serves as a major stepping stone towards my goal of computer vison expertise.

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4 of 4: Machine Learning Intro

Python

Implemented a neural network for classification tasks, like recognizing handwritten digit images. Computed gradients via backpropagation using vectorized NumPy operations, handled batched inputs, and updated parameters through gradient descent. The modular design allows swapping activation functions and loss functions on the fly. It highlights my foundational knowledge of neural network mechanics and software design for machine learning systems.

Contact Me

+1 609-964-6439

vchugh@umd.edu

Princeton, New Jersey