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.
State Farm
May, 2023 - Present
Code Ninjas
Aug, 2018 - Aug, 2021
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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.
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.
Analyzed 1985–2014 MLB data to quantify the “Moneyball” strategy’s efficacy using Python (NumPy, Pandas) and SQL. Identified that teams prioritizing sabermetrics outperformed salary-driven rivals in win-to-cost ratios until 2008, when widespread adoption neutralized the competitive edge. Demonstrated ability to derive actionable insights from complex datasets, track trends, and communicate strategic implications—skills directly transferable to optimizing business decisions in data-driven environments.
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.
+1 609-964-6439
vchugh@umd.edu
Princeton, New Jersey
