About
CS & Business Student at the University of Virginia
Email: aryan20544@gmail.com
Phone: +1 571 442 7810
City: Ashburn, VA
Hi, I’m Aryan Thodupunuri, a second-year Computer Science student at the University of Virginia, minoring in Data Science and General Business. I have a strong passion for software development and data-driven problem-solving, especially in areas that bridge technology and business strategy.
Right now, I’m expanding my technical foundation through courses like Computer Systems Organization and Software Development Essentials, which are deepening my understanding of low-level programming, system architecture, and scalable software design. This summer, I’ll be interning at Booz Allen Hamilton as a Federal Technology Consulting Intern, where I’ll be leveraging data analytics, automation tools, and procurement systems to improve federal contract tracking and supplier diversity initiatives. Previously, I interned at UVA’s Link Lab, collaborating with a Ph.D. student to enhance urban flood forecasting models. My work focused on optimizing a CNN-RNN hybrid model, using Python and TensorFlow to improve the speed and accuracy of real-time flood predictions. Outside of academics, I’m actively involved in Delta Upsilon Fraternity as the VP of Academic Excellence, where I support members in their professional and academic growth. I also work on data analytics projects with BioKind Analytics, applying predictive modeling techniques to real-world nonprofit initiatives. In my free time, I enjoy volunteering as a basketball coach, playing roller skate hockey, and exploring new tech projects. I’m always looking for exciting opportunities in software engineering, data science, and tech consulting—especially those that create a meaningful impact. Feel free to explore my projects here on the site, and don’t hesitate to reach out!
Resume
Summary
Aryan Thodupunuri
I am an effective student committed to building skills in data science, software development, and finance. With a strong foundation in these areas, I excel in both academic and practical applications. Known for excellent communication and a proven ability to accomplish objectives in a timely manner, I am dedicated, responsible, and eager to grow my abilities while contributing to operational success. Through my academic journey and various projects, I have developed a deep understanding of data analysis, programming, and project management, making me a versatile and valuable asset to any team.
(571) 442-7810
Education
B.A Computer Science & Minor in DS/Economics
University of Virginia, Charlottesville
Aug 2023 - May 2026
Relevant Coursework: Algorithms & Systems Design, Cloud Infrastructure & Enterprise Solutions, Machine Learning & Predictive Analytics, Software Engineering & Agile Development, Database Systems & Intelligent Data Processing, Tech Strategy & Digital Transformation
Extracurriculars: Research @ Floodwatch.io, BioKind Analytics (Predictive Analytics Consultant), Business & A.I Club (Project Analyst), Virginia Data Science & Analytics Club (VDSAC), & Delta Upsilon Fraternity (VP of Academic Excellence)
Advanced Diploma
Stone Bridge High School/Academy of Engineering and Technology, Ashburn, VA
Relevant coursework: AP Macroeconomics (5), AP Microeconomics (5), AP Calculus AB (5), AP Calculus BC (4), AP Statistics (5), AP Computer Science A (5), AP Physics C: Mechanics (4)
Stone Bridge Investment Fund (Founder and President), Speech and Debate Club (President), Stone Bridge CyberPatriot (Vice President), Stone Bridge Varsity Hockey Team
Experience
Incoming Summer 2025 Federal Technology Consultant, Booz Allen Hamilton, McLean, VA
Summer 2025
At Booz Allen Hamilton, I will work on analyzing and optimizing federal contract data to improve procurement efficiency and supplier diversity tracking. My role involves leveraging automation tools like iValua SBCMS, Power BI, and workflow automation to enhance reporting accuracy on $1M+ in federal contract awards. I will be responsible for evaluating and visualizing key procurement metrics, tracking Small Business (SB) and Small Disadvantaged Business (SDB) participation, and ensuring compliance with government subcontracting regulations (FAR, CPARS). Additionally, I will collaborate with cross-functional teams to develop data-driven insights that support federal procurement strategy and operational improvements.
Machine Learning Researcher, UVA Link Lab, Charlottesville, VA
May 2024 - August 2024
As a Machine Learning Researcher at UVA’s Link Lab, I collaborated with a PhD student to develop and optimize hybrid CNN-RNN models in Python and TensorFlow, enhancing real-time flood prediction by reducing computation time by 95%, from 3 hours to under 10 minutes. Through hyperparameter tuning and cross-validation, I increased predictive accuracy by 15%, integrating environmental factors such as topography and rainfall patterns to improve model performance. I worked extensively with satellite imagery and time-series data, implementing advanced feature extraction techniques to support flood risk management. Our work contributed to local flood management discussions and strategies, making a measurable impact on predictive capabilities in environmental monitoring.
Propel Intern, Capital One, Charlottesville, VA
December 2023 - January 2024
During my internship at Capital One’s Propel program, I partnered with a team of four to devise a solution aimed at increasing Capital One Shopping users and addressing specific marketing challenges. I utilized advanced data visualization techniques in Excel and Tableau to present performance metrics and findings to Capital One judges, helping improve decision-making processes. This experience strengthened my ability to collaborate effectively on data-driven projects and communicate insights clearly to stakeholders.
Data Analyst, BioKind Analytics, Charlottesville, VA
August 2024 - Present
At BioKind Analytics, a competitive, student-led data analysis group at UVA, I collaborate with nonprofits to drive impactful, data-driven projects. Currently, I am analyzing over $100,000 in donations for the Agape Pregnancy Center of Loudoun, automating the separation of monetary and asset donations using Python and R. This work has improved data consistency by 40% through custom scripts and statistical modeling. Additionally, I’ve developed machine learning models, including Random Forest and XGBoost, to predict high-value donors with 85% accuracy, and I’m using time series analysis in R to forecast event-based revenue with a mean absolute percentage error (MAPE) of 5%.
Software Development Intern, Triviappolis Treasures, Ashburn, VA
December 2021 - June 2022
During my internship at Triviappolis Treasures, I enhanced a city trivia game app by developing a dynamic tier system (bronze, silver, gold) based on users' correct answers. This feature fostered engagement and incentivized continued participation. I collaborated within a team to elevate the mobile application, utilizing React Native for the frontend and Django for the backend. Additionally, I implemented AWS Beanstalk for seamless deployment, ensuring efficient delivery of features and improved scalability. One of my key achievements was successfully transitioning advertisement deployment functionality from AdSense to Google AdMob, optimizing ad delivery and revenue generation for the app.
Code Coach, The CoderSchool Ashburn, Ashburn, VA
June 2022 - August 2023
As a Code Coach at The CoderSchool Ashburn, I customized individualized lessons in 1-on-1 training sessions, adapting content and pace to match students' proficiency levels in diverse programming languages, thereby fostering their skill development and mastery. I provided hands-on guidance and support to students as they embarked on diverse project endeavors aligned with their chosen programming language. These projects ranged from simple animations to complex applications featuring functional user interfaces, empowering students to apply and refine their skills effectively.
Portfolio (Click for Details)
NBA Win Prediction Neural Network
The Future Of Artificial Intelligence in Weather Forecasting
Traffic Sign Convolutional Neural Network
Stock Price Prediction Using K-Nearest Neighbors (KNN)
