Harrison Stark

I am a forward-thinking computer science enthusiast deeply committed to innovation and pushing technological boundaries. My passion for continuous learning is evident through hands-on experience and cutting-edge projects. My values extend beyond technology, reflecting a holistic educational background and my relentless pursuit of knowledge.

For information about my hobbies, click here.

Education

Bachelor of Science in Computer Science, Minor in Mathematics - 3.78 GPA (August 2021 - December 2023)
University of Florida

Associate of Arts - 4.0 GPA (January 2019 - May 2021)
Palm Beach State University

High School Diploma - 3.94 GPA (August 2017 - May 2021)
Suncoast Community High School

Experience

Qualtrics - Software Engineer 1 (January 2024 - Current)

  • Accelerated Machine Learning Infrastructure:
    • Managed and implemented features for a low-latency, high-traffic API gateway for company-wide machine learning inference supporting billions of monthly inferences.
    • Integrated with cutting-edge models, implementing cost-tracking mechanisms, and ensuring real-time metrics with effective visualizations.
  • Unified API Developments:
    • Created a comprehensive embedding API from scratch.
    • Integrated with Azure OpenAI, AWS Bedrock, and self-hosted AWS SageMaker models.
    • Spearheaded the general availability readiness of our LLM API including robust reporting and comprehensive scale testing protocols and documentation.
  • Internal Tool Enhancements:
    • Upgraded our internal LLM playground with significant new features such as chat history and CI/CD.
    • Guided an intern through project development, ensuring platform compliance with enterprise-grade standards for the internal LLM playground.
    • Led a team in the development of an internal LLM-powered Slack bot designed to triage and resolve tickets by suggesting relevant information from various sources, improving ticket resolution times and enhancing ticket experience.
  • Cost Tracking and Data Collection Systems:
    • Designed and deployed dashboards for extensive cost tracking across models, utilizing advanced data analytics tools.
    • Automated critical data collection processes for Monthly Engineering Review (MER) to reduce manual overhead and enhance accuracy, efficiency, and visibility across brands and inferences.
    • Implemented a company-wide AWS policy enforcing billing code tagging across all roles to ensure precise cost tracking for SageMaker resources.
  • Operational Readiness and Documentation:
    • Led the creation of extensive on-call runbooks, a standard documentation repository for our team, and standard operating procedures.
    • Improved operational readiness and ensured smooth knowledge transfer.
  • Cross-Team Collaboration and Support:
    • Facilitated cross-functional meetings.
    • Presented design documents and led initiatives to streamline processes, optimize resource allocation, and address team-wide challenges.

Qualtrics - Software Engineer (Part-Time) (October 2023 - December 2023)

  • Production Readiness:
    • Prepared the Quack-end Developer project for production, ensuring launch readiness and internal access for all employees.
  • Security and Maintenance:
    • Migrated high volume automations from Python 2 to Python 3, enhancing security posture.
    • Developed comprehensive unit and integration tests and thorough documentation for ease of future development.

Qualtrics - Software Engineer Intern (June 2023 - August 2023)

  • LTI 1.3 Integration:
    • Designed and developed the Gradebook Passback feature for the Qualtrics LTI 1.3 integration using Node.js, facilitating seamless grade data synchronization from Qualtrics surveys.
  • Permissions Feature Development:
    • Proposed and integrated a permissions feature, significantly expanding the potential customer base.
  • Launch Readiness Preparation:
    • Executed thorough testing, review, and completion of the launch readiness checklist.
    • Enabled a successful early access launch.

Undergraduate Research - Independent Researcher - (Link to Repository) (January 2023 - May 2023)

  • Authored a research paper exploring the use of YOLOv8, a state-of-the-art object detection algorithm, to improve real-time traffic signal operations at a complex intersection in Gainesville, Florida.
  • Suggested an implementation plan involving cameras connected to a central processing unit to monitor traffic and pedestrian activity.
  • Proposed signal timing adjustments to improve safety and efficiency up to 10%.

Projects

Legame (June 2024 - Current)

  • Developed a farming simulation game using C# in Godot, focusing on achieving a maximum farm size, paying off loans, and becoming self-sufficient to retire in the shortest possible time.
  • Implemented mechanics for planting and harvesting various legumes, each with unique growth patterns and special abilities, to create a dynamic and engaging gameplay experience.
  • Designed intricate gameplay systems including inventory management, item drops, legume hybridization, and upgrade paths to provide players with complex strategic choices.

Stark's Industry Mentorship (February 2024 - Current)

  • Mentored students at the University of Florida through tutoring, project management, and mock interviews to enhance their understanding of the tech industry.
  • Led students in industry-standard project lifecycle development, emphasizing agile methodologies, version control best practices, and thorough documentation.
  • Guided a student in developing a full-stack banking application, providing practical experience in real-world software development, including concurrency handling, UI and API design choices, and database management.

Harrison Stark's (My) Website - (Link to Project) (November 2023 - Current)

  • Developed a personal website using HTML, CSS, and JavaScript to effectively present information about my background, skills, and projects.
  • Designed and implemented a user-friendly interface that highlights my professional experience and portfolio.
  • Leveraged the project to enhance my understanding of fundamental web development concepts and improve my proficiency in frontend technologies.

MAISTRO - (Link) - (Frontend) - (Backend) (Fall 2023)

  • Led a team of four in developing an AI DJ using JavaScript and Python, adhering to enterprise software engineering standards.
  • Integrated the Spotify SDK to create a seamless user experience with a unique song recommendation algorithm utilizing the ChatGPT and Spotify APIs and a custom NLP model for emotion and valence extraction.

Quack-end Developer (Summer 2023)

  • Led a team of three in a two-day AI Hackathon to develop an LLM-based rubber duck debugger.
  • Assisted in creating the frontend utilizing React.js and led development of the backend powered by FastAPI.
  • Implemented text-to-speech and speech-to-text using Amazon Polly and Amazon Transcribe, respectively, and used AWS S3 Buckets for rapid input and output.

Only Cats - (Link to Project) (Fall 2022)

  • Developed a video processing program with a partner to remove video frames not containing cats in Python.
  • Utilized a CNN for cat detection in videos, implementing an enhanced EfficientNetB0 architecture.
  • Accelerated unit testing by up to 99% using single images in place of videos.

Traffic Accident Heatmap - (Link to Project) (Spring 2022)

  • Worked in a group of three to create a C++ program to display traffic accidents using the Google Maps API.
  • Adapted dataset of nearly three million traffic accidents from Kaggle which included location and severity.
  • Improved time efficiency of click response and sort by over 90% by utilizing attributes of heaps.

Assembly Simulator - (Link to Project) (Spring 2022)

  • Created an assembly program simulator in C++ for ARM programming.
  • Implemented functionality of 16 ARM commands, along with branching and a program counter.
  • Included ability to produce disassembly and simulation files from binary input.

Pokémon Pokédex - (Link to Project) (Spring 2021)

  • Utilized Java and Swing to create a scrollable GUI Pokédex with information about every Pokémon.
  • Implemented a SQL database using JDBC to fetch and store Pokémon and user information devised from a UML diagram.
  • Improved program incrementally using client-supplied feedback, success criteria, and a Gantt chart.

Landscape Classifier - (Link to Paper) (Spring 2021)

  • Developed a CNN to classify 150x150 RGB images for a research paper: The Effects of Hyperparameter Choices on Convolutional Neural Networks using Python
  • Incorporated elements from the following libraries: TensorFlow, NumPy, and Matplotlib.
  • Determined the most accurate activation function-kernel size pair to be Leaky ReLU and (3, 3).