//email: rheats2@illinois.edu
//github: github.com/Rhea-Shah23
//photos: view photos
//blog: view blog

<Rhea Shah>

//CS @ UIUC
//Machine Learning Engineer
//about
Hello World, I'm Rhea Shah!

I'm studying computer science at the university of illinois urbana-champaign. I'm passionate about all things machine learning, whether that be computer vision, natural language, agents, or reinforcement learning, i love learning and applying different concepts. I'm also super passionate about building, I love building side projects and useful tools for myself and peers!

In my free time, I love spending time outdoors either by hiking, travelling, or just sitting outside. Fun fact: I've summitted mt. kilimanjaro! I also love fitness and just finished my first triathlon! To learn more about my life check out some photos and/or my substack

//work experience
September 2025 - Present
John Deere //Machine Learning Engineer
  • Deployed ML artifact removal pipeline to 10K+ farmers using Pytorch, reducing yield map noise by 73%, validated through a 20-customer panel
  • Designed and implemented a self-supervised learning quality metric adopted company-wide as a customer-facing standard, enabling automated scoring and flagging of low-confidence regions.
  • Developed computer vision and reinforcement learning features for autonomous combine harvesters, integrating multi-sensor fusion for real-time obstacle detection and harvesting path optimization.
  • Translated complex analytical findings into professional reports and presentations, communicating technical insights to key stakeholders to inform business decisions
transformer / reinforcement learning / deep learning / computer vision
August 2024 - June 2025
MIT Lincoln Lab //computer vision intern
  • Engineered a deep learning pipeline to classify 3D printing failures using a curated 10K-image subset of the CAXTON dataset.
  • Benchmarked ResNet, ViT, and Swin Models with ResNet50 achieving 89% accuracy; used feature extraction to cut model size by 99.9%.
  • Resolved data quality issues and conducted model analysis to inform autonomous defect detection in additive manufacturing.
transformer / computer vision / big data / anomaly detection
May 2024 - May 2025
MIT Lincoln Lab //natural language processing intern
  • Fine-tuned BERT and integrated SVMs using PyTorch and Hugging Face to build a robust fake news classifier
    • Benchmarked CNNs, RNNs, and transformers to optimize performance and generalize misinformation detection across sources
  • Strengthened skills in Python, scikit-learn, PyTorch, and Hugging Face Transformers through iterative model development
hugging face / natural language processing / bert
//projects
Advancing Diabetic Retinopathy Diagnosis: Deep Learning Approach Using Vision Transformers
NeurIPS2024
Computer Vision
Medical Technology
TweetSim: Predict Tweet Engagement Before You Post
Command Line Interface
Natural Language Processing
Sentiment Analysis
ARGreeksLab: Visualize 3D Options Pricing in AR
iOS App Development
Options Pricing
Augmented Reality
Distributed Real-Time Risk Engine for Crypto Derivatives
End-to-End
Quant Finance
2025
//communities
january 2026 - present
Quant @ Illinois //software developer
  • Collaborated with a team of developers to build and maintain software tools supporting the club's quantitative finance initiatives
  • Contributed to a cross-functional team bridging software engineering and finance, helping drive the club's technical direction
august 2025 - present
BuildIllinois //executive member
  • Organized a 500+ person hackathon, sponsored by YC-Backed companies, fostering innovation and collaboration among student developers.
  • Built a thriving entrepreneurial ecosystem at UIUC, organizing hackathons, summits, and retreats that connected 1,000+ students with investors and founders from Y Combinator, Sequoia, and Google
  • Facilitated partnerships with leading VCs and tech companies, securing $120k+ in prizes and creating fast-track opportunities to accelerators for student builders
august 2025 - present
Women in Computer Science //workshops committee developer
  • Designed and deveoped machine learning and computing workshops for college and high school students, consistently engaging audiences with 30+ students
  • Created accessible educational content that introduced underrepresented populations to computing topics fostering interest in computer science
  • Built a welcoming learning enviornment that helps bridge the gender gap in technology education