Eric Cui
CS & EE at Stanford 🌲
About Me
Hi there! I'm Eric Cui, a Stanford CS + EE undergrad specializing in Artificial Intelligence and Hardware & Software. I'm passionate about creating innovative solutions at the intersection of full-stack development and cutting-edge AI research. My experience spans building secure infrastructure at scale, developing scalable web applications, managing robust API services, and designing user-facing experiences. I'm currently interested in Web3 and applied AI.
I'm always eager to tackle challenging problems and collaborate with others to make an impact. Let's connect!
Experience
Software Development Engineer Intern
Amazon Web Services
June - September 2025
Seattle, WA
Developed Rust-based post-quantum cryptography support for AWS hardware security modules. Worked on cryptograhpic SDKs (PKCS#11, JCE, OpenSSL) for exposing CloudHSM functionalities. Implemented Module-Lattice-Based CRYSTALS Dilithium (ML-DSA) cryptographic operation support.
Software Development Engineer Intern
Wise Agents
December 2024 - March 2025
Palo Alto, CA
Automated ERP and CRM systems with agentic AI. Utilized Spring Boot to refactor Java microservice servers and React for frontend development. Worked with ApolloAPI, MySQL, and StripeAPI to create and maintain client prospecting databases and user profiles.
Software Development Engineer Intern
Logistics Plus
June - September 2024
Erie, PA
Utilized ASP.NET Core to develop, maintain, and refactor warehousing web services and applications. Built customizable data models for client integrations and communication pipelines. Explored AI integrations using Azure ML Studio.
Portfolio
Pretrained Vision Models for Dermascopic Image Analysis
GithubA medical imaging research study exploring finetuning pretrained vision models for downstream dermascopic image analysis. Studied DINOv2 and ViT architectures with lossy image augmentation techniques.
TreeTrash: TreeHacks 2025 "Most Creative Use of OpenAI API" 2nd
A computer vision pipeline for identifying incorrectly sorted waste items, winning 2nd Prize at Stanford TreeHacks 2025. Integrated YOLOv8 object detection, OpenCV preprocessing, and vision models for waste classification to generate sustainability reports.
Get In Touch
Let's connect and discuss opportunities!
Feel free to reach out through any of the channels below.