# Devin Yue

> ML Engineer @ Luel (YC W26) | CS @ Georgia Tech

Location: United States, United States
Profile: https://flows.cv/devinyue

## Work Experience
### Machine Learning Engineer @ Luel (YC W26)
Jan 2026 – Present | San Francisco, California, United States
Building frontier models

### Teaching Assistant (CS 2050) @ College of Computing at Georgia Tech
Jan 2026 – Present | Atlanta, GA
Held 4 office hours/week for a 200+ student course, tutoring and lecturing students on proofs (contradiction/contrapositive), induction/recursion, combinatorics, and asymptotic analysis. Graded course exams and assignments using consistent rubrics; delivered feedback emphasizing rigor and correctness; Supported student inquiries around structures and computability-focused topics

### Student Researcher @ Georgia Tech VIP Program
Jan 2026 – Present | Atlanta, Georgia, United States
Cool RL stuff with Waymo Datasets and VLMs

### Analysis Member @ Big Data Big Impact @ Georgia Tech
Jan 2026 – Present
Building Cool CV pipeline (STGCNs, Bi-GRUs, etc) for Perfect Punch

### Software Developer – RoboWrestling @ RoboJackets
Jan 2025 – Present | Atlanta, Georgia, United States
Develop C++ control systems for competition-grade wrestling robots, focusing on autonomy, movement algorithms, and reliable state-machine design. Collaborate in a fast-paced engineering environment with Electrical and Mechanical teams to deliver robust, low-latency software optimized for real-time robotic combat.

### Web Developer – Apiary Team @ RoboJackets
Jan 2025 – Jan 2026 | Atlanta, Georgia, United States
Contribute to the development of Apiary, the internal platform supporting 400+ RoboJackets members. Work across React Native, SQL, and Laravel to build and improve organizational tooling, enhance UI/UX, maintain clean data management flows, and resolve production issues to keep systems reliable for all teams.

### General Project Developer @ AI at Georgia Tech
Jan 2025 – Jan 2026
Worked on an AI-powered study assistant that uses Bayesian Knowledge Tracing (BKT) and DINA (Deterministic Inputs, Noisy AND) cognitive diagnosis models to estimate student mastery at a fine-grained skill level and adapt problem selection. Implement item–skill mappings and mastery inference so the system can identify which specific concepts a student struggles with, then integrate Manim-driven animation generation and LLM generation to create targeted explanations that address those gaps.

### USG IT @ Vancouver Model United Nations
Jan 2024 – Jan 2025 | Vancouver, British Columbia, Canada


## Education
### Bachelor of Applied Science - BASc in Computer Science
Georgia Institute of Technology

### St. George's School


## Contact & Social
- LinkedIn: https://linkedin.com/in/devinyue

---
Source: https://flows.cv/devinyue
JSON Resume: https://flows.cv/devinyue/resume.json
Last updated: 2026-04-10