What is AIRC?
AIRC (AI Research & Competition) is a structured pathway in collaboration with Ryquo Lab, designed to help students build the foundations needed to understand modern AI, develop research thinking, work with data and models, and grow toward projects, papers, and competitions.
Rather than stopping at tutorials or small demos, students learn how to ask meaningful questions, test ideas, analyze results, and communicate their work clearly.
NeurIPS 2024 High School Competition Spotlight
Building Independent AI Researcher
AIRC is designed not only to teach AI concepts, but to help students gradually develop the ability to think, build, and investigate independently.
Through AIRC 101 and 201, students learn how to formulate research questions, design experiments, evaluate results, and turn ideas into structured technical work.
Why AIRC is Different
Beyond
AI tools
Students learn how modern AI systems actually work — not just how to use them.
Beyond
coding
Students develop research thinking, experimentation, and technical reasoning.
Beyond
projects
Students build the foundations for independent research, competitions, and publication pathways.
How Students Grow Through AIRC
From learning AI concepts to building research thinking, technical depth, and meaningful work.
Research Thinking
• Formulate research questions and hypotheses
• Read, critique, and build on papers
• Design experiments and evaluate results
Technical Execution
• Work with real datasets and AI models
• Turn ideas into structured technical work
• Present research clearly and professionally
Research Outcomes That Go Beyond the Classroom
Students in AIRC do not stop at tutorials or small projects. They develop work that reflects real research thinking, technical depth, and exploration.
Examples of outcomes include:
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Research-based AI / AI+ X projects
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Competition-ready technical work
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Papers and publication-oriented writing
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Stronger portfolios for future academic pathways
Selected Student Work：
From real-world problem solving to academic research and competition-level work.
OptiPath Buses: An AI-Powered Microtransit System for Equitable Urban Mobility in New York City
A. Liu, R. Liu, J. Li
OptiPath is an AI-powered system built to improve urban transit efficiency and accessibility in New York City using real-world data.
Details are partially withheld due to ongoing competition submission.
📄 Accepted to ICLR Workshop 2026 （top-tier AI conference）
PAVE improves the reliability of retrieval-augmented LLMs by verifying whether answers are truly supported by evidence before final output.
Structured Reasoning for Fairness: A Multi-Agent Approach to Bias Detection in Textual Data
T. Huang, E. Fan
We introduce a multi-agent framework for bias detection in text, improving both accuracy and interpretability in LLM-based systems. The approach achieved 84.9% accuracy on the WikiNPOV dataset.
🏆 AAAI Workshop 2025 （top tier AI conference）— Spotlight Oral
⭐ Selected for oral presentation
Springer Nature
arXiv preprint
These projects reflect a clear progression — from solving real-world problems to contributing to academic research and top-tier AI conference pathways.
Meet the AIRC Instructors
Learn from researchers and engineers working at the frontier of modern AI systems and research.
Research experience at MIT CSAIL and industry experience at Cleanlab, focused on trustworthy and modern AI systems. Contributed to work published at NeurIPS, ICLR, and AAAI, and has mentored students whose research was accepted to top workshops and IEEE venues.
Graduate student at CMU LTI, focusing on machine learning and multi-agent systems, with research published at NeurIPS, ICLR. Brings real-world ML engineering experience from Amazon and extensive experience mentoring students in AI and research projects.
Begin Your Summer AIRC Journey
Our Summer AIRC Program is designed as an entry point into the research pathway.
Students build foundational skills, develop projects, and prepare for deeper research and competition.
Foundation in AI & Research Thinking
（G8-G12)
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Build AI fundamentals and intuition
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Learn research workflow
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Complete your first structured project
Weekend Track，Date A: 6/6/2026–8/8/2026 at time 5:00-7:00 pm PT, 10-week live online course, Saturdays;
Intensive Track，Date B: 7/6/2026 - 7/28/2026 at time 5:00-7:00 pm PT, 4-week live online course, Monday, Wednesday, and Friday.
