AI Research & Competition Pathway
A structured pathway for students who want to move beyond learning AI tools and into real research, technical depth, and competition-level outcomes.
AI 科研与竞赛路径
从 AI 基础到科研项目，建立技术深度，打造具备竞争力的成果。
From foundational learning to research projects, papers, and competitions —
AIRC guides students through a structured journey into AI research.
What is AIRC?
AIRC (AI Research & Competition) is a structured pathway designed to help studentsbuild 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.
AIRC 是一条系统化路径，帮助学生建立理解现代 AI、发展科研思维、处理数据与模型，并逐步走向项目、论文与竞赛所需的核心能力。它并不停留在教程式学习或简单 demo，而是引导学生学会提出问题、验证想法、分析结果，并清晰地表达自己的工作。
Why AIRC is Different
Not Just
AI tools
Build real understanding of AI models，data，and systems work
Not just
coding
Develop research thing， experimentation and technical reasoning
Not just
projects
Create work that can lead to papers， competitions and serious academic direction
A Structured Pathway from Foundations to Publication
从基础到发表的系统成长路径
What Students Learn to Do
学生将在 AIRC 中学会什么
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Work with real datasets and AI models
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Formulate research questions and hypotheses
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Design experiments and evaluate results
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Read, critique, and build on existing papers
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Turn ideas into structured technical work
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Present research clearly and professionally
Research Outcomes That Go Beyond the Classroom
超越课堂的科研型成果
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
Arick Liu, Ryan Liu, Jason 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.
Structured Reasoning for Fairness: A Multi-Agent Approach to Bias Detection in Textual Data
Tianyi Huang, Elsa 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 — Spotlight Oral
⭐ Selected for oral presentation
Springer Nature
arXiv preprint
PAVE: *** AI Research Project (Title Withheld)
📄 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.
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.
由活跃在现代 AI 系统与科研前沿的研究者与工程师带领。
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.
Start Your Journey with Our Summer AIRC Program
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.
AIRC 暑期项目是进入科研路径的起点，
帮助学生建立基础能力、完成项目实践，并为后续科研与竞赛路径做好准备。
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: 6/8/2026 - 6/29/2026 at time 5:00-7:00 pm PT, 4-week live online course, Monday, Wednesday, and Friday.
Limited seats · Structured cohorts
Ready to get started this summer?
准备从这个暑期开始你的科研路径了吗？