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🚀 AI Research & Competition (AIRC) Program — Now Open！
From Curiosity to Contribution. Learn how AI really works.
Ever wondered what’s behind ChatGPT, Gemini, or Claude — not how to use them, but how they’re built?
AIRC is a structured AI research pathway for high school students who want to move beyond surface-level AI.
You’ll build real understanding, real technical skills, and real research-level outcomes — step by step.
Across the pathway, students will:
✨ Understand how modern AI systems (especially LLMs) work
✨ Read and analyze real research papers
✨ Build projects with industry-standard tools (Python, PyTorch, LangChain, GitHub)
✨ Learn how research moves from idea → experiment → paper → submission
✨ Create portfolio-ready work for competitions and advanced research tracks
Start where you are. Build what matters. Contribute to the future of AI.
⭐ Why This Pathway Matters
⭐ Why This Pathway Matters
Students must go beyond using AI—they need to understand it, analyze it, and create with it.
This pathway builds real AI research ability through a clear progression from interest → understanding → skills →Achievement → college & career advantage, helping students grow into real AI researchers.
Professional Mentorship Team from Leading Research Institutions
including MIT, Carnegie Mellon, Stanford Other Top-Tier Universities
Instructor A：AI researcher and ML engineer at MIT CSAIL, with industry experience at Cleanlab, focusing on trustworthy and modern AI systems. His research has contributed to publications at top AI conferences such as NeurIPS, ICLR, and AAAI, and he has successfully mentored students whose work was accepted to top conference workshops and IEEE venues.
Instructor B：ML engineer and NLP researcher and a graduate student at Carnegie Mellon University’s Language Technologies Institute (LTI), one of the world’s leading AI research hubs. He works on machine learning and multi-agent systems and has published research at NeurIPS. He also brings strong real-world engineering experience, having worked as a software and ML engineer at Amazon, and has extensive experience teaching and mentoring students in AI and research projects.
Level 1 (AI RC 101)
Your first real step into AI research
Go beyond using AI — learn how modern AI actually works and build your first real AI research project.
AIRC 101 is a beginner-friendly AI research program designed for high school students who want to go beyond using AI and start understanding how it actually works. This course combines AI fundamentals, research thinking, and hands-on projects, helping you build a real foundation in modern artificial intelligence — even if you’re starting from zero. By the end of the course, you will complete a full AI Innovation Proposal aligned with the Presidential AI Challenge (Track I), a nationally recognized competition.
Who This Is For
Level 1 (AI RC 101)
Your first real step into AI research
Go beyond using AI — learn how modern AI actually works and build your first real AI research project.
AIRC 101 is a beginner-friendly AI research program designed for high school students who want to go beyond using AI and start understanding how it actually works. This course combines AI fundamentals, research thinking, and hands-on projects, helping you build a real foundation in modern artificial intelligence — even if you’re starting from zero. By the end of the course, you will complete a full AI Innovation Proposal aligned with the Presidential AI Challenge (Track I), a nationally recognized competition.
Who This Is For
Students in Grades 8–12
Curious about AI, CS, or research
No prior AI or research experience required
Ideal for students with basic coding experience who want to level up
What You’ll Learn
You won’t just listen — you’ll think and build like a researcher.
Understand how modern AI works, especially LLMs like GPT
Learn how researchers read papers, identify problems, and design solutions
Explore how language is represented mathematically (vectors, embeddings, attention)
Set up GitHub + VS Code and complete an AI mini-task
Design a complete AI Innovation Proposal, including:
Problem definition
AI-based solution
Feasibility, impact, and limitations
Core Technical Foundations
Core Technical Foundations
ML basics: vectors, loss functions, neural networks, gradient descent
NLP fundamentals: tokenization, embeddings, cosine similarity, language modeling
Model architectures: RNNs, LSTMs, attention, transformers
Frameworks: PyTorch, LangChain
Why AIRC 101 Is Different
Why AIRC 101 Is Different
Real Research Context: Learn how AI research is actually done at top conferences
Frontier AI Focus: Understand how today’s LLMs are trained and evaluated
Tangible Outcome: A national-level AI Innovation Proposal
Strong Foundation for competitions, portfolios, and future AI research pathways
Level 2 (AIRC 201)
From understanding AI to building it like a researcher
Turn concepts into real ML systems.
AIRC 201 is the hands-on, technical continuation of AIRC 101. This course is designed for students who already understand what AI is and are ready to build, evaluate, and present real machine learning systems like an ML researcher. You will work with real datasets, train and evaluate models in Python, visualize results, and package your work into a public-facing AI demo and research-style mini paper — the essential toolkit for advanced research and competitions.
Who This Is For
Who This Is For
Students in Grades 8–12 who completed AIRC 101 or have equivalent foundations
Students who want real technical depth, not just theory
Ideal for students preparing for AI research projects, competitions, or publications
What You’ll Learn
What You’ll Learn
You’ll move from ideas to execution — end to end.
Build full ML workflows: data → model → evaluation
Learn how researchers measure, debug, and compare models
Turn results into clear visualizations and insights
Package your work into a Streamlit AI demo
Practice writing a research-style mini paper with structure and rigor
What You’ll Do
What You’ll Do
This course is hands-on and skill-driven.
Work with real datasets in Python
Train and evaluate baseline ML models
Perform error analysis and interpret results
Build an interactive Streamlit web app to showcase your work
Complete a guided mini research paper (multi-agent systems topic), including:
Experimental design
Results & comparisons
Limitations and future work
Core Technical Foundations
Core Technical Foundations
Python for ML: clean code, notebooks, debugging, project structure
Data & Visualization: NumPy workflows, EDA, Matplotlib charts
Baseline ML: scikit-learn pipelines, cross-validation, metrics
Streamlit Demos: model + plots + UI integration
Paper Practicum: experiment design, results analysis, limitations
Why AIRC 201 Is Different
Why AIRC 201 Is Different
Skill-First Execution: you build real ML systems, not toy examples
Research-Grade Workflow: learn how ML research is actually done
Portfolio-Ready Output: demos + experiments + structured writing
Direct Bridge to AIRC 301: prepares you for original, publishable research
Final Outcomes
Final Outcomes
A complete ML project with evaluation + demo
A research-style mini paper
The technical foundation to pursue:
Advanced AI research
High-level competitions
AIRC 301 (publication track)
📩 Contact Us
📩 Contact Us
For questions or enrollment inquiries, please contact us at:
Email: powerupbootcamp@gmail.com
Organization: PowerUp Academy
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