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Getting Started with Research Engineering

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This Research Engineering Learning Path is currently in beta. We're actively improving and refining the content based on community feedback and real-world usage.

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Welcome to the Average Joes Lab Research Engineering Learning Path, a community-driven educational resource that teaches you how to read, understand, implement, and conduct research using the same systematic methods used by researchers worldwide.

What is Research Engineering?​

Research Engineering combines the rigor of scientific research with the practical skills of implementation. It's about understanding how research works, being able to reproduce and validate findings, and contributing to the collective advancement of knowledge.

This learning path teaches you to:

  • Understand research papers - Read and comprehend academic literature in any field
  • Implement research findings - Turn theoretical concepts into working code and experiments
  • Follow the research process - Use the same methodology that drives scientific discovery
  • Practice reproducible science - Document and share your work so others can build upon it
  • Join a community of learners - Connect with others who are exploring research outside traditional institutions

Why Average Joes Lab?​

Average Joes Lab is a community initiative founded on the belief that research and scientific inquiry shouldn't be limited to academic institutions. We exist to democratize access to research knowledge and methods.

Who We Are​

We're a community of regular people from varied backgrounds who believe that systematic research methods can be learned and applied by anyone. Our mission is to create a community united by curiosity and a desire to understand how research really works and participate within the larger scientific community.

Our Mission​

  • Demystify research - Break down the barriers between academic research and everyday learners
  • Teach practical methods - Share the actual processes researchers use, not just theory
  • Build community - Create a supportive environment where questions are welcomed and learning is collaborative
  • Promote open science - Encourage reproducible, transparent research practices

Our Approach​

  • Learn by doing - Work through real research examples and implement actual papers
  • Start where you are - No prerequisites required; learn what you need as you go
  • Community support - Learn alongside others on the same journey
  • Curated resources - We organize and contextualize freely available educational materials
  • Open source - All our guides and examples are freely available and community-maintained

Is This For You?​

Perfect for:​

  • Curious individuals who want to contribute to human knowledge
  • Career explorers interested in research and development
  • Students who want hands-on research experience
  • Professionals interested in learning research methods
  • Independent researchers who want systematic methodology
  • Anyone passionate about understanding how the world works

Requirements:​

You Don't Need:

  • ❌ Advanced degrees or credentials
  • ❌ Years of domain expertise
  • ❌ Perfect mathematical background
  • ❌ Expensive equipment or software
  • ❌ Institutional affiliation

You Do Need:

  • ✅ Curiosity - Genuine interest in understanding how things work
  • ✅ Basic computer literacy - Comfortable with files, software, internet research
  • ✅ Willingness to learn - Persistence when things don't work the first time

Your Learning Path​

This comprehensive guide teaches research engineering through a structured three-part approach:

Part 1: The Universal Research Process​

Understanding the research cycle that provides a structured way of asking questions, exploring possibilities, and sharing what you learn. This methodology works across all fields, from computer science to biology to psychology to economics.

The Steps You'll Learn:

  1. Curiosity & Problem Framing - Transform vague interests into sharp research questions
  2. Literature Review - Map the knowledge landscape and stand on the shoulders of giants
  3. Hypothesis & Goal Setting - Make testable predictions that bridge curiosity and action
  4. Methodology Design - Create your research blueprint with clear protocols
  5. Experimentation - Do the work and test your ideas against reality
  6. Analysis & Interpretation - Transform raw data into knowledge and insights
  7. Iteration - Refine your approach based on what you've learned
  8. Writing & Communication - Share your discoveries effectively
  9. Peer Review & Feedback - Validate your work through community scrutiny
  10. Next Questions - Let your findings spark new research cycles

Part 2: The Perceptron Research Journey​

Experience the complete research cycle by stepping into Frank Rosenblatt's shoes in 1958 as he creates the first learning machine. This detailed historical walkthrough shows you exactly how each step of the research process unfolds in practice.

What You'll Learn:

  • The historical context of AI in 1958: Room-sized computers, vacuum tubes, and the birth of AI
  • Rosenblatt's curiosity: "Can we build a machine that learns like a brain?"
  • Literature review: Building on McCulloch-Pitts neurons (1943) and Hebbian learning (1949)
  • The breakthrough hypothesis: Weighted inputs + error correction = learning
  • Building the perceptron: The first implementation of machine learning with Rosenblatt's 1958 paper
  • The XOR problem: Discovering fundamental limitations through experimentation
  • The Minsky-Papert critique (1969): How peer review shaped the field with Perceptrons: An Introduction to Computational Geometry
  • The 25-year dormancy: From AI winter to deep learning revolution
  • Modern vindication: How backpropagation (1986) solved the multi-layer training problem

Why This Example Works: The Perceptron worked example provides a way of learning the research process using a complete narrative arc of Rosenblatt's work using code and math that align with the original paper (programming was a lot different back then, so we modernize this part with Python in our example).

Part 3: Your Research Journey​

Apply the methodology you've learned to your own research interests using our comprehensive starter repository with templates, examples, and community support.

Need Help Choosing a Research Paper?

If you want to explore other foundational papers beyond the perceptron, check out our comprehensive Paper Recommendations Guide which includes curated options across Computer Science, Biology, Psychology, Economics, and Physics - all selected for their ease of being implemented and learning potential.

Learning Approach & Support​

Just-in-Time Learning Philosophy​

Our approach is based on "just-in-time learning" - you don't need to master everything upfront. Instead, you learn what you need when you need it, building knowledge incrementally through hands-on work with real problems.

How it works:

  1. Start with genuine interest in a specific problem or field
  2. Pick a foundational paper that seems approachable
  3. Learn the minimum math/tools needed for that specific paper
  4. Build knowledge incrementally as you tackle more complex problems
  5. Join the community to learn from others on similar journeys

Foundation Support Available​

Start Now, Learn As You Go!

Prerequisites are NOT required to begin! Our STEM Foundations Learning Path provides comprehensive support from Pre-K to graduate level for just-in-time learning.

Available Foundation Topics:

Field-Specific Learning Paths​

For AI/Machine Learning Research:

For Biology/Life Sciences Research:

For Psychology/Social Sciences Research:

For Physics & Engineering Research:

For Economics & Data Science Research:

Remember: Every expert started as a beginner. The foundations are there to support you when needed, not to delay your research journey.

Get Started​

Ready to start learning research methods?

Your Next Steps:​

  1. Learn the Research Process - Understand the research methodology
  2. Study the Perceptron Example - See the methodology in action
  3. Get the Starter Repository - Apply what you've learned to your own research

Community & Resources:​

"Research is not just for academics. Every curious mind can contribute to human knowledge."

Welcome to Average Joes Lab - a community for learning and practicing research together.