This course introduces the fundamental concepts of vector and point data structures in a computational design context. It explains how real-world information can be translated into vector data, which represents magnitude and direction, and raster data, which is like a digital image. The importance of coordinate systems in interpreting these numerical data points is stressed, and different measurement systems like Euclidean and Manhattan distances are discussed. The lecture further explores basic vector operations such as scaling, addition, subtraction, and dot and cross products, highlighting how these operations are crucial for understanding the relationships between vectors and solving geometric problems like calculating angles, finding midpoints, and performing projections. Finally, the concept of transformations using matrices to manipulate geometric data is briefly touched upon, emphasizing how these core principles underpin much of computational design.

https://namjulee.github.io/njs-lab-public/work?id=2025-introductionToDesignComputation

This workshop lecture provides an introduction to programming and data processing, specifically within the Grasshopper environment. The instructor guides attendees through basic Python syntax and data structures like lists, strings, loops, and conditional statements, emphasizing the importance of understanding the underlying logic behind visual components. Key concepts demonstrated include manipulating numerical data, working with lists, using functions for tasks like mapping data between different domains, and generating random numbers. The lecture encourages hands-on practice by recreating visual component functionalities through coding, ultimately aiming for students to develop the ability to translate their design ideas into code and handle various data types and structures within the Grasshopper workflow.

https://namjulee.github.io/njs-lab-public/work?id=2025-introductionToDesignComputation

These sources present a compelling argument for the integration of data and computation into the field of design. They emphasize that future success in design hinges not on predicting fleeting trends, but on mastering enduring principles, problem-solving skills, and computational thinking. The lecture posits that understanding how to translate design concepts into quantifiable data structures—like vectors, rasters, and graphs—and then processing this data through code and algorithms is essential for creating powerful, iterative, and optimized design solutions. Ultimately, the material stresses the importance of hands-on coding practice, focusing on fundamental concepts like APIs, to enable designers to leverage the growing power of digital infrastructure.

https://namjulee.github.io/njs-lab-public/work?id=2025-introductionToDesignComputation

These sources introduce students to the foundational concepts of programming and data processing within the realm of computational design. The lecture emphasizes that programming is fundamentally data processing, requiring an understanding of data types and data structures to represent and manipulate information effectively. A key takeaway is the importance of applying computational thinking—breaking down problems and creating explicit instructions—to design challenges, distinguishing this from traditional implicit processes. The instructor also touches on essential skills like debugging and utilizing APIs to interact with design software, highlighting how embracing technology and its underlying principles is crucial for designers.

https://namjulee.github.io/njs-lab-public/work?id=2025-introductionToDesignComputation

These sources introduce a course titled "Data in Design," focusing on how designers can effectively use computation and data in their creative process. The instructor, an architect and software engineer, highlights that computational design is fundamentally about translating design thought into code and viewing data as a core design material. The course emphasizes understanding computational thinking, fundamental programming concepts like Python and Object-Oriented Programming, different types of design data (vector and raster), and techniques for handling them through discretization and various programming approaches. Ultimately, the goal is to equip students with the knowledge to develop explicit design processes and create software-based solutions, stressing the importance of mastering foundational principles over simply using fancy tools.

https://namjulee.github.io/njs-lab-public/work?id=2025-introductionToDesignComputation

https://youtu.be/K0Y20rPuoJs

This speaker, an architect turned software engineer and computational design specialist, discusses the increasing importance of data as a design material in the 21st century. He highlights how embracing computation, which involves translating design ideas into code, allows for the manipulation and analysis of spatial information in forms like vector and raster data, and explores how these digital representations can be used in various architectural and urban planning applications. The speaker emphasizes that understanding the foundational principles of computational thinking and data structures is crucial for designers to adapt to rapidly evolving technologies like AI and machine learning, rather than simply chasing the latest trends.

This presentation argues that data is emerging as a fundamental design material of the 21st century, much like stone or steel in previous eras. The speaker, with a unique background in architecture and software engineering (Esri, MIT, Ready), emphasizes the transformative role of computation in design.

• Data as Material: Designers must now treat data as a medium for creativity and problem-solving.

• Digitization & Codification: Design involves turning the real world into data (discretization) and translating design processes into algorithms (codification).

• Computational Thinking: Essential skills include breaking problems into parts, identifying patterns, and constructing abstract models.

• Data Structures & Algorithms: Understanding vectors, rasters, graphs, and voxels is critical; algorithms are seen as design processes.

• Deductive vs. Inductive Logic: Traditional programming (deductive) is best for certainty; machine learning (inductive) is better for prediction. Each has its place.

• Foundational Knowledge: Emphasis on mastering fundamentals (like vector/raster logic) rather than chasing trends.

• Historical Context: The talk draws parallels between past material innovations and today's data revolution, influenced by milestones like the big data boom, AlphaGo, and ChatGPT.

• Design Through Data: Everything from urban design to landscape analysis benefits from data-driven methods.

• Creative Potential of Computation: Algorithms can embody design logic, and machine learning can generate "good errors" that inspire novel outcomes.

• Responsible Use of ML: While powerful, ML isn’t suited for every problem—particularly not for high-precision tasks.

• Educational Commitment: The speaker is deeply involved in spreading computational knowledge through videos, lectures, and a book.

• “Data is a material.”

• “Your algorithm is your design process.”

• “Creativity is a good error.”

• “Invest in what won’t change.”

Core Themes:Key Messages:Memorable Quotes: