The provided sources summarize a "Data In Design" course, emphasizing its core objective to teach students how to codify design processes using computational methodologies. The curriculum, structured around 17 sections and over 100 modules, covers topics from basic coding and geometry to advanced concepts like AI, data visualization, and software development for design. Students were guided to apply computational thinking through weekly assignments culminating in a final project, which also served as the primary assessment. The course materials, including lectures, slides, and podcasts, were designed to support continuous learning, with a final review session featuring expert feedback to reinforce student understanding and growth.

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

The provided sources offer an overview of computational design software development, emphasizing the integration of data design principles. They explore various development environments and tools, such as Unity for cross-platform deployment and Three.js/Babylon.js for web-based 3D graphics, alongside fundamental concepts like design process pipelining and event handling. The materials also discuss the importance of building personal software libraries and the philosophical underpinnings of a computational designer, stressing continuous learning and meta-cognition to challenge conventional approaches. Ultimately, the content aims to guide students in applying these concepts to develop and distribute meaningful software for their design projects.

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

The sources discuss the development of CAD software, emphasizing its role as a "software revolution" that transforms theoretical knowledge into executable and distributable systems for design and research. They highlight the fundamental differences between traditional design iteration and the methodical, step-by-step approach of software development, stressing the importance of structured architecture using concepts like front-end/back-end distinctions and the MVC (Model-View-Controller) design pattern. The lectures also explore object-oriented programming (OOP) for building hierarchical geometric data, the significance of rendering engines and performance optimization (including GPU-based parallel processing), and the crucial role of UI/UX principles in creating effective and user-friendly software. Ultimately, the material frames software development as a process of defining states, relationships, and rules to codify complex design processes, with a concluding motivational message about problem-solving and persistence.

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

These sources provide an extensive overview of design visualization, encompassing its broad definition and practical applications in fields like architectural and product design. The lectures and accompanying document emphasize the technical principles behind creating visuals, including rendering processes, camera techniques, lighting, and post-production. Significant attention is given to animation and simulation methods for depicting movement and processes, alongside numerous real-world examples of commercial, artistic, and research-based visualization projects. Ultimately, the materials highlight that effective design visualization goes beyond mere presentation, focusing on conveying meaning and facilitating understanding through various visual strategies and a blend of technical skill and artistic vision.

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

This lecture is about digital mapping and GIS for visualization, discussing how these tools are crucial for various design fields like architecture and urban planning. The lecture covers data types like vector and raster data, file formats such as GeoJSON and Shapefiles, and common GIS operations like buffering and dissolving. It emphasizes how mapping helps to uncover insights from data and explores different projection methods and visualization techniques, including animation and interactive maps, using various libraries and tools. The material also provides code examples and resources for practical application of these concepts.

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

This lecture consists of excerpts from two video lectures and a related briefing document focusing on the topic of data visualization within a design context. The lectures emphasize that data requires visualization to be understood by humans, serving both for analysis to uncover patterns and for communication to convey insights effectively. They outline a three-stage process: recording, analyzing, and communicating data visually, highlighting the distinct strengths of human visual perception and computer calculation in this process. The materials also discuss principles for creating effective visualizations, including maintaining graphical integrity and considering human cognitive limitations through techniques like chunking and appropriate scaling, while also addressing the potential for bias and the importance of interaction with data in modern visualization.

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

These sources, a lecture series and accompanying briefing documents, provide an overview of Dynamics and Agent Systems within the context of Data in Design, building on foundational concepts like geometry and algorithms. They explain how dynamic systems, which account for time-dependent states, and agent systems, which model the behaviors and interactions of individual components within an environment, offer powerful approaches for tackling complex design challenges. Spring models and particle systems are highlighted as core examples of dynamic simulation, while the Flocking/Boids algorithm illustrates collective agent behavior. The lectures strongly emphasize the necessity of Object-Oriented Programming (OOP) for structuring these systems and the importance of hands-on coding for practical understanding, concluding that formulating design problems in a computationally solvable way is key to leveraging these methods for generating emergent and interactive designs.

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

These sources, primarily drawn from a lecture on design algorithms and optimization, introduce algorithmic thinking as a method for tackling design challenges. They discuss bottom-up approaches that build from foundational data structures and algorithms, contrasting them with top-down approaches that start with the design problem itself. The lecture explains both deterministic algorithms, which yield consistent results, and stochastic methods, which incorporate randomness, as valuable tools for finding optimal or best solutions. Crucially, the sources emphasize the need for quantifiable metrics and objective functions to evaluate and optimize designs, illustrating these concepts through real-world examples and the notion of the Pareto front, which defines the boundary of optimal design parameters.

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

These sources primarily focus on design algorithms and optimization, arguing that these are not merely technical tools but are inherently integrated with the design process itself. They emphasize the importance of computational thinking for designers and suggest that understanding an engineering mindset, particularly in software, is crucial for applying computational methods effectively. The texts provide an overview of foundational algorithm types (like deterministic vs. stochastic and brute force vs. heuristic) and essential data structures (including lists, graphs, and queues), illustrating their relevance through practical design examples. Ultimately, the sources posit that algorithms and optimization are ways for designers to express their intentions and guide their creative endeavors.

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

This presentation explores AI-aided design, starting with the fundamental concept of generative models like pix2pix, which learn to create new images based on paired input and output data. It then discusses more complex applications such as creating 3D objects from sketches and generating maps using geographical data. The speaker also introduces Large Language Models (LLMs), explaining their architecture, the evolution from RNNs to Transformers, and the concept of fine-tuning and embedding to customize their behavior and knowledge. The presentation concludes by demonstrating practical uses of LLMs, including local execution of models and exploring various types of machine learning problems such as classification and regression, showcasing how AI models can be applied to different datasets like medical or financial information.

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