Q: What inspired the “Beyond Data-Driven Aesthetics” project and what questions does it raise?
AND: The conceptual origins of “data-transcending aesthetics” emerged from three intersecting lines of research.
First, while completing my PhD in Design and Computation at MIT’s School of Architecture around 2022, I watched in real time as advances in data-driven machine learning – systems like ChatGPT and Stable Diffusion – were rapidly entering public discussions about creativity, aesthetic judgments, design, and even high-profile art auctions.
At the same time, my own research was already focusing on aesthetic judgment and evaluation, and it became increasingly clear to me that many of the questions presented to the public as “new” regarding artificial intelligence actually have a much longer history, dating back to the 20th century. For example, in the 1956 Dartmouth Summer Research Project, the fundamental event in artificial intelligence, creation, and evaluation processes was identified as one of seven key dimensions of human intelligence that future artificial intelligence research should address.
Second, the exhibition was influenced by research on design computation and shape grammar, which explored the relationship between human insight and computation using rule-based methods rather than solely data-driven learning. More recent interpretive studies of aesthetic theories – drawing on figures such as Samuel Taylor Coleridge, Oscar Wilde, and even John von Neumann – have been particularly vital to me. This research examines whether theories of aesthetic value and comparisons articulated in philosophical and literary texts can reveal possibilities or limitations in contemporary models of digital computing and artificial intelligence in architecture and design.
Finally, the theme of the exhibition was the operate of data design, production and visualization as methods for interpreting mathematical concepts, algorithms and “black box” machine learning systems. Across disciplines, researchers are increasingly using reconstruction and visualization techniques to make computational systems more actual and interpretable – from neural network visualization in computing to software reconstruction and digital fabrication in architecture and curatorial practice.
Q: How to translate computational and aesthetic research into an exhibition?
AND: The exhibition’s approach is to ask what exactly in a particular research article or book captures the most salient idea, and then operate design to interpret that idea in a visual, spatial and experiential format. Drawing on design techniques such as software reconstruction, physical creation, and data visualization, the exhibition takes written sources that are luxurious in algorithmic ideas, abstract concepts, and mathematical formulas, and translates them into stories in space that include interaction, material form, and digital visualization.
The exhibition itself is organized around five thematic areas: Aesthetic Measure, Aesthetic Guidelines, Algorithmic Aesthetics, Aesthetic Appropriation and Aesthetic Novelty. Each topic functions as a selective “window” into a distinct computational approach to aesthetic evaluation drawn from a specific publication – a book or research article. The titles of these topics are derived from concepts central to each publication. For example, “measure” refers to mathematician George Birkhoff’s work in the 1930s to mathematically determine aesthetic value, while “novelty” examines how the AICAN machine learning system evaluates how images are generated according to the theory of cognitive aesthetics, which balances familiarity with and deviation from familiar artistic styles.
In all five cases, the key insight is that design itself can function as a method of interpretive translation—a way of making observable, actual, and experientially what time-honored academic scholarship in technical fields typically communicates only through words and word-like representational devices such as scientific diagrams and tables.
Q: What questions do you hope to explore next?
AND: “Beyond Data-Driven Aesthetics” is conceived as both a research exhibition and a indefinite platform for examining how computational systems participate in processes of aesthetic appreciation, generation and transformation in architecture and the applied arts.
One of the central questions of the exhibition—which is increasingly the focus of architecture, design, and engineering scholars—is computational evaluation beyond purely performative or functional requirements. This applies to many different design spaces, whether buildings, structural forms or everyday products. The case studies in the exhibition suggest that many of these questions arose long before the current interest in computer science and artificial intelligence, and have been approached through a range of computational and theoretical assessment models since at least the early 20th century.
At the same time, I am becoming increasingly interested in how these ideas can be transferred to broader built environment applications. In particular, I am interested in how research related to “Moving Beyond Data-Driven Aesthetics” can facilitate designers and engineers better understand how computation – whether rule-based or data-driven – can inform us about what positively impacts human experiences of the spaces and objects in which people live and operate.
Finally, a direction that I continue to explore is the methodological role of the project itself as an interpretive tool. Through software reconstruction, visualization, and physical creation, the exhibition uses design to translate cloudy computational systems into more readable, actual, and experiential artifacts. More broadly, this raises questions not only about the mechanization of “beauty” or “taste” (the time-honored preoccupation with aesthetic formalism in the 20th century), but also about how time-honored forms of scientific inquiry and communication can evolve through spatial, visual, and public-facing formats.