CAMBRIDGE — The exhibition "Beyond Data-Driven Aesthetics" by Alexandros Haridis is on view at the MIT Keller Gallery in Cambridge through June 30, 2026. The exhibition presents historical and contemporary works examining how algorithms, computation, and machine learning have shaped aesthetic thinking in architecture and design.

The exhibition is organized around five thematic areas: Aesthetic Measure, Aesthetic Guidelines, Algorithmic Aesthetics, Aesthetic Appropriation, and Aesthetic Novelty. "The exhibition itself is organized around five thematic areas: Aesthetic Measure, Aesthetic Guidelines, Algorithmic Aesthetics, Aesthetic Appropriation, and Aesthetic Novelty. Each theme functions as a selective 'window' into a distinct computational approach to aesthetic judgment drawn from a specific publication — a book or research paper," Haridis said.

"The conceptual origins of 'Beyond Data-Driven Aesthetics' emerged from three intersecting lines of research. First, while completing my PhD in design and computation in the MIT Department of Architecture around 2022, I observed in real time how advances in data-driven machine learning — systems such as ChatGPT and Stable Diffusion — were rapidly entering public discussions about creativity, aesthetic judgment, design, and even high-profile art auctions," Haridis said. He holds an SM degree from MIT awarded in 2017 and a PhD awarded in 2022.

"Second, the exhibition was influenced by research in design computation and shape grammars that investigates relationships between human insight and computation through rule-based methods, rather than purely data-driven learning. More recent interpretative studies of aesthetic theories — drawing from figures such as Samuel Taylor Coleridge, Oscar Wilde, and even John von Neumann — have been especially important to me," Haridis said.

"Finally, the exhibition was motivated by the use of design, fabrication, and data visualization as methods for interpreting mathematical concepts, algorithms, and 'black box' machine-learning systems. Across disciplines, researchers increasingly use reconstruction and visualization techniques to make computational systems more tangible and interpretable — from neural network visualization in computer science to software reconstruction and digital fabrication in architecture and curatorial practice," Haridis said.

"The titles of these themes are derived from concepts central to each publication. For example, 'measure' refers to mathematician George Birkhoff's work in the 1930s to quantify aesthetic value mathematically, while 'novelty' examines how the machine learning system AICAN judges generated images according to a theory in cognitive aesthetics that balances familiarity and deviation from known artistic styles," Haridis said.

"'Beyond Data-Driven Aesthetics' is conceived both as a research exhibition and as an ongoing platform for investigating how computational systems participate in processes of aesthetic judgment, generation, and transformation across architecture and the applied arts. One of the central questions of the exhibition — and one that researchers across architecture, design, and engineering are increasingly focusing on — is computational evaluation beyond purely performative or functional requirements," Haridis said.

"The exhibition's case studies suggest that many of these questions long predate current interest in computing and AI, and have been approached through a range of computational and theoretical models of evaluation since at least the early 20th century. At the same time, I'm increasingly interested in how these ideas can move into broader applications related to the built environment," Haridis said.