All in Publication
ACADIA Conference, 2022. Vanguard Paper Award Runner-Up
A key technological weakness of artificial intelligence (AI) is adversarial images, a constructed form of image-noise added to an image that can manipulate machine learning algorithms but is imperceptible to humans. Over the past years, we developed Adversarial Architecture: A scalable systems approach to design adversarial surfaces, for physical objects, to manipulate machine learning algorithms.
Human Robot Interaction 2018. ACM.
Understanding explanations of machine perception is an important step towards developing accountable, trustworthy machines. Furthermore, speech and vision are the primary modalities by which humans collect information about the world, but the linking of visual and natural language domains is a relatively new pursuit in computer vision, and it is difficult to test performance in a safe environment. To couple human visual understanding and machine perception, we present an explanatory system for creating a library of possible context-specific actions associated with 3D objects in immersive virtual worlds.
METU Journal of the Faculty of Architecture. 2011
Early stages of a design process are where the designer experiments with the initial concepts and the perceptual qualities of the envisioned design idea. In our contemporary culture of technology, the acts of design performed in these stages are being transferred into the digital domain more and more as design and production processes merge and gain speed. The visual and spatial integrity of design performances in earlier stages has value for the entire design process and should be sustained in the digital domain as well. The value of hands-on design thinking, or in other words the spatial and visual thinking with materials at hand, comes forth in pedagogical discussions that emphasize design reasoning in the field of arts and design as early as the beginning of the 19th century.
International Conference on Spatial Cognition 2018.
In this research we aim to understand the interactions between visual space perception and language in various environmental conditions and tasks. In particular, we investigate spatial concepts in language that are used during active explorations and characterize them in relation to corresponding visuospatial context. We introduce an immersive virtual reality system that allows conducting spatial exploration tasks and recording perceptual and language data. Using this system, we conduct an experiment with 16 subjects, and generate a novel dataset, which includes audio recordings and time coded transcriptions, visual modalities such as RGB and depth, and camera extrinsic for a 10-minute exploration per subject.
ISUF International Conference, 2015
Proliferation of data centric methods in mapping practices brings about the question of whether they can integrate the urban morphology and its implications on spatial data analysis. While quantitative data are processed within geographic information systems (GIS) framework through an extensive set of spatial data extraction and processing tools, qualitative assessment of city form is mostly a manual task that requires careful examination of formal and material features. In this research, we introduce a novel computational method for the analysis of the morphological features of cities ranging from macro scale street networks to building stock patterns. We use image processing and pattern matching techniques that are often used in computer vision algorithms for the assessment of morphological features that of street networks, parcel and building stock. Through a comparative analysis of four neighborhoods in Istanbul, we show that there is a strong parallelism between socio- economic development and urban form of Istanbul.
