Tzu-Chieh Kurt Hong
- Assistant Professor of Architecture
Dr. Tzu-Chieh Kurt Hong is the assistant professor of the School of Architecture and Design at the University of Kansas teaching courses in design studios, algorithmic modeling, parametric design and computer programming in design studies, as well as the founder of K9K Research & Design. His research focuses on the areas of shape grammars, visual computing, generative modeling, parametric design, and computer-aided design. From 2016 to 2022, Kurt was a research scientist at the Shape Computation Lab at the Georgia Institute of Technology working with Dr. Athanassios Economou on the Shape Machine, a new shape-rewrite computational technology, designing and implementing its geometry kernel, data representation and system architecture. Before entering the Shape Computation Lab, Kurt was a research scientist at the Digital Building Lab working with Dr. Dennis Shelden, and his main contribution to Digital Building Lab included the implementation of an automatic routing system in architecture and an interactive web-based data visualization platform, Smart 3D Atlanta. Kurt received his Ph.D. degree in Architecture (Design Computation) from the Georgia Institute of Technology; a Master of Science degree in Architecture and Design (MS.Arch) from the University of Michigan (Ann Arbor); a Master of Architecture (M.Arch); a Master degree in Electronics Engineering (MSEE) and a Bachelor degree in Electronics Engineering (BSEE) from the National Chiao Tung University (Taiwan).
Dr. Tzu-Chieh Kurt Hong’s PhD dissertation, Shape Machine: shape embedding and rewriting in visual design, is a research work on the implementation of a shape grammar interpreter, Shape Machine, which allows designers to visually process shape recognition and shape replacement in computer-aided design system (CAD). This dissertation received ARCC (Architectural Research Centers Consotium) Dissertation Award 2022, because of the contributions of shape recognition to CAD systems. In this dissertation, five solutions are proposed respectively to five critical problems that have been identified in this discourse for decades, including: 1) a new framework integrating shape computing system and CAD system to avoid fragmented workflow, 2) a maximal line calibration system to mitigate the precision errors in modeling, 3) a pictorial algorithm to reduce the accumulated errors in transformation derivation, 4) a multi-step procedure to resolve the indeterminate shape embedding, and 5) a new data structure, register point signature, to significantly lower the algorithmic complexity in shape embedding. Shape Machine has been used in three design studios and five elective courses in the School of Architecture and the School of Interactive Computing at the Georgia Institute of Technology since 2019, furthermore, it is also adopted in two workshops in the School of Architecture at the National Taiwan University of Science and Technology in Taiwan.
Selected Publications —
Hong TK, Economou A (forthcoming) Shape Machine: Implementation of Shape Embedding in CAD Systems. Automation in Construction.
Okhoya V, Bernal M, Economou A, Saha N, Vaivodiss R, Hong TK, Haymaker J (forthcoming) Generative Workplace and Space Planning in Architectural Practice. International Journal of Architectural Computing.
Econmou A, Hong TK (forthcoming) Back to the Drawing Board: Shape Calculations in the Shape Machine. Design Computing and Cognition. (DCC) eds: J. S. Gero, Springer.
Hong TK, Economou A (2022) What Shape Grammars Do that CAD Should: The 14 Cases of Shape Embedding. Artificial Intelligence for Engineering Design, Analysis and Manufacturing.
Yu Y, Hong TK, Economou A, Paulino GH (2021) Rethinking Origami: A Generative Specification of Origami Patterns with Shape Grammars. Computer-Aided Design, Volume 137.
Hong TK, Economou A (2020) Five criteria for shape grammar interpreters. Design Computing and Cognition. (DCC) eds: J. S. Gero, Springer, 189-208
Economou A, Hong TK, Ligler H, and Park J (2020) Shape Machine: A Primer in Visual Composition, J.-H. Lee (ed.), A New Perspective of Cultural DNA, KAIST Research Series, Springer Nature Singapore Pte Ltd, pp. 65-92.