Immanuel Koh

Immanuel Koh is an Assistant Professor in both the pillars of Architecture and Sustainable Design (ASD) and Design and Artificial Intelligence (DAI) at the Singapore University of Technology and Design (SUTD), where he teaches and conducts research on the creative use of deep learning for architectural and urban designs. Currently, he directs the research laboratory Artificial Architecture in designing and developing new bespoke architectures of artificial intelligence learning models to solve complex design problems facing the built environment.

Prior to joining SUTD, he was based at École polytechnique fédérale de Lausanne (EPFL) in Switzerland, doing transdisciplinary research work between the School of Computer Sciences and the Institute of Architecture. His doctoral studies, which was nominated for the EPFL Best Thesis Prize, interrogated the epistemological and formal basis of architecture, by reformulating a new design theory through the conceptual and algorithmic lens of probabilistic sampling in machine learning.

Since graduating from the Architectural Association (AA) London, he has taught at the AA, Royal College of Art (London), Tsinghua University (Beijing), Strelka (Moscow), Angewandte (Vienna), DIA (Bauhaus Dessau), Harvard GSD, UCL Bartlett and many others. His design work has been exhibited internationally, such as at London’s Victoria and Albert Museum, Shanghai’s 3D Printing Museum and Taipei’s Tittot Glass Art Museum; and published widely, such as in Architectural Design (AD) and Design Computing and Cognition. Immanuel has also practiced as an architect at Zaha Hadid Architects (London), as a programmer at ARUP with Relational Urbanism (London), and as a creative coder at Convergeo (Lausanne) and anOtherArchitect (Berlin).

Education

• Doctor of Philosophy - PhD EPFL (École polytechnique fédérale de Lausanne)
• Masters of Architecture & Urbanism Architectural Association Design Research Lab( AADRL), London
• M.Arch National University of Singapore

Companies

• Tenure Track Assistant Professor Singapore University of Technology and Design (SUTD) (2019)
• Researcher | Course Instructor in Computer Science & Architecture EPFL (École polytechnique fédérale de Lausanne) (2016 — 2019)
• Creative Coding Consultant RELATIONAL URBANISM LIMITED (2013 — 2019)
• Unit Master / Workshop Tutor Architectural Association (2010 — 2019)
• Research Scientist Singapore University of Technology and Design (SUTD) (2015 — 2017)
• Visiting Lecturer Harvard University (2015 — 2016)
• Visiting Lecturer University College London (2013 — 2016)
• Interprofessional Studio Tutor Architectural Association (2012 — 2014)
• Director, AA-Taipei Visiting School The Architectural Association, AA Global School (2011 — 2014)

Skills

• Concept Design
• Architecture
• Urban Design

Other

AutoCAD Architecture, Rhino, Architectures, Design Research, Architectural Design, Vray, Digital Fabrication, Computational Design, Parametric Design

Selected Publications

• Koh, I., 2022. Architectural Plasticity: The Aesthetics of Neural Sampling, in Architectural Design (Special Issue: Machine Hallucination: Architecture and Artificial Intelligence). (Forthcoming).
• Koh, I., 2022. Artificial Intelligence and Architecture: From Research to Practice, in: Challiou, S. (Eds.). Birkhauser. (Forthcoming).
• Koh, I., 2022. Machine Intelligence, in: Leach, N., Yuan, P. (Eds.). Springer International Publishing. (Forthcoming).
• Koh, I., 2022, AI-Urban-Sketching: Deep Learning and Automating Design Perception for Creativity, in: B. Romic, B. Reimer (Eds.), Transformations: Journal of Media, Culture and Technology (Special issue: Artificial Creativity), Malmö University, Sweden.
• Lee, Z., Wu, J., Koh, I., Sun, L. & Tang, Y., 2021. Image Synthesis from Layout with Locality-Aware Mask Adaptation, in: International Conference on Computer Vision (ICCV), 11-17 October 2021.
• Koh, I., 2020. Artificial & Architectural Intelligence in Design, Inform / Reform. Architecture and Sustainable Design, Singapore University of Technology and Design, Singapore.
• Koh, I., 2020. Voxel Synthesis for Architectural Design, in: Gero, J.S. (Ed.), Design Computing and Cognition ’20. Springer International Publishing, pp. 303–322.
• Koh, I., 2020. AI-Urban-Sketching in the Age of COVID-19, in: Artificial Creativity Conference. Malmö University, Malmö, Sweden.
• Koh, I., 2020. The Augmented Museum: A Machinic Experience with Deep Learning, in: Holzer, D., Nakapan, W., Globa, A., Koh, I. (Eds.), RE: Anthropocene, Design in the Age of Humans – Proceedings of the 25th CAADRIA Conference – Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 639-648.
• Koh, I., 2019. Architectural Sampling: A Formal Basis for Machine-Learnable Architecture. PhD dissertation, Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland.
• Koh, I., 2019. Discrete Sampling: There is No Object or Field … Just Statistical Digital Patterns. Architectural Design 89, 102–109.
• Koh, I., Huang, J., 2019. Citizen Visual Search Engine: Detection and Curation of Urban Objects, in: Lee, J.-H. (Ed.), Computer-Aided Architectural Design. “Hello, Culture,” Communications in Computer and Information Science. Springer Singapore, pp. 168–182.
• Khokhlov*, M., Koh*, I., Huang, J., 2019. Voxel Synthesis for Generative Design, in: Gero, J.S. (Ed.), Design Computing and Cognition ’18. Springer International Publishing, pp. 227–244. (*both are 1st authors)
• Koh, I., 2019. Machinic Design Inference: from Pokémon to Architecture – A Probabilistic Machine Learning Model for Generative Design using Game Levels Abstractions, in: M. Haeusler, M. A. Schnabel, T. Fukuda (Eds.), Intelligent & Informed – Proceedings of the 24th CAADRIA Conference – Volume 2, Victoria 543 University of Wellington, Wellington, New Zealand, 15-18 April 2019, Pp. 421-430.
• Koh, I., 2018. Inference Design Machine: “Infinite” & “Recombinant” Series, in: Leach, N., Yuan, P.F. (Eds.), Computational Design. Tongji University Press Co., Ltd, Shanghai, pp. 291–296.
• Koh, I., 2018. Learning Design Trends from Social Networks – Data Mining, Analysis & Visualization of Grasshopper® Online User Community, in: T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (Eds.), Learning, Adapting and Prototyping – Proceedings of the 23rd CAADRIA Conference – Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, Pp. 277-286.
• Koh, I., Keel, P., Huang, J., 2017. Decoding Parametric Design Data – Towards a Heterogeneous Design Search Space Remix, in: P. Janssen, P. Loh, A. Raonic, M. A. Schnabel (Eds.), Protocols, Flows, and Glitches – Proceedings of the 22nd CAADRIA Conference, Xi’an Jiaotong-Liverpool University, Suzhou, China, 5-8 April 2017, Pp. 117-126.