Telephone: +65 6499 4916
Pillar / Cluster: Architecture and Sustainable Design, Design and Artificial Intelligence
Immanuel Koh is an Assistant Professor in both the pillars of Architecture & Sustainable Design (ASD) and Design & 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 A.I. 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 V&A 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 & 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).
- Ph.D. — Doctorate in Architecture and Sciences of the City, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland.
- M.Arch. II — Masters in Architecture & Urbanism, Design Research Lab (DRL), Architectural Association (AA) School of Architecture, London, UK.
- M.Arch. I — Masters in Architecture, National University of Singapore (NUS), Singapore.
- Artificial Intelligence & New Artificial Design Paradigms
- Creative Machine Learning & Deep Learning for the Built Environments
- Design Computing & Interactive Cognition
- Digital Architecture Theory & Praxis
- Sustainable & Citizen Design Science
Selected Funded Research Project
- Sketching with Deep Neural Networks: Optimizing Design Ideation, SUTD-ZJU IDEA, China.
- Human-AI Interaction: Robot-aided adaptive architecture for disruption management, SUTD SGP-AI, Singapore.
- Design (by Deep Learning) & Deep Learning (by Design), SUTD-SRG, Singapore.
- Artificial Design with MIT’s CSAIL, SUTD-MIT International Design Center, Singapore
- Relational Urban Models with Relational Urbanism, ARUP Research, London, UK
- Ambient Intelligence Lab with Interactive and Digital Media Institute (IDMI), NUS, Singapore
- 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.