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). He directs Artificial-Architecture — an interdisciplinary research laboratory that focuses on the design and development of deep learning models for artificial creativity, generative architecture, predictive urbanism and defence intelligence, with funded projects from industry, academia and government. At ASD & DAI, he teaches courses on the history, theory and practice of artificial intelligence for critical thinking (Artificial & Architectural Intelligence in Design), creative design (Creative Machine Learning) and industry application (Spatial Design Studio).
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 and the Lopez-Loreta Prize, interrogated the formal basis of machine-learnable architecture by formulating a new design theory called Architectural Sampling. He is the author of the book Artificial & Architectural Intelligence in Design (2020) — a first to reflect on the epistemological implications of AI on architecture, and vice versa.
Since graduating from the Architectural Association (AA) London, Immanuel has taught at the AA, Royal College of Art (London), Tsinghua (Beijing), Strelka (Moscow), Die Angewandte (Vienna), DIA (Bauhaus Dessau), Harvard GSD, UCL Bartlett, GAFA (Guangzhou), HIT (Harbin), Makerspace Academy (Bangalore) and many others. His design work has been exhibited internationally, such as at NeurIPS’ AI & Art Gallery, Venice Architecture Biennale, London’s Victoria & Albert Museum, Shanghai’s 3D Printing Museum and Taipei’s Tittot Glass Art Museum; and published widely, such as in Architectural Design (AD), Design Computing & Cognition, CAAD Futures and DigitalFUTURES. 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). He is the co-founder of the international avant-garde collective Neural Architecture Group (NAG), co-curator of the global AIArchitects.org and Singapore’s Art & AI Festival.
- 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.
- Hokkien Foundation Career Professor (2021 to present)
- Artificial Creativity & Design
- Deep Learning for the Built Environment
- Human-AI Design Cognition & Interaction
- Contemporary Digital Design Theory
- Geospatial & Defence Intelligence
- AI Aesthetics in Art, Architecture & Design
- PhD Students
- Visiting Students
- Research Assistants
- Student Interns
Selected Funded Research Projects
- Principal Investigator [PI]. Project VITRUVIUS (AI). Funded by DSO National Laboratories & Temasek Laboratories.
- Principal Investigator [PI]. Sketching with Deep Neural Networks: Optimizing Design Ideation. Funded by SUTD-ZJU Innovation, Design and Entrepreneurship Alliance (IDEA).
- Principal Investigator [PI]. Human-AI Interaction. Funded by SUTD Growth Plan (AI).
- Principal Investigator [PI]. Design (by Deep Learning) & Deep Learning (by Design). Funded by SUTD Start Up Research.
- Principal Investigator [PI]. GameSpace: min(COVID-19) & max(Community). Funded by SUTD Undergraduate Research Opportunities Programme (UROP).
- Co-Principal Investigator [Co-PI]. Cities of Tomorrow Horizontal 1: Building Proposal Development Evaluation with AI. Funded by Urban Redevelopment Authority (URA).
- Co-Principal Investigator [Co-PI]. From BigD to BigD+: An SUTD community led development of Next‐Gen Design Education. Funded by Pedagogy Leadership Grant.
- Collaborator [CL]. Artificial Design with MIT’s CSAIL. Funded by SUTD-MIT International Design Centre.
- Collaborator [CL]. Relational Urban Models with Relational Urbanism. Funded by ARUP Research, London, UK.
- Collaborator [CL]. Ambient Intelligence Lab. Funded by Interactive and Digital Media Institute (IDMI), NUS.
- 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.