Daniel Bolojan is an Assistant Professor focusing on AI & computational design and one of the leading voices in the development of deep learning strategies in architecture and architectural design process. Over the years, he has taught several design studios and seminars at the Institute of Structure and Design-University of Innsbruck, Florida International University Miami and conducted numerous international workshops and conference workshops, dealing with the application of complex systems and Neural Networks in architectural design.
He is currently pursuing a Ph.D. at the University of Applied Arts, Institute of Architecture, Vienna – Austria. Daniel received his B.Arch. and Master’s Degree in Architecture from the University of Applied Arts, Institute of Architecture, Vienna – Austria, where he studied under the late architect Zaha Hadid and Patrik Schumacher at the Zaha Hadid Vienna Studio. He later joined the research project “Agent-Based Parametric Semiology” (Research Grant Funding- PEEK – FWF. Der Wissenshaftsfonds) as Research Fellow under the supervision of P.I. – Patrik Schumacher. The research explores agent-based systems as agent-based life process simulations (architectural crowds) in order to operationalize the semantic layer within the design process, where the semiological code is defined in terms of the agent’s behavioral rules when interacting with a variety of spatial features.
In 2013, he founded his own research studio Nonstandardstudio. Over the years, through Nonstandardstudio’s work, Daniel’s design research developed at the intersection between generative design, computation, multi-agent systems, neural networks, deep learning, and machine learning. The studio focuses on generative design strategies and algorithmic techniques that target the creation of highly complex autopoietic systems that could offer new opportunities for the architectural organization, articulation, and signification. These strategies emerge from growth processes, rule-based, multi-agent systems and bottom-up driven design.
Upon graduation, Daniel joined the internationally renowned architecture office CoopHimmelblau, Vienna – Austria, as Computational Designer. There he had the opportunity to practice on numerous internationally renowned projects and competitions. Shortly after joining CoopHimmelblau, Daniel held the position of Junior Associate, Computational Design Specialist & Founder and Head of Chbl|Code. As Head of Chbl|Code, he held the leading role of developing custom computational design tools (e.g. standalone apps, plugins, and add-ons), computational design strategies, virtual and augmented reality applications, machine learning, and neural networks applications, as well as robotic fabrication processes. He is responsible for the office’s current drive to develop deep learning strategies aimed at the augmentation of the designer’s native abilities through the development of the DeepHimmelblau Neural Network.
deep learning, creative ai, generative algorithms, genetic algorithms, parametric design, advanced computational design, architecture