Daniel Bolojan is an Assistant Professor and the Director of the Creative AI Lab. He is also one of the leading voices on the application of Creative AI, Deep Learning, and computational design techniques to architecture and architectural design processes. His current research focuses on the development and application of deep learning strategies in architectural design, addressing topics of shared-agency, augmentation of design processes and augmentation of designer’s creativity. Throughout his academic career, he has instructed numerous design studios and seminars at institutions such as the Institute of Structure and Design at the University of Innsbruck and Florida International University Miami. Additionally, he has played a pivotal role in advancing the field through his numerous international workshops and conference workshops that examine the utilization of complex systems and Neural Networks in the realm of architectural design.

Daniel is currently engaged in the pursuit of a Ph.D. degree at the Institute of Architecture, University of Applied Arts, Vienna, Austria. He previously received both his B.Arch and Master's Degree in Architecture from the same institution, where he had the opportunity to study under the late architect Zaha Hadid and Patrik Schumacher at the Zaha Hadid Vienna Studio. Additionally, he served as a Research Fellow on the "Agent-Based Parametric Semiology" research project (funded by the PEEK - FWF Der Wissenschaftsfonds Research Grant). The research project investigated the potential of agent-based systems as simulations of agent-based life processes (architectural crowds) to operationalize the semantic layer in the design process, wherein the semiotic code is defined in terms of the agent's behavioral rules in interaction with various spatial features.

In 2013, he founded his own research studio Nonstandardstudio,a research studio that operates at the confluence of several crucial domains, including creative AI, deep learning, computation, multi-agent systems, generative design, and algorithmic techniques. The studio endeavors to create highly autopeietic systems through the application of generative design strategies and algorithmic techniques. The result of these efforts aims to offer novel opportunities for the architectural organization, articulation, and signification of highly complex systems. 

Upon completion of his studies, Daniel joined the highly esteemed CoopHimmelblau architecture firm in Vienna, Austria, as a Computational Designer. During his tenure at the firm, he was able to gain practical experience on numerous globally recognized projects and competitions. Promptly after joining CoopHimmelblau, he assumed the role of Junior Associate, Computational Design Specialist, and Founder and Head of Chbl|Code. In this capacity, he was tasked with leading the development of custom computational design tools, including standalone apps, plugins, and add-ons, as well as computational design strategies, virtual and augmented reality applications, machine learning, and neural network applications, and robotic fabrication processes. As the Head of Chbl|Code, Daniel is accountable for the firm's ongoing efforts to explore and develop deep learning strategies aimed at enhancing the designer's innate abilities through the creation of the DeepHimmelblau Neural Network.