California Institute of Technology | United States
Dr. Julian Humml, Dr. sc. ETH, is an accomplished researcher and scientist specializing in the integration of machine learning, fluid dynamics, and augmented reality for scientific measurement and human–AI interaction. Since January 2025, he has been serving as a Postdoctoral Researcher at the Graduate Aerospace Laboratories of the California Institute of Technology (GALCIT), where he develops active learning measurement systems and online data assimilation algorithms for large-scale sampling and augmented reality–based human visualization and interaction. His work focuses on leveraging physics-informed machine learning techniques to enhance scientific measurements, particularly within complex aerodynamic environments. Prior to his appointment at Caltech, Dr. Humml gained extensive experience at the Institute of Fluid Dynamics, ETH Zurich (2018–2022), where he contributed to pioneering research in active learning systems for wind tunnel measurements and the augmented reality visualization of flow fields. His work involved the development of robotic manipulator operations optimized for multi-hole pressure probes and collaboration with the Swiss Data Science Center on the SmartAIR project, aimed at intelligent aerodynamic measurement systems. He also gained early industrial experience as an Engineering Intern at Liebherr Aerospace in Lindenberg, Germany (2016), where he managed wind tunnel testing facilities, planned and executed test campaigns, and oversaw equipment upgrades. In parallel with his research activities, Dr. Humml has demonstrated strong teaching and leadership capabilities. As Head Teaching Assistant for Fluid Dynamics I at ETH Zurich, he coordinated multiple exercise groups, prepared course materials and exams, and mentored students. He also served for several years as a Teaching Assistant for the ETH Wind Tunnel Laboratory Course and supervised junior researchers at the Institute of Fluid Dynamics. Dr. Humml earned his Doctor of Science (Dr. sc. ETH) in Mechanical Engineering from ETH Zurich in 2022, under the supervision of Prof. Dr. Thomas Rösgen and co-examiner Prof. Dr. Fernando Perez-Cruz. His doctoral thesis, titled “Self-Guided Machine Learning Algorithm for Real-Time Assimilation, Interpolation, and Rendering of Aerodynamic Measurements”, advanced the field of real-time data-driven aerodynamic measurement and visualization.
Profile: Orcid
Featured Publications
Humml, J., Cohen, V., & Perez-Cruz, F. (2024). Augmented reality guided aerodynamic sampling. In AIAA SciTech Forum 2024, Orlando, FL, USA, January 8-12, 2024 (AIAA 2024-1382). American Institute of Aeronautics and Astronautics.
Kottom, L., Stefan-Zavala, A., Gharib, M., & Humml, J. (2025). Augmented reality and vision-language models to guide humans across manual tasks. Proceedings of the ACM (print version published August 10, 2025).
Humml, J. (2024). Physics-informed machine learning for large-scale sampling and augmented-reality human visualization and interaction.