Daniele Grandi

As a Sr. Research Engineer at Autodesk Research, I'm working to further the machine understanding of mechanical design problems. I'm interested in leveraging knowledge representation and reasoning, ontologies, knowledge graphs, and semantic technologies to create the next generation of design tools. In collaboration with UC Berkeley, Oregon State University, and MIT, we are researching methods to automatically learn design best practices from CAD databases.

At Autodesk, I've also worked as a Design Engineer on generative design research prototypes. Previously, I worked as an engineering consultant for a metal AM startup, focusing on design, simulation, and optimization of assemblies for AM.

I graduated from UC Berkeley with a Mechanical Engineering degree. I started working with 3D printers at Berkeley, where I founded the 3D Modeling Club. While additive manufacturing had been my main field of focus, I also enjoyed traditional manufacturing methods, applied to mechatronics projects. I enjoy getting my hands on all parts of a project, whether it involves design, coding, circuits, or fabrication.

The first 3D printing startup that I joined was eucl3D, a Berkeley startup working with game developers to provide custom high-quality 3D printed collectibles.

Through Project BAM, the second 3D printing startup I worked at, I learned more about metal additive manufacturing and became interested in design optimization.

Other projects at Autodesk, GE, and Bay Area IP, are sampled below.

Contact me:

Publications

Google Scholar

Meltzer, P., Lambourne, J., & Grandi, D. (2023). What's in a Name? Evaluating Assembly-Part Semantic Knowledge in Language Models through User-Provided Names in CAD Files.

Wang, Y., Goridkov, N., Rao, V., Cui, D., Grandi, D., & Goucher-Lambert, K. (2023). Embedding Experiential Design Knowledge in Interactive Knowledge Graphs. Journal of Mechanical Design, 145(4), 041412.

Goridkov, N., Rao, V., Cui, D., Grandi, D., Wang, Y., & Goucher-Lambert, K. (2022, August). Capturing Designers’ Experiential Knowledge in Scalable Representation Systems: A Case Study of Knowledge Graphs for Product Teardowns. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 86267, p. V006T06A032). American Society of Mechanical Engineers.

Willis, K. D., Jayaraman, P. K., Chu, H., Tian, Y., Li, Y., Grandi, D., ... & Matusik, W. (2022). Joinable: Learning bottom-up assembly of parametric cad joints. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 15849-15860).

Bian, S., Grandi, D., Hassani, K., Sadler, E., Borijin, B., Fernandes, A., Wang, A., Lu, T., Otis, R., Ho, N., & Li, B. (2022, August). Material Prediction for Design Automation Using Graph Representation Learning. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers.

Wang, Y., Grandi, D., Cui, D., Rao, V., & Goucher-Lambert, K. (2021, August). Understanding professional designers’ knowledge organization behavior: a case study in product teardowns. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers.

Ferrero, V., DuPont, B., Hassani, K., & Grandi, D. (2022). Classifying Component Function in Product Assemblies With Graph Neural Networks. Journal of Mechanical Design, 144(2).

Nourbakhsh, M., Morris, N., Bergin, M., Iorio, F., & Grandi, D. (2016, August). Embedded sensors and feedback loops for iterative improvement in design synthesis for additive manufacturing. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 50077, p. V01AT02A031). American Society of Mechanical Engineers.