Martin Juan José Bucher is a second-year PhD Student at Stanford University, fortunately advised by Iro Armeni & Leonidas Guibas, and part of the Gradient Spaces Lab.

His research interests lie broadly at the intersection of Deep Generative Models, Representation Learning, and Computer Vision — primarily centered on leveraging Generative Models and Structured Scene Representations for a Circular Built Environment. This endeavor is shaping a new frontier that he envisions as Computational Circularity.

Martin holds an MSc in Computer Science (CS) from ETH Zurich (2022) and a BSc in Informatics from the University of Zurich (2019). He is also the recipient of the Stanford HAI Graduate Fellowship (2024).

In the past, he has collaborated at ETH Zurich with Catherine De Wolf in the Circular Engineering for Architecture Lab, with Romana Rust at Gramazio Kohler Research, and with Michael Anton Kraus at Design++. He has also worked with Stefanie Walter and Marco Martini on NLP for Political Economy at UZH.

Originally from Chile, he lived in Santiago de Chile for a few years before moving to Switzerland for the last two decades. In addition to research, he is passionate about speciality coffee, mixing music, arthouse cinema, climbing, and backpacking!

He is actively looking for collaborators, so don't hesitate to reach out.

• Stanford University — USA

  PhD Student

  09/2023 – Present

  Research at the interesection of Deep Generative Modeling, Computer Vision, Representation Learning, Building Information Modeling, and Computational Design.

• ETH Zurich — Switzerland

  Master of Science (MSc), Computer Science

  2019 – 2022

  Focus on Computer Vision, Deep Learning, Machine Perception, Machine Learning, and Deep Generative Modeling.

• University of Zurich (UZH) — Switzerland

  Bachelor of Science (BSc), Informatics

  2015 – 2019

  Broad curriculum ranging from Software Engineering and Economics over to Physical Geography and Foundational Courses in Computer Science.