André Graubner

I am interested in building genetic and epigenetic foundation models, for example with State Space Models.

Before that I built tools, methods and datasets to advance research questions in climate science at Nvidia and the European Space Agency. When appropriate, I like to apply and improve methods from deep learning.

During my undergrad at ETH Zurich, I worked with my supervisor Nora Hollenstein to bridge rationale-based interpretability methods for natual language processing and psycholinguistic data like eye-tracking.

I also spent time as a part-time researcher at Lawrence Berkeley Lab working on large-scale deep learning for climte analytics, supervised by Prabhat and Karthik Kashinath. As a follow-up, I co-led the creation of the largest hand-crafted extreme weather dataset for the European Space Agency and built deep learning models on top of it.

Afterwards, I joined Nvidia as a research intern under supervision of Anima Anandkumar, improving calibration of the FourCastNet neural weather model.

Currently I am a Masters student at Tsinghua University in Wenwu Zhu’s lab, connecting abstraction-based library learning, program synthesis and mathematical reasoning to enable machines to re-organize formal theories based on experience, allowing for better generalisation and interpretability.