I am a research scientist at Bosch Research (Sunnyvale, USA), where I work on computer vision problems for autonomous driving. I’m broadly interested in model interpretability and the “science” of deep learning — figuring out what deep models are actually doing, and why they work as well as they do.

I completed my PhD at EPFL / Idiap with François Fleuret, and a postdoc at Harvard with Hima Lakkaraju. Some representative papers:

• splice — CLIP representations can be decomposed into human-readable concept vectors, useful for auditing and editing vision-language models
• perceptually aligned gradients — explains why robust models produce cleaner saliency maps, connecting two seemingly unrelated phenomena
• forgetting data contamination — benchmark contamination in LLM pre-training is often simply forgotten during training, and may not inflate scores as commonly assumed

For more, please see my research themes and publications. I’m always happy to chat about interpretability or the science of deep learning — feel free to reach out!