I am a third-year PhD student in Seong Joon Oh’s group Scalable Trustworthy AI (STAI), co-located at the University of Tübingen and KAIST AI. I am co-supervised by Hilde Kuehne.

I find it fascinating that large-scale pretraining of generalist foundation models can lead to the emergence of specific capabilities, often beating dedicated specialists (the Bitter Lesson). In my recent work, I show instances of this emergence: LLM-based agents are getting better at narrow, repetitive work [1], and large vision models exhibit ordinal structure competitive with dedicated ranking models [2].

Before this, my engagement with STAI started with a project on neural network loss landscapes. Contrary to the popular belief that loss landscapes are convex modulo permutations, our work revealed evidence that they are rather star-shaped, a weaker form of convexity [3].

Through these projects, I’ve learned that I enjoy facing challenges that bring me to the limits of my intelligence and skills (which happens often). I’ve found it rewarding to produce work that furthers our (especially my own) understanding of existing generalists. In the near future, I want to understand how to build even better generalists. More on this in the coming months.

Visit my homepage for more.