I am a research scientist (Ambizione fellow) at the LTS2 lab in EPFL, Switzerland.
My research focuses on the foundations and applications of machine learning to structured problems. I aim to find ways to exploit (relational, group, constraint) information, with the ultimate goal of designing algorithms that can learn from fewer data. I am also fascinated by the theoretical analysis of neural networks and in using them to solve bio-engineering problems (especially protein design).
Selected recent works
Y. Dong, J.B. Cordonnier, A. Loukas. Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth. Oral at ICML 2021. (preprint, bibtex, blogpost)
N. Karalias, A. Loukas. Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs. Oral at NeurIPS 2020. (paper, bibtex, video starts at 36:54, blogpost, slides, talk at Yale)