I am a computer science researcher and graph enthusiast.
My work focuses on the foundations and applications of machine learning to structured problems. I aim to find ways to exploit (graph, constraint, group) 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 hard combinatorial and bio-engineering problems (especially protein design).
Selected recent works
A. Loukas. What graph neural networks cannot learn: depth vs width. ICLR 2020. (paper, bibtex, blogpost, 5min-presentation)
JB Cordonnier, A Loukas, M. Jaggi. On the relationship between self-attention and convolution. ICLR 2020. (paper, bibtex, blogpost, code, interactive website)
List of publications
Please consult google scholar.
Drop me an email at “[lastname][first two letters of first name]@gmail.com”.
Catch me on twitter, researchgate, or linkedin.