"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 bioengineering problems (especially protein design).
I obtained my Ph.D. in computer science from TU Delft in 2015 and pursued postdoctoral studies at TU Berlin and EPFL. I became an SNSF Ambizione fellow at EPFL in 2018 and an Assistant Professor at the Computer Science department of the University of Luxembourg in 2021. I joined Genentech/Roche in 2022.
I co-lead the machine learning effort at Prescient Design, specifically by developing structure (graph, geometry, constrained)-aware neural networks computational molecule design.