I'm an AI Research Scientist at OpenProtein.AI, where I work with Tristan Bepler on generative models for protein design.

Previously, I did my PhD in Computer Science at Aalto University and the ELLIS Institute Finland, advised by Vikas Garg, Markus Heinonen and Samuel Kaski in the QuML group, with a thesis on structured deep learning for science. During my PhD, I was also a visiting ML researcher at Huawei Finland, where I worked on Bayesian inference for personalised health monitoring from hemodynamic signals on wearables. For my doctoral research, I received the Nokia Scholarship, the DAAD AI-Net Fellowship, and an Encouragement Prize from the Finnish Foundation for Technology Promotion. Before that, I studied physics at IISER Mohali as an INSPIRE Fellow, and worked on machine learning for high-energy physics with the University of Zurich on the CMS experiment at CERN, as well as at IFJ PAN and IAS.

My research sits at the intersection of AI and science: I build generative and geometric deep learning models that respect the physics and symmetries of the systems they describe, with applications ranging from proteins, molecules and antibodies to weather and climate.