Most of my research interests revolve around the design of neural network architectures for slightly unusual data and task modalities. My current focus is on geometric learning, specifically on learning on irregularly sampled signals.
My background is interdisciplinary; I have studied both arts and computer science. Naturally, I hold the intersection of art and AI close to my heart. I believe these two domains complement each other well; AI can serve art by being a medium on its own, rich and open to exploration; art can serve AI by providing a critical outlook on its societal impact, paving the way to more beneficial outcomes.