Simon Fraser University

In this talk, we focus on the problem of reconstructing a multivariate function from discrete d-dimensional samples. Beyond achieving accurate function recovery, we aim to enhance interpretability by identifying how individual variables and their...

Abstract TBA

Geophysical inversion is a well-established method for conducting non-invasive surveys of underground anomalies, such as buried infrastructure. Typically, inversion is performed using numerical methods: gradient descent and physics-based simulations...

Despite only comprising about a quarter of the total population of Canada, foreign-born individuals bear about three-quarters of the burden of active tuberculosis (TB) cases. To investigate the impact of immigration on the burden of TB among foreign...

In this talk, I will present some recent advances for analyzing stochastic optimization methods without the bounded variance assumption. It is well-known that the bounded variance assumption is violated for even the most standard problems such as...

In arithmetic geometry, local-to-global principles capture the ways in which one approaches difficult "global" questions over number fields by studying their "local" analogues over $p$-adic fields. These principles often fail for questions about the...

We construct explicit examples of abelian surfaces whose 2-torsion Galois representations are not self-dual. Such surfaces are necessarily not principally polarized. We consider abelian surfaces with a polarization of type (1,2) instead. Our main...

Let $A$ be an abelian surface. We investigate curves in a linear system on the dual abelian surface $\hat{A}$. There is an isomorphism of moduli spaces due to Yoshioka between the space $K_3(A)$ parametrizing 0-dimensional length-4 subschemes on $A$...

Minimizing energy dissipation in driven stochastic systems is a fundamental goal in nonequilibrium thermodynamics. In the linear-response (slow driving) regime, this becomes a problem of Riemannian geometry: The control space is equipped with a...

Recent applications in machine learning have renewed the community’s interest in min-max optimization problems. While gradient-based optimization methods are widely used to solve these problems, there exist many scenarios where such techniques are...