- Statistical and computational methodology in genetics, cancer biology, metagenomics, and morphometrics;
- Bayesian methodology for high-dimensional and complex data;
- Machine learning algorithms for the analysis of massive biological data;
- Integration of statistical inference with differential geometry and algebraic topology;
- Stochastic topology;
- Discrete Hodge theory;
- Inference in dynamical systems.
Zachary P. Adams et al.
Meta-posterior consistency for the Bayesian inference of metastable system
Shreya Arya et al.
A sheaf-theoretic construction of shape space
Samuel I. Berchuck et al.
Scalable Bayesian inference for the generalized linear mixed model
Marzieh Eidi et al.
Higher order bipartiteness vs bi-partitioning in simplicial complexes
Henry Kirveslahti et al.
Representing fields without correspondences : the lifted Euler characteristic transform
Andrea Agazzi et al.
Global optimality of Elman-type RNNs in the mean-field regime
Youngsoo Baek et al.
Asymptotics of Bayesian uncertainty estimation in random features regression
Youngsoo Baek et al.
Generalized Bayes approach to inverse problems with model misspecification
Michele Caprio et al.
Concentration inequalities and optimal number of layers for stochastic deep neural networks
Michele Caprio et al.
Ergodic theorems in dynamic imprecise probability kinematics
Marzieh Eidi et al.
Irreducibility of Markov chains on simplicial complexes, the spectrum of the discrete Hodge Laplacian and homology
Nicolas Fraiman et al.
The shadow knows : empirical distributions of minimum spanning acycles and persistence diagrams of random complexes
