Engineering is evolving in the same way than society is doing. Nowadays, data is acquiring
a prominence never imagined. In the past, in the domain of materials, processes and …

A suitable feature representation that can both preserve the data intrinsic information and
reduce data complexity and dimensionality is key to the performance of machine learning …

Attempts to incorporate topological information in supervised learning tasks have resulted in
the creation of several techniques for vectorizing persistent homology barcodes. In this …

Sparse model identification by means of data is especially cumbersome if the sought
dynamics live in a high dimensional space. This usually involves the need for large amount …

We consider the problem of supervised learning with summary representations of
topological features in data. In particular, we focus on persistent homology, the prevalent …

Persistence diagrams are one of the main tools in the field of Topological Data Analysis
(TDA). They contain fruitful information about the shape of data. The use of machine learning …

A modern aircraft may require on the order of thousands of custom shims to fill gaps
between structural components in the airframe that arise due to manufacturing tolerances …

Recognition of occluded objects in unseen and unstructured indoor environments is a
challenging problem for mobile robots. To address this challenge, we propose a new …

We establish a new theory which unifies various aspects of topological approaches for data
science, by being applicable both to point cloud data and to graph data, including networks …

There are various methods to predict the wastewater pipe deterioration for the actual and
future conditions of wastewater pipes. These methods can help decision-makers to plan for …