Training neural networks (NNs) using combinatorial optimization solvers has gained
attention in recent years. In low-data settings, the use of state-of-the-art mixed integer linear …

With the expanding role of neural networks, the need for complete and sound verification of
their property has become critical. In the recent years, it was established that Binary Neural …

A key challenge in medical decision making is learning treatment policies for patients with
limited observational data. This challenge is particularly evident in personalized healthcare …

Besides training, mathematical optimization is also used in deep learning to model and
solve formulations over trained neural networks for purposes such as verification …

Artificial Neural Networks (ANNs) are prevalent machine learning models that are applied
across various real-world classification tasks. However, training ANNs is time-consuming …

This thesis explores innovative methodologies in two distinct areas of Applied Mathematics:
Mathematical Modelling in Biosciences and Discrete Optimization. The work is structured …

Binary neural networks (BNNs), which have binary weights and activations, hold significant
potential for enabling neural computations on low-end edge devices with limited …