Understanding signals from neutron stars requires knowledge about the transport inside the
star. We review the transport properties and the underlying reaction rates of dense hadronic …

Inspired by recent successes of Monte-Carlo tree search (MCTS) in a number of artificial
intelligence (AI) application domains, we propose a reinforcement learning (RL) technique …

I consider the thermal conductivity and shear viscosity of leptons (electrons and muons) in
the nucleon neutron star cores where protons are in the superconducting state. I restrict the …

It has long been a challenging problem to design algorithms for Markov decision processes
(MDPs) with continuous states and actions that are provably approximately optimal and can …

Focusing on CNC process data, this paper provides an in-depth analysis, characterization
and mining of macro machining processes by associating CAD and CAM models. The article …

Deep reinforcement learning methods have achieved state-of-the-art results in a variety of
challenging, high-dimensional domains ranging from video games to locomotion. The key to …

In this paper, we present Randomized Empirical Value Learning (RAEVL) algorithm for
MDPs with continuous state and action spaces. This algorithm combines the ideas of …

This thesis is about optimal decision making for resource allocation problems and
sequential decision problems. There are two parts to this thesis: the first part focuses on …