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 that …

Deep neural networks (DNNs) are vulnerable to "backdoor" poisoning attacks, in which an
adversary implants a secret trigger into an otherwise normally functioning model. Detection 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 developing an …

We develop new techniques involving group symmetries and complex analysis to obtain exact
solutions for the transition probabilities of the M/M/1/k queueing process. These methods …

In encrypted search, a server holds an encrypted database of documents but not the keys
under which the documents are encrypted. The server answer keyword queries from a client …

Neural networks can conceal malicious Trojan backdoors that allow a trigger to covertly
change the model behavior. Detecting signs of these backdoors, particularly without access to …

Fundamental stochastic models for studying the dynamics of bike sharing systems can be
found within the transient behavior of the M/M/1/k queue and related stopped processes. We …

Most existing deep reinforcement learning (DRL) frameworks consider action spaces that are
either discrete or continuous space. Motivated by the project of design Game AI for King of …

In order to study the effects of global climate change on regional scales, we need high
resolution models that can be injected into local ecosystem models. Although the injection of …

Healthcare workers (HCWs) are regarded as role models regarding health-related issues,
including vaccination. Therefore, it is essential to identify the predictors for COVID-19 vaccine …