Theses and Dissertations

Spring 2020

Nian, Rui

Machine learning (ML) has shown great potential to create tremendous value and growth to all sectors around the world, enhancing productivity, health, and longevity of humanity. ML differentiates itself from all previous methods through its adaptive and self-learning capabilities. In recent...

Spring 2023

Guevara Urdaneta, Jose L

Finding optimal steam injection policies in the context of Steam Assisted Gravity Drainage (SAGD) represents a major challenge due to the complex dynamics of the process. This complexity is reflected by: i) several concurrent sub-processes, e.g., heat transfer, counter-current flow, imbibition,...

Spring 2023

Wang, Zeyi

The intent of this thesis is to develop a high-performance open-source system that plans with a learned model and to understand the algorithm through extensive analysis. We formulate the problem of maximizing accumulated rewards in Markov Decision Processes, and we frame playing games as such...

Fall 2023

Ray, Debraj

Real-time strategy games require players to respond to short-term challenges (micromanagement) and long-term objectives (macromanagement) simultaneously to win. However, many players excel at one of these skills but not both. This research studies whether the burden of micromanagement can be...

Spring 2024

Guo, Yi

Seismic acquisition constitutes a significant economic commitment, accounting for up to 80% of the overall cost of seismic exploration. This cost is intrinsically linked to the quantity of deployed sensors and sources, each carrying its own set of expenses related to acquisition, deployment, and...

Fall 2021

Chakravarty, Sucheta

Successful learning is of vital importance to human cognition. Accordingly, researchers have been interested to understand brain-activity signals that support it. However, traditional analysis of brain activity is based on planned comparisons and descriptive methods, which can both overestimate...

Spring 2023

Stephens, Connor J

Many practical problems in fields ranging from online advertising to genomics can be framed as the task of selecting the best option from among several choices, based on a limited number of noisy evaluations of the quality of each choice. Pure exploration in multi-armed bandits is an...

Fall 2023

Pramanik, Subhojeet

The transformer architecture is effective in processing sequential data, both because of its ability to leverage parallelism, and because of its self-attention mechanism capable of capturing long-range dependencies. However, the self-attention mechanism is slow for streaming data, that is when...

Spring 2024

Naik, Abhishek

This dissertation develops simple and practical learning algorithms from first principles for long-lived agents. Formally, the algorithms are developed within the reinforcement learning framework for continuing (non-episodic) problems, in which the agent-environment interaction goes on ad...

Fall 2024

Lemouchi, Rafik

This work presents a novel approach to constructing site crane path planning using reinforcement learning and virtual-reality simulations. The approach involves a comprehensive simulation model that includes an agent, actions, states, environment, and a reward system. After extensive training...