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Fall 2019

Zhang, Tianyu

Occupancy and thermal modelling is the foundation of several smart building applications, such as intelligent control of residential and commercial buildings' Heating, Ventilation and Air Conditioning (HVAC) system to improve energy efficiency and overall occupant experience in the built...

Spring 2024

Zhang, Tianyu

Retrofitting buildings and optimizing their operation have been at the forefront of global efforts to reduce carbon emissions over the past few decades. Intelligent control of building systems, such as Heating, Ventilation, and Air Conditioning (HVAC), presents two clear benefits: it improves...

Spring 2022

Shiva Soleimany Dizicheh

Reinforcement learning (RL) offers agents a framework for learning to perform hard-to-engineer behaviors that other machine learning (ML) approaches cannot due to the complex nature of these problems. However, it is impractical to learn a complex task from scratch due to reasons such as the huge...

Fall 2024

Wei, Dongdong

Operated under changing wind speed and harsh environment conditions, the rotating parts in wind turbine gearboxes, such as gears and bearings, will deteriorate and become faulty over time. By conducting real-time and accurate fault detection and diagnosis before significant failures occur, we can...

Fall 2022

Miahi, Erfan

In this thesis, we investigate the connection between the properties and the generalization performance of representations learned by deep reinforcement learning algorithms. Much of the earlier work on representation learning for reinforcement learning focused on designing fixed-basis...

Fall 2023

Vaid, Samridhi

Deep learning has had much success on challenging problems with large datasets. However, it struggles in cases with limited training data. Transfer learning represents a class of approaches for transferring knowledge from large source datasets to smaller target datasets. But many transfer...

Spring 2022

Sahir

Reinforcement learning (RL) has shown great success in solving many challenging tasks via the use of deep neural networks. Although the use of deep learning for RL brings immense representational power to the arsenal, it also causes sample inefficiency. This means that the algorithms are...

Fall 2023

Singamsetti, Mohan Sai

Deep learning approaches have had success in many domains recently, particularly in domains with large amounts of training data. However, there are domains without a sufficient quantity of training data, or where the training data present is of insufficient quality. Transfer learning approaches...

Fall 2023

Krishna Guruvayur Sasikumar, Aakash

The application of reinforcement learning (RL) to the optimal control of building systems has gained traction in recent years as it can reduce building energy consumption and improve human comfort, without requiring the knowledge of the building model. However, existing RL solutions for building...

Fall 2024

Imani, Ehsan

Classical wisdom in machine learning advises controlling the complexity of the hypothesis space for achieving good generalization. Despite this, modern overparametrized neural networks demonstrate remarkably high generalization performance, oftentimes with larger and more expressive architectures...