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Writing a master's or ph.d. thesis, latex dissertation template, how to graduate: the paperwork, pandemic update (spring 2021).

Note: What appears below, with permission, began as a slightly edited version of unofficial advice from MGSA, the UC Berkeley Mathematics Graduate Student Associate, with Berkeley specific information removed. Over time this document has evolved to contain a great deal of UC Davis-specific information as well as current LaTeX templates.

(Thanks to Megumi Harada) Steps to your goal:

• Start early . Jot down notes when you talk to your advisor. Keep them somewhere that you'll be able to find them again. Often, you find that little notes you write can serve as a “seed” to start the writing process.
• Write a little each day. Obvious, and yet few people follow this advice. Even just one hour a day, divided into four 15-minute sessions, goes a long way if you keep at it for a month.
• Get your advisor to look at small parts of drafts and chapters early on.

All information is subject to change. Current information specific to filing a doctoral thesis at UC Davis can be found via the Grad Studies website .

There are several websites dedicated to Using LaTeX to Write a PhD Thesis . But since you're in the UC Davis math department, your best bet is to use a template that was specifically created for UC Davis math students.

Download: 2024 template (Overleaf compatible)

This template was created originally by Tyrell McAllister with later edits by Jeff Irion, John Challenor, Will Wright, and David Haley in order to keep the template current with formatting requirements. In 2024, Greg DePaul updated the template to be overleaf compatible.

Once you pass your Qual, download the dissertation template! Fill in some basic info (name, major, committee members' names, etc.), and add to your dissertation while you work on your research. After all, it's easier to write and cite as you go than to do it months/years later when that stuff isn't fresh in your mind.

This is an unofficial guide . Please see Preparing & Filing Your Thesis or Dissertation for the official set of instructions.

• Take note of the filing deadlines . They recommend completing your dissertation in time to give it to your committee for review at the beginning of the quarter in which you intend to graduate. In most cases, your committee probably won't need that much time, but you should check in with your committee members well in advance to make sure they'll be around when you need them.
• Once your committee members have signed your dissertation's title page, you can file your dissertation electronically (see the Grad Studies website linked above for the specific instructions). The Grad Studies office will review it to make sure that it is formatted correctly and let you know if any corrections need to be made. They will then inform you when your dissertation has been approved, after which time no further changes are allowed.
• The final step is to make an appointment with the Senior Academic Advisor for Mathematics and Applied Mathematics (as of Spring 2020, this is Brad Wolf ). This appointment can't take place until at least two business days after you file your dissertation online (and it must have been approved). The purpose of this appointment is to file the remaining paperwork and receive an official letter confirming your degree.

As of Spring 2021, the documents you will need to bring are as follows (see the official checklist here ).

• Your title page with the signatures of your committee members.
• One copy of your abstract.
• The dissertation embargo agreement (this requires your advisor's signature even if you aren't planning on an embargo on your dissertation).
• Two online surveys: The Survey of Earned Doctorates and the Graduate Studies Exit Survey. Each will give you a completion code which you will need to provide.
• The Graduate Program Exit Information form. As of Spring 2021, this requires a signature from either Tina or Vanessa.

The three documents with signatures obviously must be physical copies. The other item can be emailed to the Academic Advisor when you set up your appointment.

Given that we've all become hermits here in our underground bunkers, only occasionally opening Zoom to remind ourselves that other people exist in this world, a few changes have been made to the process. These may or may not be permanent changes going forward. Always consult the Graduate Studies page if you are unsure about current requirements.

• The title page must be a single form signed by all committee members, but those signatures can be electronic. It must still be ONE form. As having multiple persons sign a single PDF is not as easy as it sounds, I recommend to download the Word Template that is provided on the Graduate Studies page . It can easily be edited to contain your information, and adding signatures to a Word document is arguably simpler than teaching your committee members to add signatures to a PDF without accidentally closing it to future edits.
• The signed title page must be submitted by your committee chair, or else your program chair (the coordinator might also work – check me on this). Since your committee chair is also one of the people who will be signing your dissertation, it makes sense to have them sign last, and then they can send the form to the Senior Academic Advisor directly. You cannot submit the title page yourself – this is a measure implemented to help prevent fraud and forgery.
• All documents can be submitted digitally to the Senior Academic Advisor (and ideally, all in the same email, except the title page). You'll probably want to then schedule a Zoom appointment with the Advisor to go over your materials and make sure that they are complete.
• Remember that you must schedule an exit seminar before the Program Exit Information form can be signed.

Older Templates

The “ucdavisthesis” LaTeX package maintained by Ryan Scott has a number of convenient macros that can generate a dissertation. It is loosely based on an earlier version of the math department template. It is also available in most TeX distributions by default (including Overleaf), and so accessing it is also convenient. However, editing the package to keep up with changes to formatting requirements requires some thought. To that end, you might be interested in one of the following two documents which bring the template up to date with Spring 2021 requirements:

• ucdavisthesis.cls : Add this to the root directory of your Overleaf document
• ucdavisthesis.dtx : Use this version if you are the kind of person who likes to compile your own stuff

The 2020 version of the math department template can be found here . It differs from the 2021 version only in the way that the page numbers are written (it places hyphens on either side of roman numerals, but not on the other pages).

A previous template that complied with UCD's requirements as of 2012 was created by Sean Whalen and can be found on GitHub . It has not been maintained since 2012 and so is not up-to-date with the current formatting requirements.

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American Literature

Dissertations & theses databases.

• Networked Digital Library of Theses and Dissertations (NDLTD) This link opens in a new window The Networked Digital Library of Theses and Dissertations (NDLTD) is an international organization dedicated to promoting the adoption, creation, use, dissemination and preservation of electronic analogues to the traditional paper-based theses and dissertations. This website contains information about the initiative, how to set up Electronic Thesis and Dissertation (ETD) programmes, how to create and locate ETDs, and current research in digital libraries related to NDLTD and ETDs.
• Open Access Theses and Dissertations This link opens in a new window OATD.org aims to be the best possible resource for finding open access graduate theses and dissertations published around the world. Metadata (information about the theses) comes from over 1000 colleges, universities, and research institutions. OATD currently indexes 2,311,795 theses and dissertations.
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• Last Updated: May 22, 2024 2:25 PM
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Computer Science Theses

Permanent uri for this collection.

This collection is made up of doctoral and master theses by research, which have been received in accordance with university regulations.

For more information, please visit the UCD Library Theses Information guide .

Recent Submissions

• Publication Navigating Academia – Recommender Systems for Module Exploration ( University College Dublin. School of Computer Science, 2022 ) Hagemann, Nina   ; 0000-0001-7705-2368 Personalised recommendations feature prominently in many aspects of our lives, from the movies we watch to the news we read and even the people we date. However, one area that is still relatively underdeveloped is the educational sector, where recommender systems have the potential to help students in a variety of ways, support- ing their decision making when choosing a suitable university programme, finding the right study material, and making informed choices about their learning pathways. This work focuses on recommender systems for academic advising, helping students find the most suitable modules. Today’s students enjoy various options regarding the availability of courses and modules, encouraging students to broaden their horizons, explore their interests and strengths, and develop new skills. One such opportunity offered in many universities is the possibility to freely choose elective modules from outside a student’s primary area of study. Taking such elective modules is often a requirement and can significantly impact students’ academic experience and overall performance. In this thesis, we explore how recommender systems, and content-based approaches, in particular, can be used to support students in finding suitable modules, shape their academic and career paths, as well as gain knowledge and make more informed decisions. Our approach is based on the textual descriptors that are freely available on universities module catalogues to match students with modules based on their learned interests and preferences. In contrast to the majority of related work in the field, our approaches work independently of students’ demographic, personal, and performance data. We show how the module descriptors can be used to extract module similarities and latent topics that allow for rich visualisation options and personalised module recommender systems. We evaluate our approach using offline and online studies. In a live user study, we show that our approach can improve student knowledge about their subject and elective module options. Furthermore, the results show that the participating students largely enjoy interacting with the system and show a high likeliness of reusing the system again in the future.   218
• Publication Development of a Ransomware Investigation Playbook for the Financial Sector, in compliance with ISO/IEC 27043 ( University College Dublin. School of Computer Science, 2022 ) Clancy, Diarmaid   Within the field of digital forensics, incident response and investigation, many groups have developed and evolved their own methods and procedures for conducting investigations of incidents in the digital space, until the creation of ISO/IEC 27043 in 2015. This was an attempt to harmonise existing methods into a single model, however the Standard is intentionally generalist and non-industry specific. This is why we have developed an augmented version tailored for the financial services sector, in the hope that this will assist the reader in both comprehending and implementing ISO/IEC 27043 within their own organisation, thus increasing compliance. Specifically, we have developed and evaluated a playbook for ransomware incident investigation that is practical without sacrificing compliance.   329
• Publication Data-driven Quality of Experience for Digital Audio Archives ( University College Dublin. School of Computer Science, 2022 ) Ragano, Alessandro   The digitization of sound archives began to safeguard records that naturally deteriorate due to the irreversible chemical processes of the sound carriers. The digitization process has improved the usability and accessibility of audio archives and provided the possibility of using digital restoration. Assessing the quality of digitization, restoration, and audio archive consumption is essential for evaluating sound archive practices. The state-of-the-art in digitization, restoration, and consumption of audio archives has neglected quality assessment approaches that are automatic and take into account the user's perspective. This thesis aims to understand and define the quality of experience (QoE) of sound archives and proposes data-driven objective metrics that can predict the QoE of music audio archives in the absence of human listeners. The author proposes a paradigm shift to deal with the problem of quality assessment in sound archives by focusing on quality metrics for musical signals based on deep learning which are developed and evaluated using annotations obtained with listening tests. The adaptation of the QoE framework for audio archive evaluation is proposed to consider the user's perspective and define QoE in sound archives. The author, in a case study of audio archive consumption, proposes a curated and annotated dataset of real-world music recordings from vinyl collections and three objective quality metrics. The thesis shows that annotating a dataset with real-world music recordings requires a different approach to prepare the stimuli and proposes a technique based on stratified random sampling from clusters. The three proposed quality metrics are based on learning feature representations with three different tasks: degradation classification, deep convolutional embedded clustering (DCEC), and self-supervised learning (SSL). The first two tasks are proposed using an architecture based on framewise convolutional neural networks, while the SSL task is based on pre-training and fine-tuning wav2vec 2.0 on musical signals. This thesis demonstrates that degradation classification, DCEC, and wav2vec 2.0 learn useful musical representations for predicting the quality of vinyl collections. More specifically, the proposed metrics overcome two baselines when fine-tuning small annotated sets. The author also proposes a new correlation-based feature representation for classifying audio carriers, which overcomes the raw feature representations in terms of speed and feature dimensionality. Classifying audio carriers can be used as a pre-step of the quality metrics mentioned above when predicting the quality of multiple collections. The significance of the proposed work is that audio archive metadata can be enriched by providing quality labels using the proposed metrics. Overall, the thesis encourages scholars and stakeholders to a paradigm shift when evaluating the quality of sound archives i.e. moving from a manual system-centric approach to a more automatic user-centric approach.   434
• Publication Effective Deep Learning Based Methods for the Anomaly Detection in Software-Defined Networks ( University College Dublin. School of Computer Science, 2022 ) Abdallah, Mahmoud Said ElSayed   ; 0000-0003-2416-7481 In the traditional IP networks, the functionality of decision making processes known as control plane and the forwarding of network traffic (data plane) are implemented within the network devices (e.g. routers or switches). The network operators configure traffic policies (e.g. routing, switching, quality of service) on each device independently. However, the aforementioned architecture increases the operational costs and makes it challenging to adapt and maintain the network configurations security on-demand. Hence, Software-defined Networks (SDN) is an emerging networking paradigm, which has the characteristics to allow more flexibility in network management. SDN accelerates network innovation by centralising the control and visibility across the network (i.e. set policies and prioritise traffic through a centralised controller). However, security has become a serious concern which impedes the widespread adoption of SDN. The new network architecture introduces new potential attack surfaces that did not exist before or are harder to exploit. One of the most common and serious types of attacks is Distributed Denial of Service (DDoS) attack, which can prevent normal users from access their network services. ~If the attacker successfully floods the SDN controller with a massive number of requests, the entire network turns into a ‘body with no brain’. Therefore, detecting these attacks is considered one of the most essential topics for the anomaly detection community. Intrusion detection systems (IDSs) are the standard security solutions to protect the network from malicious activities. Recently, several Machine Learning (ML) approaches have been proposed to provide a framework for securing SDN networks from intrusion attacks. However, the current work that applied ML for intrusion detection depends heavily on feature engineering to choose the right feature set. The evolving nature of network attacks and the rapid change of the attacker techniques makes these methods not suitable for attack detection in real-time. Since learning the complex relationships among different features requires prior knowledge from experts, and thus it is problematic and susceptible to lag. Besides the aforementioned limitations, one of the main challenges in deployment of detection mechanisms is the lack of realistic datasets for SDN networks. Most of the research community uses intrusion detection datasets, which are generated for IP traditional networks. The objective of this research dissertation is to develop an efficient and effective intrusion detection technique using Deep Learning (DL) algorithms to detect malicious activities in the SDN architecture. Firstly, we solved the lack availability of intrusion detection datasets by producing a new specific dataset for SDNs. The dataset contains the new attacks, which are generated as a result of separating the control plane from the data plane. Secondly, we developed a new detection approach based on DL techniques (DDoSNet) to solve the problem of DDoS attacks in SDN networks. The proposed approach has combined the autoencoder with the long short term memory (LSTM) algorithm to improve the detection rate of the DL approaches. Thirdly, we develop a new detection method by using the convolutional neural network (CNN) to reduce the weight explosion of the traditional neural networks. A new regularisation technique based on standard deviation has been deployed to avoid the overfitting problem and enhance the model performance for unknown attacks. The experimental results show that the developed approach has the capability to detect the known and new attacks as well with high performance rate. Finally, we produce a new DL method based on semi-supervised learning to tackle the problem of unlabeled and unbalanced datasets for network traffic. The obtained results for all experiments approved the potential of DL algorithms in anomaly detection techniques.   461
• Publication User-centred Digital Health in Cardiovascular Rehabilitation and Self-management ( University College Dublin. School of Computer Science, 2022 ) Tadas, Shreya   ; 0000-0003-1330-2406 An acute cardiac incident is a life changing event and people face emotional and physical challenges during their transition from hospitalisation to self-management. Supervised rehabilitation programs, like, cardiac rehabilitation play a vital role in supporting this transition. Lack of knowledge, transportation, and motivation limits the uptake of such programs. Increasingly, sensor technologies providing patient-generated data are showing potential to overcome these limitations. But, evidence regarding its routine use and effectiveness is mixed and the commonly reported barriers include insufficient time, data lacking context, unfamiliar structure, misaligned objectives, usability, and reliability issues. Therefore, a greater understanding of patients’ experiences and factors that impact their behaviour after hospitalisation is needed to design such technologies. Also, to increase their success when deployed in real-world clinical contexts, designing by integrating both clinicians' and patients' perspectives is important. User-centred design approaches emphasise the importance of situating user experiences, needs, and preferences as the driver of the digital intervention design. Given the strong evidence from the field of human-computer interaction that user-centred and iterative design methods increase the success of digital health interventions, limited studies were identified that involved users in the design process and applied iterative methods. To contribute new insights to an area lacking in empirical research, this thesis applies the user- centred design methods and the co-design framework to design technology-mediated solutions to support cardiac rehabilitation and self-management. This thesis engages more directly with patients’ and clinicians’ post-hospitalisation experiences and the impact of patient-generated data through a series of studies. Four studies were conducted to achieve the aims of the thesis: a qualitative systematic grounded theory literature review; semi- structured interviews with cardiac patients; co-design study with cardiac rehabilitation clinicians; and field study for system deployment in real-world clinical context. Building on the collective findings of the studies conducted in this thesis, empirically grounded user-centred recommendations are presented to improve the design of technology-mediated support for CR and self-management. The key design recommendations presented in this thesis include: (i) the use of technology to support a normal life, leveraging social influences to extend participants’ sense of normality; (ii) the use of technology to provide both emotional and physical safe zoning; (iii) a focus on recognising capability and providing recommendations that are positive and reinforce this capability; (iv) supplementing objective data from consumer wearable devices with subjective patient experience data to enable meaningful and actionable insights for clinicians; (v) adopting structured approach to subjective data collection grounded in the clinicians’ workflow and co-designed with the clinicians to allow for such data to be shared in a familiar presentation; (vi) the importance of carefully considering the timing, type of App, context, and type of data presentation while sharing data between patients to avoid negative consequences and to empower patients to use technology to self-manage their condition.   324
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Theses Information: Theses Request Form for External Library Users

Thesis request form - james joyce library (jjl).

NOTE: UCD students and staff should request theses via the Library catalogue. Please see  the instructions on consulting UCD theses  on how to do this.

To request a thesis if you do not hold a UCD account and card, please complete the form below.

Theses are available for collection from the Level 1 Information Desk  the day after your request is submitted (Monday – Thursday). 

Theses held at the JJL are available for consultation purposes within the Library only .  The consultation period is for two weeks, during which readers may collect and return theses as required to the information desk.  Readers can consult up to three theses at any one time.

Thesis Request Form

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