UCL Cybersecurity CDT


2020/21 student profiles (Cohort 2)

Find out about the current PhD students at the Centre for Doctoral Training in Cybersecurity.

Cohort 2

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Daniel Blackwell

I studied in the UCL MEng Computer Science course from 2014-2018, during the first 2 years of which I worked freelance on various iOS and Android apps; though as time went on this is something that began to appeal to me less and less.

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I spent my 3rd year studying abroad at the Chinese University of Hong Kong, which gave me the opportunity to focus my studies on lower level programming, computer architecture and hardware design languages. Following on from this, in 2017 I spent 5 months working in the Arm CoreSight debug and trace team, researching ways to improve bitstream compression for the storage of instruction trace. In the 2 years following my graduation in 2018 I went on to work as a software developer in a startup supplying real-time medical monitoring to world motorsports series, where I was writing bespoke firmware, designing secure wireless data transmission protocols, as well as writing client side libraries to retrieve the wirelessly transmitted data. Here in the CDT, my research will be focused on identifying information leaks in software.

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Gerard Buckley

I have come full circle. I studied Engineering Science over 40 years ago, was drawn to the world of business, and now have returned to my roots. My interests lie in privacy protection in the face of ever more intrusive surveillance capitalism and what people can do to take back control. 

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I was attracted to the CDT Cybersecurity programme because of its interdisciplinary nature. I have always enjoyed harnessing new technologies, understanding their market potential and then translating that into profitable business products and services. Having completed the MSc in Information Security at UCL in 2020, I am interested in studying technical and regulatory measures that rebalance the asymmetry of power between the common man and big technology. 

In addition to a MA, MAI in Electronic Engineering at Trinity College Dublin, I also hold an MBA. My career follows the path of many engineers – I worked as a software and hardware developer after graduation, moved into marketing and sales in information-intense industries, then director-level management and eventually general management. I founded a global data business at The London Metal Exchange, was CEO of a global financial information services group that successfully floated on the London Stock Exchange and have been CEO of a series of VC and PE-backed hi-tech start-ups in London and Cambridge. 

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Ilaria Pia La Torre

I am a PhD Student in Cybersecurity at UCL's CDT under the supervision of Dr David Clark, Dr Enrico Mariconti and Dr Jens Krinke. I graduated in Italy, from the University of Molise, with a BSc degree in Computer Science in 2016 and with a MSc degree in Security of Software Systems in 2019.

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My interest in the research and in the Privacy and Security areas strengthened during my Erasmus traineeship experience at UCL, in 2018, where I worked on a my MSc dissertation project focused on Information Flow and Fairness testing relating to social networks entitled “Gender Discrimination and Privacy Violation on Twitter”.

On that occasion I was strongly attracted by the stimulating, professional and world-renowned UCL’s research environment, so I felt that it would have been the perfect context in which to give my best, express my problem-solving proneness, steadily feed my passion for the Computer Science and Security field and test my skills in order to extend my knowledge and my future perspectives together with the possibility of feeling fulfilled by a job that allows me to contribute to the progress and to the human life safety.  

Being part of the CDT in Cybersecurity represents a unique opportunity of collaboration to face the interdisciplinary challenges of cybersecurity working in a close-knit environment that allows to share knowledge and learn from experts from different fields.

My PhD research is designed as an extension of my MSc dissertation work and concerns the objective investigation of possible causality in large-scale black box heterogeneous systems with the aim to provide a general method able to detect and describe hidden behaviours of such systems, valid in disparate application contexts from Information Leakage to Unfairness to Image processing and other.

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Alexandros Efstratiou

I am a PhD student at UCL’s CDT in Cybersecurity, supervised by Prof Emiliano De Cristofaro and Dr Tristan Caulfield. My work is broadly situated within the area of computational social science and focuses on the causes and effects of social polarization on social media, especially as this relates to the propagation of misinformation and barriers to healthy information environments. 

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I completed my BSc degree in Psychology from the University of East Anglia and my MSc degree in Behavioural Science from Durham University, graduating top of my class on both occasions. Over the course of my taught degrees I studied, among other things, theories of prejudice, social identities, group psychology, cognitive biases, and decision-making under risk. I have had the chance of incorporating all of these into my current research. 
I was drawn to the CDT because of its interdisciplinarity and its focus on addressing issues from multiple angles. In my time here, I have learned new research methods which I have combined with my previous expertise to tackle interesting problems such as understanding the impact of differential scientific advice on the public’s health attitudes and mapping out the relationships between different communities on social news sites. 

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Stefanos Evripidou

I am a PhD student at the Cybersecurity Centre for Doctoral Training at UCL, supervised by Professor Jeremy Watson, Professor Steve Hailes and Dr Uchenna Ani. My research is centered around the application of socio-technical theory to the security of Cyber-Physical systems.

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Before joining the CDT I was a student at UCL, where I graduated with a Meng in Computer Science, specializing in Information Security in 2020. What attracted me the most to the CDT was its interdisciplinary approach combining both technical and social approaches to Cybersecurity.

My research interests include the application of AI in improving the security of CPS systems, and how to take into consideration the inherently social aspect of such systems to better inform their security. Some examples are the modelling of AI systems that include social attributes and the integration and subsequent evaluation of AI systems into CPS systems under a socio-technical perspective.


Kart Padur
Kart Padur

I am a PhD candidate at the Cyber Security CDT supervised by Prof Stephen Hailes and Dr Hervé Borrion. I am currently focusing my research on how to identify, assess and evaluate hybrid threats.

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During my master studies in a joint Cyber Security program of Tallinn University of Technology and University of Tartu, I understood that cyber security is not only a technical subject, instead, core skills and knowledge are relevant to be acquired across various areas. I applied to the Cyber Security CDT as it offers strong multidisciplinary training which is important to be successful in the field.

Before joining the Cyber Security CDT, I spent a few years in the industry. I was part of an operational risk team in a major bank conducting research on the topic of cyber risk assessment of outsourcing. After, I worked in a fast-growing cyber security company where I focused my research on the human aspects of cyber security risk. I think this experience advanced my understanding of the problematic areas and opportunities that the field of cyber security offers. Continuing my studies as part of the Cyber Security CDT at UCL is a fascinating opportunity for me.

Although everything is currently conducted online, I am amazed how supportive and helpful are my supervisors, the members of the cohort and everybody related to the program. Despite being away from London, I still feel part of the community.

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Dan Ristea

After seven years as a software engineer, I developed a keen interest in privacy enhancing technologies. Privacy exists at the intersection of policy, ethics, and technology, so I was drawn to UCL's Centre for Doctoral Training in Cybersecurity for the unique blend of disciplines which I considered a great environment to pursue privacy-related research.

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My current focus is validating privacy guarantees and I am advised by Dr Steven Murdoch and Dr Enrico Mariconti.

My background is in Computer Science and Software Engineering. I graduated with a BSc (Hons) Computer Science from the University of Edinburgh and went on to work in various Software Engineering roles at internet-focused companies. Working in industry, I saw the practical challenges of implementing the policies and regulations that govern personal data. I also saw privacy come to the forefront of public discourse. I undertook a MSc in Information Security at UCL to delve deeper into the topic.

I strongly believe that privacy is fundamental to human dignitiy and is a requirement for a just society. Regulations, such as GDPR, and algorithms, such as differential privacy, have been developed to protect private data but, ultimately, privacy hinges on correct implementations. Through my research, I want to find ways to evaluate and validate the implementations of systems that provide private access to data and to create tools that allow developers to find and eliminate issues that may compromise the theoretical security guarantees of differential privacy.

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Maria Corte-Real Santos

I am a Doctoral Student at UCL’s CDT in cybersecurity, with a focus on cryptography. Having completed a Master’s degree in Mathematics at the University of Cambridge, I’ve decided to change fields and move into Cyber Security.

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My current research focus is post-quantum cryptography, which aims to find new cryptosystems that can be implemented on classical computers but are secure against both classical and quantum computers. This area of research has seen a recent surge in research due to NIST’s standardisation effort for post-quantum secure public-key encryption and key-establishment algorithms.  There are various types of post-quantum cryptography, all based on different hardness assumptions. Having completed a Master of Mathematics at the University of Cambridge, my background is number theory and algebra, and I am therefore particularly interested in isogeny-based cryptography.

The majority of my research thus far has focused on analysing the hardness of the fundamental problem in isogeny-based cryptography: given two elliptic curves, find a (nice, well-behaved) map between them. In a joint work with Craig Costello and Jia Shi – ‘Accelerating the Delfs—Galbraith Algorithm with Fast Subfield Root Detection’ -- published at CRYPTO 2022, we improve the concrete complexity of the best attack against this problem. In future work, I aim to build new protocols using isogenies, particularly in the distributed setting. To see more of what I’ve been working on, visit my website

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Current student

1. Title: Privacy trade-offs in technology supply chains

Summary: An interdisciplinary approach combining elements of behavioural science, behavioural economics, operations research and privacy economics looks at the privacy behaviours and actions of agents in a supply chain from a 'local' perspective, that is, from an agent's point of view, as well as from a 'global' perspective, in other words, from an exogenous viewpoint.

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The aim of this research is to understand the factors that influence knowledge, information, and behaviour relating to privacy in supply chains to help both consumers/sellers, but also policymakers and regulators.

2. Title: Method-Level Software Security Vulnerability Prediction

Summary: The study will consider the research objectives:

1.    Develop five custom security-specific method-level metrics.
2.    Investigate and nominate five traditional method-level metrics best suited for vulnerability prediction.
3.    Apply both traditional machine learning and deep learning-based anomaly detection techniques to data derived from both traditional and custom metrics.
4.    Evaluate predicted results and optimise the experiment to improve precision and recall, if necessary.
5.    Compare performances between machine learning and deep learning-based anomaly detection techniques to determine the more effective approach.

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Current student

Computer systems form the very foundation of our digital society, empowering individuals and companies globally. However, their ubiquitousness comes with the proliferation of software bugs.

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Bugs arise because software development comprises a complex web of activities that can easily see software developers inadvertently introduce them because of negligence or inexperience. Some of these bugs, otherwise known as vulnerabilities, have security implications. 

When exploited, software vulnerabilities can have varying consequences. These consequences range from mere user inconveniences such as denial of service to something more severe like reputational damage, financial loss or, in the case of safety-critical systems, injury or death. Thus, adequate security controls that do not interfere with productivity are critical in eliminating or mitigating the number of vulnerabilities making their way into final software products. For this reason, software professionals believe that vulnerability prediction holds much promise for software quality assurance; as a result, the concept has gained significant attention over the years. 

The idea of automatically identifying vulnerable areas of a software system mid or post-development appeals to many software researchers. However, despite a tremendous amount of work in the field, there have been varying levels of success. Even more, the idea of a standardised industry adoption remains a pipe dream. 

This research focuses on contributing to the effort of realising a standardised solution that would be integral to secure software development processes. The study explores ways to effectively and efficiently granularly identify vulnerability-prone components in modern software systems. Automating the identification of these at-risk components ensures that security testers can focus their efforts and already-limited resources on the aspects of the software development that need it most.