From Honours Project to Open-Source Application: Developing a Wireshark ILNP Dissector

Wireshark is one of the world’s most widely used network analysis tools, and it comes with great pride that recent graduate Shubh Sinhal’s CS4099 Project “Wireshark and ILNP” has been included within the tool’s official codebase.

Shubh developed a Wireshark ILNP dissector that could be used for the ease of study for researchers and students interested in investigating and testing Identifier Locator Network Protocol (ILNP). Commenting on his work, he stated that his goals were “to identify ILNP flows, validate checksums, and produce tables for tracking and analysing data.” In this way, Shubh’s project adds the ability for Wireshark to detect and analyse ILNP traffic in TCP and UDP segments, check data integrity, and provide new filters and tracking tools for flow analysis. He notes:

I wanted to work on a large, well-known code base to gain experience with complex software and understand how such projects are organised. Since IP is still the dominant protocol, there is little work on new Internet layer protocols, and through the networking modules offered by St Andrews, I gained an interest in Internet architecture and protocol design for communication between devices located across the globe.

The CS4099 module prepares students to design, develop, and test a software system. Pursuing such a project involves students embracing independent research that can have an impactful effect on current software tools. Shubh relays that by supporting ILNP in Wireshark, “it lays the groundwork for potential wider adoption of an alternative internet layer protocol that improves on IPv6 with better mobility and multi-homing capabilities and simpler network management.” This development opens the door for innovation and unexplored opportunities in the future, including “new uses and features, as well as the improvement of performativity,” which in turn could lead to “ILNP becoming a strong alternative to traditional IP.”

With the guidance of his supervisor, Saleem Bhatti, Shubh remarks how the module’s personalized mentorship allowed him to “navigate” a complex code base and “strengthen” his capacity for software development by improving his abilities in understanding existing documentation and code, testing, debugging and producing documentation of his own. Most importantly, he reflects on how the project strengthened his confidence in working with real-world software and networking technologies, as well as improved his ability to work effectively with existing code, therefore giving him the chance to “explore an experimental protocol and contribute to open source by creating a useful tool that others can build on.”

With Shubh Sinhal now being credited on the list of authors for Wireshark, his contribution has shown how research within the School of Computer Science, as well as the engineers graduating from the school, are creating real impact through software applications within academia and beyond.

Information about ILNP can be found at https://ilnp.cs.st-andrews.ac.uk/. With the main part of the codebase Shubh produced being located in the master branch of the Wireshark GitHub repo here.

By Nina Globerson

AI Seminar – Symbolic reasoning with Dr Ruth Hoffmann Wednesday 29 October

Please join us for an in-person event entitled ‘Symbolic reasoning’ on Wednesday 29 October 2025 between 1:30pm and 3pm. To help monitor attendance, please register using the link below. This event is open to all members of the community at the University of St Andrews.  

Speaker: Dr Ruth Hoffman, School of Computer Science, University of St Andrews

Abstract: Symbolic Reasoning (or Symbolic AI) consists of the logical modelling and an exhaustive search for definite solutions to problems. Whether that is finding the solution of a sudoku, finding an optimal route for delivery vehicles or creating kidney matching chains, symbolic AI and logic are the building blocks of this type of reasoning. We will be exploring the foundations of (Symbolic) AI, logic and search, and what type of intelligence it might represent.

Speaker bio: Dr Ruth Hoffmann is a Lecturer at the School of Computer Science, where she is also the Head of the AI Research Theme. She obtained her PhD at St Andrews and has spent some time at the University of Glasgow before returning to St Andrews. She has a background in Discrete Mathematics and Computational Combinatorics. Her research broadly focuses on efficiently finding (smaller) patterns inside bigger (target) structures. Currently, she is teaching the Symbolic AI module she developed.
Ruth is leading an UKRI project on improving search algorithms in one field, while taking inspiration from another search. She is a reviewer for numerous top AI, and Constraints venues, and a co-chair for a coding workshop for GAP (a computational algebra tool).

Register here following the ‘Book tickets’ link (registration is free)

Venue: St Mary’s College: T205 – Lecture Room 2

Directions: St Mary’s Lecture Theatre 2 – entering St Mary’s Quad from South Street, go through the door roughly in the middle of the building on the right, head up the spiral staircase to the first floor and go South (there are also signs).

We hope to see many of you there!

PGR Seminar – Erdem Kus & Junyu Zhang

You are warmly invited to the next PGR Seminar.

Date & Time: Monday 20/10/2025 14:00-15:00

Location: JC 1.33A

  1. Speaker: Erdem Kus

Title: Frugal Algorithm Selection for Combinatorial Search

Abstract: Solvers for combinatorial search and optimisation problems often exhibit highly complementary performance: instances that are hard for one solver may be easy for another. The Algorithm Selection Problem (ASP) addresses this by predicting, for each problem instance, which solver will perform best. Machine learning models trained for this purpose, however, are typically expensive to construct, as they require exhaustive solver runs on all training instances to obtain ground-truth performance data.

In this work, we propose a frugal alternative that formulates algorithm selection as an active learning problem. Instead of uniformly evaluating all solver–instance pairs, our method intelligently selects the most informative ones, thereby drastically reducing the cost of data collection. We show that standard active learning techniques are inadequate for this setting, as they overlook the structure and cost characteristics unique to algorithm selection. To address this, we introduce novel, cost-aware active learning strategies that leverage auxiliary models to balance informativeness and evaluation cost.

Bio: Erdem is a PhD candidate whose research focuses on Artificial Intelligence (AI) and Constraint Programming (CP).

  1. Speaker: Junyu Zhang

Title: Remaking Characters in Heritage Contexts to Support Inclusive Learning

Abstract: Characters in immersive environments have the potential to enrich user experience, improving engagement with heritage and in so doing benefiting heritage organisations and their communities. Creating authentic digital scenes based upon survey, archaeological and historical data, co-creative design and community engagement enables communities and their visitors to understand the past better. The understanding of authenticity stimulates the potential of enriching cultural heritage with the details of lives past and also discusses how this research benefits the Sustainable Development Goals.

Bio: Minty is a PhD candidate exploring the authenticity of characters to support inclusive learning in heritage contexts. She is interested in how digital technologies can be used in the intersection of different disciplines to achieve SDGs in the field of cultural heritage, so as to enhance the promotion, representation, and well-being in digital humanities education and also affect resonated dialogue and thinking among diverse people and communities in facing the current challenges.

We hope you can join us!

Inaugural Lecture showcase Wednesday 15th October

The Inaugural Lecture showcase takes place at Buchanan Lecture Theatre on Wednesday 15th October at 4:00pm.

Professor Richard Connor
‘Finding New Dogs with Old Tricks’

Professor Juliana Bowles
‘The Temporality of Formal Reasoning: As it was, as it is and as it could be’

Professor Susmit Sarkar
‘Peering Inside the Box: Making Modern Computing Safer with Mathematical Specifications’

Please come along if you can and support the school and our colleagues.

Fully-funded PhD scholarship in Artificial Intelligence and Copyright

The University of St Andrews School of Computer Science and the Macquarie University Law School are pleased to offer a Global Doctoral Scholarship in the area of Artificial Intelligence and Copyright Law: The Problem of Authorship. This PhD scholarship is fully-funded and includes fees (home or international) and a stipend for 3.5 years. The PhD will be supervised by Dr Tristan Henderson at St Andrews and Dr Daniela Simone at Macquarie.

Artificial Intelligence (AI) is increasingly important as businesses begin to use it to undertake work traditionally done by humans. As AI systems become more sophisticated and capable, they become more significant in content creation. But the use of AI complicates the determination of authorship of the work created using it; and, as a consequence, the subsistence and ownership of any copyright in that work. This project will investigate the challenges that arise when copyright’s concept of authorship meets AI from an interdisciplinary perspective.

Authorship is at the heart of copyright law in most legal traditions. Copyright protection usually requires some ‘authorial’ input, but jurisdictions differ on the amount and type of human contribution that counts. As AI transforms creative processes and supercharges content generation at scale more clarity is urgently needed. Many are concerned that AI output will displace human creativity – and as such they question whether it should be incentivised via copyright at all. Human creators who turn to AI to augment and complement their own practices will be equally concerned about whether the use of AI tools affects their ability to exploit their work. There is a public interest in ensuring that creativity is encouraged and rewarded.

Whilst it is clear that human input shapes AI output, AI developers and users have much less control over output than a writer does over the words produced by a pen. Is AI output authored in a sense that copyright can, or should, recognise? This question has important implications for the economic exploitation of work made using AI. Uncertainty and variation between the law of different jurisdictions creates economic and cross-border friction. An important and related question is how market participants – both rights-holders and users of creative work – know whether the level of human input rises to the level of copyrightability. Can technology, the law, or both, help provide these participants with sufficient transparency and control over their input?

Where current research focuses on the technological abilities of AI to respect copyright, or the legal application of copyright to AI outputs, we would like to go beyond this and seek student-led proposals on the application and limits of copyright’s concept of authorship from an interdisciplinary computer science (CS) and law perspective. Projects might address questions such as:

  • What amount and type of human contribution suffices to establish authorship of AI output?
  • What sort of causal relationship must exist between human input and AI output to establish authorship?
  • How can human contribution be verified, measured, traced or evidenced?
  • Is control over the output an appropriate touchstone for authorship?
  • How can existing AI systems be adapted to provide sufficient attribution?
  • How do provisions relating to computer-generated works apply?
  • What is the proper scope of copyright in AI output?

This is a joint Computer Science (CS) and Law project, and the successful candidate should have a foundational knowledge in one or both of these disciplines, and a willingness to engage with other related disciplines. Our experience indicates that successful interdisciplinary projects still reside in a ‘home’ discipline, and so we would welcome proposals that use legal approaches such as doctrinal or comparative methods and are informed by CS, as well as CS-driven proposals such as the development of new AI systems that are informed by legal requirements.

The student would join the St Andrews School of Computer Science’s Responsible and Sustainable Research Theme, and be a part of the Scottish Informatics and Computer Science Alliance, a research pool of all 14 computer science departments across Scotland. Depending on the focus of the proposal, the student could join Macquarie’s Data Horizons Research Centre, Centre for Applied Artificial Intelligence, and/or the Ethics and Agency Research Centre. Resources provided by both departments will include computing equipment, conference travel and access to GPU-equipped servers.

Details on how to apply can be found at https://www.mq.edu.au/research/phd-and-research-degrees/how-to-apply/scholarship-opportunities/scholarship-search/ai-and-copyright-law-the-problem-of-authorship and applications should be received by 3 December 2025. Interested applicants are encouraged to make informal contact with the supervisors first by e-mail: Tristan Henderson tnhh@st-andrews.ac.uk and Daniela Simone daniela.simone@mq.edu.au.

PGR Seminar – Qurat ul ain Shaheen

You are warmly invited to the next PRG Seminar.

Date & Time: Monday 13/10/2025 14:00-14:40

Location: JC 1.33A

Speaker: Qurat ul ain Shaheen

Title: A Framework for Uncertainty Sampling in Active Learning

Abstract: Uncertainty sampling is an active learning paradigm where data instances representing maximum uncertainty for a machine learning model are selected for training. This talk will explore existing uncertainty modelling approaches for binary classification of categorical data.  It will introduce a conceptual framework to improve uncertainty modelling and present some preliminary results.

Bio: Qurat ul ain Shaheen is a final year PhD researcher. Her research focuses on modelling uncertainty in active learning.

We hope you can join us!

New Chair in Artificial Intelligence

Professor Lars Kotthoff has been appointed as the Johann and Gaynor Rupert Chair in Artificial Intelligence (AI) at the University of St Andrews.

The role, based in the School of Computer Science, is a crucial appointment to explore and advance the critical role of AI, both now and in the future.

Professor Kotthoff completed his PhD at St Andrews and returns to his alma mater from the University of Wyoming where he was Templeton Chair in Computer Science, Associate Professor in the Electrical Engineering and Computer Science Department, Founding Adjunct Professor in the School of Computing, and Presidential Faculty Fellow.

He has held postdoctoral appointments at the University of British Columbia, Canada, and University College Cork, Ireland. Professor Kotthoff also holds a Diploma from the University of Leipzig, Germany.

His research focuses on developing approaches in Artificial Intelligence and Machine Learning that empowers domain scientists and practitioners to apply state-of-the-art methods to solve real-world problems. Part of this is making cutting-edge research available to, and usable by, non-experts. His contributions span foundational AI, applications of AI in other areas such as materials science, and the development and support of AI tools that are widely used across a broad range of areas.

Of his appointment as The Johann and Gaynor Rupert Chair in Artificial Intelligence at the University of St Andrews, he said: “I very much look forward to re-joining St Andrews after a 13-year absence. AI is becoming increasingly important in all areas of science, and I am excited to explore opportunities with my colleagues in Computer Science and beyond. St Andrews’ world-class staff and collaborative environment make it an ideal location for advancing AI and its application in other areas.”

This position was made possible by a donation from regular participants of the Dunhill Links Championship who collectively donated £3m for an endowed chair in Johann and Gaynor Rupert’s name, as a thank you for the years of participation in the Dunhill Links Championship, and also in thanks for what Mr Rupert has done for golf and St Andrews since the original Alfred Dunhill Cup in 1985.  This new academic Chair is part of the University’s Making Waves Campaign, a £300-million fundraising initiative to foster world-leading research, attract and retain talented academics and students. It also aims to address global challenges by developing new infrastructure and expanding academic and entrepreneurial initiatives.

 

 

 

Young Software Engineer of the Year 2025 Awards

Huge congratulations to Verity Powel, a winner at last night’s Young Software Engineer of the Year Awards (https://www.scotlandis.com/blog/rugby-video-tech-scores-top-award-for-st-andrews-student/). Her final year project “Video Analytics For Rugby Skills Training” was nominated by the school (https://blogs.cs.st-andrews.ac.uk/csblog/2025/07/28/nomination-to-young-software-engineering-of-the-year-awards-2025/) in June. The awards were announced at the ScotSoft 2025 (https://www.scotlandis.com/scotsoft-2025/), Scotland’s leading tech conference at the Edinburgh International Conference Centre.

The Young Software Engineer of the Year accolades are awarded to the best undergraduate software projects from students studying computer science and software engineering in Scotland. Over the years, St Andrews has many finalists and prize winners.

PGR Seminar – David Morrison

You are warmly invited to the next PRG Seminar.

Date & Time: Monday 06/10/2025 14:00-14:40

Location: JC 1.33A

Speaker: David Morrison

Title: Synthetic Whole Slide Image Patch Embeddings for Multiple Instance Learning

Abstract: Obtaining high-quality data is a persistent challenge for the training of computational pathology models. As medical data, Whole-slide images (WSIs) are often held under restrictive terms by medical institutions and, as a result, are hard to access by researchers. Where data is available, the number of whole slide images can be limited and skewed towards common pathology types. In addition, there can be issues with labelling: slide-level labels may lack information about specific pathologies, for example, they may be limited to binary labels of normal or malignant, while annotations at the level of patches are rarely available.

Synthetic data generation is a possible solution to these problems by allowing researchers to produce data on demand that can be used in an unrestricted manner with high-quality labels. I have previously presented on the generation of synthetic patch data. In this talk, I will discuss an extension to this work in which this approach is combined with models trained to characterise the slide as a whole in order to provide a synthesis process for data for use with multiple instance learning techniques, commonly used in whole slide image classification.

We hope you can join us!