The second school seminar on 5th November at 2pm, on Teams. If you do not have the Teams link available please contact the organiser, Ian Gent.

**Dimensionality Reduction in non-Euclidean Spaces**

**Richard Connor**

The second school seminar on 5th November at 2pm, on Teams. If you do not have the Teams link available please contact the organiser, Ian Gent.

Deep Learning (ie Convolutional Neural Networks) gives astoundingly good classification over many domains, notably images. Less well known, but perhaps more exciting, are similarity models that can be applied to their inner layers, where there lurk data representations that can give a much more generic notion of similarity. The problem is that these data representations are huge, and so searching a very large space for similar objects is inherently intractable.

If we treat the data as high-dimensional vectors in Euclidean space, then a wealth of approximation techniques is available, most notably dimensionality reduction which can give much smaller forms of the data within acceptable error bounds. However, this data is not inherently a Euclidean space, and there are better ways of measuring similarity using more sophisticated metrics.

The problem now is that existing dimensionality reduction techniques perform analysis over the coordinate space to achieve the size reduction. The more sophisticated metrics give only relative distances and are not amenable to analysis of the coordinates. In this talk, we show a novel technique which uses only the distances among whole objects to achieve a mapping into a low dimensional Euclidean space. As well as being applicable to non-Euclidean metrics, its performance over Euclidean spaces themselves is also interesting.

This is work in progress; anyone interested is more than welcome to collaborate!

- When: 19th June 2019 11:00 - 12:00
- Where: Cole 1.33a
- Series: AI Seminar Series
- Format: Lecture, Seminar

Can a user write a good MIP model without understanding linearization? Modelling languages such as AMPL and AIMMS are being extended to support more features, with the goal of making MIP modelling easier. A big step is the incorporation of predicates, such a “cycle” which encapsulate MIP sub-models. This talk explores the impact of such predicates in the MiniZinc modelling language when it is used as a MIP front-end. It reports on the performance of the resulting models, and the features of MiniZinc that make this possible.

Professor Mark Wallace is Professor of Data Science & AI at Monash University, Australia. We gratefully acknowledge support from a SICSA Distinguished Visiting Fellowship which helped finance his visit.

Professor Wallace graduated from Oxford University in Mathematics and Philosophy. He worked for the UK computer company ICL for 21 years while completing a Masters degree in Artificial Intelligence at the University of London and a PhD sponsored by ICL at Southampton University. For his PhD, Professor Wallace designed a natural language processing system which ICL turned into a product. He moved to Imperial College in 2002, taking a Chair at Monash University in 2004.

His research interests span different techniques and algorithms for optimisation and their integration and application to solving complex resource planning and scheduling problems. He was a co-founder of the hybrid algorithms research area and is a leader in the research areas of Constraint Programming (CP) and hybrid techniques (CPAIOR). The outcomes of his research in these areas include practical applications in transport optimisation.

He is passionate about modelling and optimisation and the benefits they bring. His focus both in industry and University has been on application-driven research and development, where industry funding is essential both to ensure research impact and to support sufficient research effort to build software systems that are robust enough for application developers to use.

He led the team that developed the ECLiPSe constraint programming platform, which was bought by Cisco Systems in 2004. Moving to Australia, he worked on a novel hybrid optimisation software platform called G12, and founded the company Opturion to commercialise it. He also established the Monash-CTI Centre for optimisation in travel, transport and logistics. He has developed solutions for major companies such as BA, RAC, CFA, and Qantas. He is currently involved in the Alertness CRC, plant design for Woodside planning, optimisation for Melbourne Water, and work allocation for the Alfred hospital.

- When: 21st August 2018 13:30 - 14:30
- Where: Cole 1.33b
- Format: Seminar

**CUHK + UniMelb = Fable-based Learning + A Tale of Two Cities**

**Prof Jimmy Lee, Chinese University of Hong Kong **

This talk reports on the pedagogical innovation and experience of a joint venture by The Chinese University of Hong Kong (CUHK) and the University of Melbourne (UniMelb) in the development of MOOCs on the computer science subject of “Modeling and Solving Discrete Optimization Problems”. In a nutshell, the MOOCs feature the Fable-based Learning approach, which is a form of problem-based learning encapsulated in a coherent story plot. Each video lecture begins with an animation that tells a story based on the Chinese classic “Romance of the Three Kingdoms”, in which the protagonists in the novel encounter a problem requiring technical assistance from the two professors from modern time via a magical tablet bestowed upon them by a fairy god. The new pedagogy aims at increasing learners’ motivation as well as situating the learners in a *coherent* learning context. In addition to scriptwriting, animation production and situating the teaching materials in the story plot, another challenge of the project is the remote distance and potential cultural gap between the two institutions as well as the need to produce all teaching materials in both (Mandarin) Chinese and English to cater for different geographical learning needs. The MOOCs have been running recurrently on Coursera since 2017. Some learner statistics and feedbacks will be presented. The experience and preliminary observations of adopting the online materials in a Flipped Classroom setting at CUHK will also be detailed.

This video at Youtube shows the trailer for the Coursera Course:

**Biography:**

Jimmy Lee has been on the faculty of The Chinese University of Hong Kong since 1992, where he is currently the Assistant Dean (Education) in the Faculty of Engineering and a Professor in the Department of Computer Science and Engineering. His major research focuses on constraint satisfaction and optimization with applications in discrete optimization, but he is also involved in investigating ways of improving students’ learning experience via proper use of technologies. Jimmy is a two-time recipient (2004 and 2015) of the Vice-Chancellor’s Exemplary Teaching Award and most recently the recipient of the University Education Award (2017) at CUHK.

We are delighted to announced that the School of Computer Science has achieved an Athena SWAN Bronze Award, as recognition of our commitment to advancing gender equality.

Almost all teaching staff contributed to the application for the award, as well as many other staff in all categories, research students, masters students, and undergraduates. In congratulating staff, Simon Dobson as Head of School said:

It really does have all our fingerprints on it. The award reflects the fact that we’ve identified things that we wanted to change and have planned how to make them happen: from now on they’ll all just be “how things are” rather than part of an external process.

- When: 10th October 2017 09:30 - 16:00
- Where: Byre Theatre
- Series: Distinguished Lectures Series
- Format: Distinguished lecture

Professor Ursula Martin CBE FREng FRSE joined the University of Oxford as Professor of Computer Science in 2014, and is a member of the Mathematical Institute. She holds an EPSRC Established Career Fellowship, and a Senior Research Fellowship at Wadham College. Her research, initially in algebra, logic and the use of computers to create mathematical proofs, now focuses on wider social and cultural approaches to understanding the success and impact of current and historical computer science research.

Before joining Oxford she worked at Queen Mary University of London, where she was Vice-Principal for Science and Engineering (2005-2009), and Director of the impactQM project (2009-2012), an innovative knowledge transfer initiative. She serves on numerous international committees, including the Royal Society’s Diversity Committee and the UK Defence Science Advisory Council. She worked at the University of St Andrews from 1992 – 2002, as only its second female professor, and its first in over 50 years. She holds an MA in Mathematics from Cambridge, and a PhD in Mathematics from Warwick.

**Timetable:**

9.30 Introduction

9.35 Lecture 1: The early history of computing: Ada Lovelace, Charles Babbage, and the history of programming

10.35 Break with Refreshments Provided

11.15 Lecture 2: Case study, Alan Turing, Grace Hopper, and the history of getting things right

12.15 Lunch (not provided)

2.30 Welcome by the Principal, Prof Sally Mapstone

2.35 Lecture 3: What do historians of computing do, and why is it important for computer scientists today

3.30 Close

IN 1843 Ada Lovelace published a remarkable paper in which she explained Charles Babbage’s designs for his Analytical Engine. Had it been built, it would have had in principle the same capabilities as a modern general purpose computer. Lovelace’s paper is famous for its insights into more general questions, as well as for its detailed account of how the machine performed its calculations – illustrated with a large table which is often called, incorrectly, the “first programme”. I’ll talk about the wider context; why people were interested in computing engines; and some of the other work that was going on at the time, for example Babbage’s remarkable hardware description language. I’ll look at different explanations for why Babbage’s ideas did not take off, and give a quick overview of what did happen over the next 100 years, before the invention of the first digital computers.

Getting software right was a theme of programming for the days of Babbage onwards. I’ll look at the work of pioneers Alan Turing and Grace Hopper, and talk about the long interaction of computer science with logic, which has led to better programming languages, new ways to prove programmes correct, and sophisticated mathematical theories of importance in their own right. I’ll look at the history of the age-old debate about whether computer science needs mathematics to explain its main ideas, or whether practical skills, building things and making things simple for the user are more important.

When people think about computer science, they think about ideas and technologies that are transforming the future – smaller faster smarter connected devices, powered by, AI, and big data – and looking at the past can be seen as a bit of a waste of time. In this lecture I’ll look at what historians do and why it is important; how we get history wrong; and in particular often miss the contribution of of women. I’ll illustrate my talk with my own work on Ada Lovelace’s papers, to show how detailed historical work is needed to debunk popular myths – it is often claimed that Lovelace’s talent was “poetical science” rather than maths, but I’ve shown that she was a gifted perceptive and knowledgeable mathematician. I’ll explain how the historian’s techniques of getting it right can help us get to grip with topical problems like “Fake news”, and give us new ways of thinking about the future.

Ian Gent, Christopher Jefferson and Peter Nightingale have shown that a classic chess puzzle is NP-Complete. Their paper “Complexity of *n*-Queens Completion” was published in the Journal of Artificial Intelligence Research on August 30.

The *n*-Queens puzzle is a classic chess problem: given a chessboard of size *n* by *n*, can you place *n* queens so that no two queens attack each other? That is, can you place the queens with no two queens are on the same row, column, or diagonal? The *n*-Queens puzzle has long been known to be simple to solve: you can solve the problem for all *n* except 2 and 3, and solutions for all other *n* can be described in a few lines. This very simplicity has led to repeated controversy in Artificial Intelligence (AI). The *n*-Queens puzzle is often used as a benchmark problem, but good results on the problem can always be challenged because the problem is so simple to solve without using AI methods.

The new work follows a challenge on Facebook on New Year’s Day, 2015, when a friend of Ian’s asked him how hard *n*-Queens is if some queens were already placed on the board. It turns out, this version (dating from 1850) of the puzzle is only two years younger than the more famous *n*-Queens problem. The picture shows Peter (left) and Ian (right) with queens on the board at positions suggested by Nauck in 1850, the squares b4 and d5. Can you put another 6 queens on the board so that the entire board is a solution of 8-Queens? The general version with some number of queens preplaced on an *n* by *n* board is the *n*-Queens Completion puzzle.

Ian, Christopher and Peter have shown that the *n*-Queens puzzle is in fact hard, not simple. It belongs to the complexity classes NP-Complete and #P-Complete. Other NP-Complete problems include the “travelling salesperson problem”, finding cliques in graphs, and many other important problems, from scheduling to circuit layout. This puts *n*-Queens Completion at the centre of the most important theoretical problem in computer science — it has long been known that either all NP-complete problems are easy, or none of them are. Most computer scientists believe that this means there is no efficient algorithm possible for solving this problem, compared to the very simple techniques long known for *n*-Queens.

The importance of this work is that it provides researchers with a benchmark that can be used for evaluating AI techniques. Moreover, it helps to explain why so many AI techniques have been used on the *n*-Queens puzzle. Most techniques do most of their work with some queens partially placed, using some form of (hopefully intelligent) trial and error. In fact it turns out that many researchers – in order to solve a simple problem – have solved it by turning the simple problem of *n*-Queens into the hard problem of *n*-Queens Completion.

It does seem that AI researchers should not use *n*-Queens as a benchmark, but the very closely related *n*-Queens Completion puzzle is a valid benchmark. As well as the theoretical results, the paper shows how example instances can be generated which appear to be hard in practice. Some caution is still needed, though. It does seem to be quite hard to generate hard instances of *n*-Queens Completion.

The University has also issued an article on the same paper, under the title “Simple” chess puzzle holds key to $1m prize

- When: 23rd June 2017 13:00 - 14:00
- Where: Cole 1.33a
- Series: AI Seminar Series
- Format: Seminar

Abstract: This talk will recap how a watched literal DPLL based SAT solver can be succinctly coded in 20 lines of Prolog. The focus of the talk will be the extension of this solver to an SMT solver which will be discussed with a particular focus on the case where the theory is that of rational-tree constraints, and its application in a reverse engineering problem.

[Note change of time from that previously advertised]

We are delighted to congratulate Iveta Dulova, who attended the 10th BCSWomen Lovelace Colloquium, and walked away with the prize for “Best Final Year Student”. Iveta’s poster, titled “*SensorCube: An end-to-end framework for conducting research via mobile sensing*“, was based on her final year project supervised by Dr Juan Ye.

The event was held at Aberystwyth University on April 12, 2017. Also attending from St Andrews were Chloe Collins, competing in the second year category with the poster “*Pedal to the metal – the role of technology in transportation*” and Laura Brewis with her poster “*What percentage of solitaire games are actually winnable?”.*

It showed great commitment for these three students to undertake the lengthy trip at a busy time of semester. Like St Andrews, Aberystwyth, is a beautiful small seaside town with an excellent Computer Science department. Iveta took a couple of photos showing off the beach and the campus.

- When: 31st March 2017 09:15 - 15:30
- Where: Lower College Hall
- Series: Distinguished Lectures Series
- Format: Distinguished lecture

**School of Computing Science, University of Glasgow**

Lower College Hall (with overflow simulcast in Upper College Hall)

**Abstract:**

Algorithms arise in numerous everyday applications – in this series of lectures I will describe how algorithms can be used to solve matching problems having applications in healthcare settings. I will begin by outlining how algorithms can be designed to cope with computationally hard problems. I will then describe algorithms developed at the University of Glasgow that have been used by the NHS to solve two particular matching problems. These problems correspond to the annual assignment of junior doctors to Scottish hospitals, and finding “kidney exchanges” between kidney patients and their incompatible donors in the UK.

Continue reading

- When: 15th December 2016 14:00 - 15:00
- Where: Cole 1.33a
- Format: Seminar

Jacob Howe, Senior Lecturer at City University London, and sabbatical visitor, will be giving a seminar to the AI Research Group at 2pm on Thursday 15th December in JC 1.33a.

The title and abstract are:

**Propagation and Reification: SAT and SMT in Prolog**

This talk will describe how a watched literal DPLL based Satisfiability (SAT)

solver can be succinctly coded in 20 lines of Prolog. The extension of

this solver to an Satisfiability Modulo Theories (SMT) solver will be discussed with a particular focus on

the case where the theory is that of rational-tree constraints, and its

application in a reverse engineering problem.