The AI seminar on 7th Dec 2023 will feature Prof Simon Dobson who will be talking about “Some stuff about sheaves”.
The seminar will be at 11am in JCB1.33a.
If you would like to join online, please email rh347@st-andrews.ac.uk for a link to the call.
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.
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.
Professor Mateu Villaret, from Universitat de Girona is a visiting scholar with the AI group from July 1st until September 30th. Professor Villaret works on algorithms for routing and scheduling with the AI group at St Andrews.
As well as solving practical problems, he also enjoys puzzle games. That is the basis of this talk, about using Planning and SMT to solve the “Snowman” puzzle.
The speaker is Lars Kotthoff, previously a PhD student here, now and Assistant Professor at the University of Wyoming. All welcome.
Often, there is more than one way to solve a problem. It could be a different
parameter setting, a different piece of software, or an entirely different
approach. Choosing the best way is usually a difficult task, even for experts.
AI and machine learning allow to leverage performance differences of
algorithms (for a wide definition of “algorithm”) on different problems and
choose the best algorithm for a given problem automatically. In AI itself,
these techniques have redefined the state of the art in several areas and led
to innovative approaches to solving challenging problems.
In this talk, I will give examples of how AI can help to solve challenging
computational problems, what techniques have been applied, and how you can do
the same. I will argue that AI has fundamental implications for software
development, engineering, and computer science in general — stop making
decisions when coding, having more algorithmic choices is better!
