AI Seminar Series

AI Seminar Tuesday 19th November – Francesco Leofante

The School is hosting an AI seminar on Tuesday 19th November at 11am in JCB1.33A/B

Our speaker is Francesco Leofante from Imperial College London.

Title:

Robustness issues in algorithmic recourse.

Abstract:

Counterfactual explanations (CEs) are advocated as being ideally suited to providing algorithmic recourse for subjects affected by the predictions of machine learning models. While CEs can be beneficial to affected individuals, recent work has exposed severe issues related to the robustness of state-of-the-art methods for obtaining CEs. Since a lack of robustness may compromise the validity of CEs, techniques to mitigate this risk are in order. In this talk we will begin by introducing the problem of (lack of) robustness, discuss its implications and present some recent solutions we developed to compute CEs with robustness guarantees.

Bio:

Francesco is an Imperial College Research Fellow affiliated with the Centre for Explainable Artificial Intelligence at Imperial College London. His research focuses on safe and explainable AI, with special emphasis on counterfactual explanations and their robustness. Since 2022, he leads the project “ConTrust: Robust Contrastive Explanations for Deep Neural Networks”, a four-year effort devoted to the formal study of robustness issues arising in XAI. More details about Francesco and his research can be found at https://fraleo.github.io/.

AI Seminar Friday 18th October – Leonardo Bezerra

The School is hosting an AI seminar on Friday 18th October at 11.30am in JCB1.33A!

Our speaker is Leonardo Bezerra from the University of Stirling.

FAIRTECH by design: assessing and addressing the social impacts of artificial intelligence systems

In a decade, social media and big data have transformed society and enabled groundbreaking artificial intelligence (AI) technologies like deep learning and generative AI. Applications like ChatGPT have impacted the world and outpaced regulatory agencies, who were rushed from a data-centred to an AI-centred concern. Recent developments from both the United Kingdom (UK) and the United States (US) originated in the executive branch, and the most advanced Western binding legislation is the European Union (EU) AI Act, expected to be implemented over the next three years. In the meantime, the United Nations (UN) have proposed an AI advisory body similar to the International Panel on Climate Change (IPCC), and countries from the Global South like Brazil are following Western proposals. In turn, AI companies have been proactive in the regulation debate, aiming at a scenario of improved accountability and reduced liability. In this talk, we will briefly overview efforts and challenges regarding AI regulation and how major AI players are addressing it. The goal of the talk is to stir future project collaborations from a multidisciplinary perspective, to promote a culture where the development and adoption of AI systems is fair, accountable, inclusive, responsible, transparent, ethical, carbon-efficient, and human-centred (FAIRTECH) by design.

Speaker bio: Leonardo Bezerra joined the University of Stirling as a Lecturer in Artificial Intelligence (AI)/Data Science in 2023, after having been a Lecturer in Brazil for the past 7 years. He received his Ph.D. degree from Université Libre de Bruxelles (Belgium) in 2016, having defended a thesis on the automated design of multi-objective evolutionary algorithms. His research experience spans from applied data science projects with public and private institutions to supervising theses on automated and deep machine learning. Recently, his research has concentrated on the social impact of AI applications, integrating the Participatory Harm Auditing Workbenches and Methodologies project funded by Responsible AI UK.

MIP Modelling Made Manageable

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.

Event details

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

Fable-based Learning: Seminar by Prof Jimmy Lee

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.

Event details

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

Seminar: SMT, Planning and Snowmen

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.

Event details

  • When: 6th August 2018 11:00 - 12:00
  • Where: Cole 1.33a
  • Series: AI Seminar Series
  • Format: Seminar

Seminar: AI-augmented algorithms — how I learned to stop worrying and love choice

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!

 

Seminar: Propagation and Reification: SAT and SMT in Prolog (continued)

Jacob Howe, City University, London

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]

Event details

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