SICSA

SICSA DVF Seminar – Dr André G. Pereira

We had our first School seminar of the semester today. The speaker was André G. Pereira visiting Scotland on a SICSA DVF Fellowship. André is working on AI Planning problems, an area that is closely related to the work of our own Constraint Programming research group.

Title: Understanding Neuro-Symbolic Planning

Abstract: In this seminar, we present the area of neuro-symbolic planning, introducing fundamental concepts and applications. We focus on presenting recent research on the problem of learning heuristic functions with machine learning techniques. We discuss the distinctions and particularities between the “model-based” and “model-free” approaches, and the different methods to address the problem. Then, we focus on explaining the behavior of “model-free” approaches. We discuss the generation of the training set, and present sampling algorithms and techniques to improve the quality of the training set. We also discuss how the distribution of samples over the state space of a task, together with the quality of its estimators, are directly related to the quality of the learned heuristic function. Finally, we empirically detail which factors have the greatest impact on the quality of the learned heuristic function.

Biography: Dr. André G. Pereira is a professor at the Federal University of Rio Grande do Sul, Brazil. His research aims to develop and explain the behavior of intelligent systems for sequential decision-making problems. Dr. Pereira has authored several papers on top-tier venues such as IJCAI, AAAI, and ICAPS. These papers contribute towards explaining the behavior of heuristic search algorithms, how to use combinatorial optimization-based reasoning to solve planning tasks, and how to use machine learning techniques to produce heuristic functions. Dr. Pereira is a program committee member of IJCAI and AAAI. His doctoral dissertation was awarded second place in the national Doctoral Dissertation Contest on Computer Science (2017), and first place in the national Doctoral Dissertation Contest on Artificial Intelligence (2018). Dr. Pereira advised three awarded students on national events, including first place and finalist in the Scientific Initiation Work Contest (2018, 2022), and finalist in the Master Dissertation Contest on Artificial Intelligence (2020).

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

Professor Aaron Quigley new SICSA Director

Congratulations to Professor Aaron Quigley who has been appointed as the new Director of SICSA. Aaron, the Chair of Human Computer Interaction co-founded SACHI, the St Andrews Computer Human Interaction research group and served as its director from 2011-2018.

In his volunteer roles he is the ACM SIGCHI Vice President for Conferences (on the ACM SIGCHI Executive Committee), member of the ACM Europe Council Conferences Working Group, a board member of ScotlandIS and an ACM Distinguished Speaker. Aaron will be general co-chair for the ACM CHI conference in Asia in 2021.

For more information about Professor Quigley, please see https://aaronquigley.org.

St Andrews Research Open-day in Computer Science

Register for St Andrews ROCS HERE for free.

St Andrews ROCS is an event for those of you who engage (or are planning to engage) with research in the School of Computer Science at the University of St Andrews.

The main audiences are prospective postgraduate students, prospective or current industrial collaborators, and colleagues from other disciplines or Schools in Scotland and beyond.

The event will take place Friday October 26th 2018, between 10:00 AM and 4 PM.

There will be talks from all research groups, posters, demonstrations, guided tours, and much more.

You can learn about how to become a St Andrews PhD student or an active industrial collaborator.

The event will take place in the JACK COLE BUILDING, NORTH HAUGH, UNIVERSITY OF ST ANDREWS, ST ANDREWS, KY16 9SX, SCOTLAND.

You can download the programme of activities.

If you have any questions, e-mail dopgr-cs@st-andrews.ac.uk.

Register for St Andrews ROCS HERE for free.

Event details

  • When: 26th October 2018 10:00 - 16:00
  • Where: School of Computer Science
  • Format: Conference, Symposium, Visiting Day

SICSA Workshop on Learning Analytics in Education

The Higher Education Research Group is happy to announce the SICSA-sponsored workshop on learning analytics on Aug 6th 2018.

Goals

The purpose of this SICSA-sponsored workshop is to encourage an evidence-based approach to teaching by leveraging quantitative and qualitative data available to CS schools. Most importantly, we plan to organise a multi-institution study on using machine learning and AI-based techniques on existing data to improve learning outcomes across multiple universities. The workshop will serve to formulate the goals of such a study and forge the necessary collaborations to make this happen.

Format

We are very happy to announce that the chief regulatory adviser at Jisc Technologies Andrew Cormack will give an invited talk about the legal and ethical framework for learning analytics. In addition to the invited talk, the workshop will consist of a set of breakout sessions and a final discussion dedicated to preparing a follow-up study. The breakout sessions will involve discussions about existing quantitative and qualitative data available to educators, how these data influence teaching, what (statistical and other) data procesisng is useful for driving decisions, and which algorithmic approaches could be applied across institutions.

Background

Evidence-based teaching is of particular importance in fast-moving fields like Computer Science, and is therefore of interest to many higher education institutions. We have more data on students and courses than ever before including grades, entry requirements, qualitative and quantitative feedback, and career paths after leaving the university, and as computer scientists we are well equipped to process such data. It is important to measure the positive and negative impact of changes to the delivery (e.g. lecture capture, different lecturers) and content (slides, supporting material, organisation) in order to maintain and hopefully improve learning outcomes over time.

However, measuring how teaching approaches affect learning outcomes can be challenging because of issues such as data protection, small numbers of students, changes in the curriculum, or changes in admission procedures. Measuring differences between institutions is even harder because of differences in course structure, class sizes and marking scales. We believe that computer science techniques such as data mining, machine learning and artificial intelligence will become increasingly important in this field, and would like to set up an ambitious study across several universities based on the findings of this workshop. Such a study is only possible if coordinated well across institutions and this workshop aims to provide the basis for such collaboration.

Target Audience

The workshop will involve 24 academics, mainly from SICSA-affiliated institutions, aiming to foster an exchange of ideas and best practice. While the central topic is CS education, we hope to also appeal to CS academics engaged in data ethics, machine learning, and artificial intelligence (e.g. for processing data in natural text form) because the topic provides an important application of CS, and has great potential for impact.

To register, contact Kasim at kt54@st-andrews.ac.uk, or go to the Eventbrite page:

http://learning-analytics-workshop.eventbrite.com/

Event details

  • When: 6th August 2018 09:30 - 15:30
  • Where: Gateway Bldg
  • Format: Workshop

SCONE (SCOttish Networking Event)

The 18th SCONE (SCOttish Networking Event) meeting will be held in St Andrews on 26th April. These are informal gatherings of networks and systems researchers and have taken place in a number of Scottish institutions since 2008. The meeting will comprise a small number of talks, including one invited speaker (Mirco Musolesi from UCL), followed by various networking activities for PhD students. We will then retire to the pub to continue our conversations. More details can be found at http://scone.cs.st-andrews.ac.uk/wiki/Meeting26042017. Attendance is free; if you are interested in coming then please contact Tristan.

We are thankful to the SICSA Networking and Systems theme for their support.

Event details

  • When: 26th April 2017 12:00 - 18:00
  • Where: Cole 1.33
  • Format: Workshop

Distinguished Lecture: ‘Scalability and Fault-tolerance, are they the same?’ by Joe Armstrong

The first of this academic year’s distinguished lectures will be given by Professor Joe Armstrong, co-inventor of Erlang, on Monday 16th November 2015 at The Byre Theatre.Joe Armstrong

Abstract:

To build a scalable system the important thing is to make small isolated independent units. To scale up we just add more units. To build a fault-tolerant system the important thing to do is make small isolated independent units…. Does that sound familiar? Haven’t I seen that somewhere before? Oh yes, in the first paragraph! So maybe scalability and fault tolerance are really different names for the same thing.

This property of systems, namely that fault-tolerant systems were also scalable, was noticed years ago, notably in the design of the Tandem computer system. The Tandem was design for fault tolerance but rapidly became a leading supplier of scalable computer platforms. Thus it was with Erlang.

Erlang followed  a lot of the Tandem design, it was built for fault-tolerance but some of the most successful applications  (such as WhatsApp) use it for its scalability.

In this lecture I’ll talk about the intimate relationship between scalability and fault-tolerance and why they are architecturally the same thing.

I’ll talk about the design of Erlang and why scalable systems have to be built on non-shared memory abstractions.

Bio:

Joe Armstrong has been programming since 1967. He invented the programming language Erlang. He has worked as a programmer, founded a few successful companies and written a few books. He has a PHD in Computer Science from KTH. He is currently Adjunct Professor of Computer Science at the KTH Royal Institute of Technology in Stockholm.

Event details

  • When: 16th November 2015 09:15 - 15:30
  • Where: Byre Theatre
  • Series: Distinguished Lectures Series
  • Format: Distinguished lecture