Seminar

SACHI Seminar: Rights-driven Development

Abstract:

Alex will discuss a critique of modern software engineering and outline how it systematically produces systems that have negative social consequences. To help counter this trend, he offers the notion of rights-driven development, which puts the concept of a right at the heart of software engineering practices. Alex’s first step to develop rights-driven practices is to introduce a language for rights in software engineering. He provides an overview of the elements such a language must contain and outlines some ideas for developing a domain-specific language that can be integrated with modern software engineering approaches. 

Bio:

Alex Voss, who’s an Honorary Lecturer here at the school and an external member of our group. Alex was also a Technology Fellow at the Carr Center for Human Rights Policy at Harvard’s John F. Kennedy School of Government and an Associate in the Department of Philosophy at Harvard.

Alex holds a PhD in Informatics and works at the intersection of the social sciences and computer science. His current research aims to develop new representations, practices and tools for rights-respecting software engineering. He is also working on the role that theories of causation have in making sense of complex socio-technical systems.

His research interests include: causality in computing, specifically in big data and machine learning applications; human-centric co-realization of technologies; responsible innovation; computing and society; computer-based and computer-aided research methods.

More about Alex: https://research-portal.st-andrews.ac.uk/en/persons/alexander-voss

Event details:

  • When: 28th February 2024 12:30 – 13:30
  • Where: Jack Cole 1.19

If you’re interested in attending any of the seminars in room 1.19, please email the SACHI seminar coordinator: aaa8@st-andrews.ac.uk so they can make appropriate arrangements for the seminar based on the number of attendees.

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).

Seminar Talk from a SICSA visitor (Daniel Garijo) Friday 10 June, 11.00am

Accelerating Research Software Understandability Through Knowledge Capture

Daniel Garijo

Summary: Research Software is key to understand, reproduce and reuse existing work in many disciplines, ranging from Geosciences to Astronomy or Artificial Intelligence. However, research software is usually difficult to find, reuse, compare and understand due to its disconnected documentation (dispersed in manuals, readme files, web sites, and code comments) and a lack of structured metadata to describe it. These problems affect not only researchers, but also students who aim to compare published findings and policy makers seeking clarity on a scientific result. In this talk I will present the main research challenges and our recent efforts towards facilitating software understanding by automatically capturing Knowledge Graphs from software documentation and code.

Short bio: Dr. Daniel Garijo Verdejo is a Distinguished Researcher at the Ontology Engineering Group of Universidad Politécnica de Madrid (UPM). Previously, he held a Research Computer Scientist position at the Information Sciences Institute of the University of Southern California, in Los Angeles. Daniel’s research activities focus on e-Science and Knowledge Capture, specifically on how to increase the understandability of research software and scientific workflows by creating Knowledge Graph from their documentation and provenance (i.e., steps, outputs, inputs, intermediate results).

For this talk we will use a hybrid approach: In person (Jack Cole, 1.33) and online, via Teams.

If you wish to attend it would be helpful if you could register on eventbrite to let us know if you intend to attend in person or online

All Welcome!

Seminar – Phong Le, Amazon – 3rd March 2021

Can Language Models be Weak Annotators

We are happy to have Phong Le, from Amazon, talk on Teams on Wed 3 March at 12 noon on Teams.

Abstract

Deep language models e.g. BERT and GPT3 are the breakthrough in Natural Language Processing in the last 3 years. Being trained on massive raw text data, they capture useful priors for several tasks such as syntactic parsing, information extraction, and question answering. Moreover, they are capable of answering factual and commonsense cloze questions such as “Dante was born in _____”. In this talk, I will firstly give an overview about what language models “know”. I will then present our work on exploiting their knowledge as weak supervision for a specific task called relation classification.

Relation classification, the identification of a particular relation type between two entities in text, requires annotated data. Data annotation is either a manual process for supervised learning, or automated, using knowledge bases for distant learning. However, both methodologies are costly and time-consuming since they depend on intensive human labour for annotation or for knowledge base creation. Using language models as annotators, on the contrary, is very cheap but the annotation quality is low. We hence propose NoelA, an auto-encoder using a noisy channel, to improve the accuracy by learning from the low quality annotated data. NoelA outperforms BERT and a bootstrapping baseline on TACRED and reWIKI datasets.

Bio: I’m an applied scientist at Amazon Alexa. Before that, I was a tenure-track research fellow at the University of Manchester. I did a postdoc with Ivan Titov at the University of Edinburgh, and got a PhD from the University of Amsterdam under the supervision of (Jelle) Willem Zuidema. I’m interested in neural networks and deep learning. My current work is to employ them to solve natural language processing tasks such as entity linking, coreference resolution, and dependency parsing. I’m also interested in formal semantics, especially learning semantic parsing.

For more details, please visit my homepage https://sites.google.com/site/lephongxyz/

 

Please note the session will not be recorded, to preserve the like-for-like nature of physical seminars and also avoid any privacy/rights issues.

Event details

  • When: 3rd March 2021 12:00 - 3rd February 2021 13:00
  • Format: Seminar

Georgios Gerasimou (University of St Andrews): Frontiers in computational revealed preference analysis

RESCHEDULED: please note the changed date and a non-standard time!

Abstract: Prest is a recently published piece of open-source software for computational revealed preference analysis that provides novel ways to estimate decision makers’ preferences over choice alternatives by analysing their observable choice behaviour. This software is informed by classic as well as recent developments in economic revealed preference theory. Some of the recent developments take the form of models that are computationally complex. This complexity currently hinders the inclusion of these models in the Prest toolkit. The presentation will first aim to describe the primary ideas underpinning Prest and illustrate them with examples from its existing toolkit. It will then proceed with a discussion of some of the challenges pertaining to the expansion of that toolkit with more models and operations. The presentation will be self-contained and no prior background in economics will be necessary.

Speaker Bio: Georgios is a Reader in Economics at the University of St Andrews, working mainly on decision theory and revealed preference analysis. In the latter research programme, Georgios’ work aims to improve our understanding of people’s decision processes and preferences through theoretical, experimental/empirical as well as computational methods. Georgios co-developed the Prest software program for computational revealed preference analysis (https://prestsoftware.com/).

Event details

  • When: 17th February 2020 14:00 - 15:00
  • Where: Cole 1.33b
  • Series: School Seminar Series
  • Format: Seminar

Philippe Palanque (University of Toulouse): Harnessing Usability, UX and Dependability for Interactions in Safety Critical Contexts

Abstract: Innovation and creativity are the research drivers of the Human-Computer Interaction (HCI) community which is currently investing a vast amount of resources in the design and evaluation of “new” user interfaces and interaction techniques, leaving the correct functioning of these interfaces at the discretion of the helpless developers. In the area of formal methods and dependable systems the emphasis is usually put on the correct functioning of the system leaving its usability to secondary-level concerns (if at all addressed). However, designing interactive systems requires blending knowledge from these domains in order to provide operators with enjoyable, usable and dependable systems. The talk will present possible research directions and their benefits for combining several complementary approaches to engineer interactive critical systems. Due to their specificities, addressing this problem requires the definition of methods, notations, processes and tools to go from early informal requirements to deployed and maintained operational interactive systems. The presentation will highlight the benefits of (and the need for) an integrated framework for the iterative design of operators’ procedures and tasks, training material and the interactive system itself. The emphasis will be on interaction techniques specification and validation as their design is usually the main concern of HCI conferences. A specific focus will be on automation that is widely integrated in interactive systems both at interaction techniques level and at application level. Examples will be taken from interactive cockpits on large civil commercial aircrafts (such as the A380), satellite ground segment application and Air Traffic Control workstations.

Speaker Bio: Dr. Philippe Palanque is Professor in Computer Science at the University Toulouse 3 “Paul Sabatier” and is head of the Interactive Critical Systems group at the Institut de Recherche en Informatique de Toulouse (IRIT) in France. Since the late 80s he has been working on the development and application of formal description techniques for interactive system. He has worked for more than 10 years on research projects to improve interactive Ground Segment Systems at the Centre National d’Etudes Spatiales (CNES) and is also involved in the development of software architectures and user interface modeling for interactive cockpits in large civil aircraft (funded by Airbus). He was involved in the research network HALA! (Higher Automation Levels in Aviation) funded by SESAR programme which targets at building the future European air traffic management system. The main driver of Philippe’s research over the last 20 years has been to address in an even way Usability, Safety and Dependability in order to build trustable safety critical interactive systems. He is the secretary of the IFIP Working group 13.5 on Resilience, Reliability, Safety and Human Error in System Development, was steering committee chair of the CHI conference series at ACM SIGCHI and chair of the IFIP Technical Committee 13 on Human-Computer Interaction.

 

Event details

  • When: 3rd February 2020 11:00 - 12:00
  • Where: Cole 1.33a
  • Series: SACHI Seminar Series, School Seminar Series
  • Format: Seminar

Blindness seminar

The medical school is holding a Seminar on Wednesday 8th January @1400-1530
New tools and methods to prevent blindness.
Seminar room 1, Medical and Biological Sciences Building

  • Dr. Andrew Blaikie, St Andrews
    Arclight Project
  • Dr. Craig Robertson, CEO Epipole Ltd
    Hand held fundus cameras
  • Prof Congdon, Queen’s University Belfast
    Overview of Global Ophthalmology

Event details

  • When: 8th January 2020 14:00 - 15:30
  • Format: Seminar

Nguyen Dang (University of St Andrews): Hyper-Parameter Tuning for an evolutionary algorithm

Abstract: In this talk, I will present a case study to illustrate how automated algorithm configuration can be used to gain insights into theoretical results on an evolutionary algorithm, namely the (1+(λ,λ)) Genetic Algorithm. This work is a collaboration with Carola Doerr.

The (1+(λ,λ)) Genetic Algorithm is an evolutionary algorithm that has interesting theoretical properties. It is the first algorithm where the benefit of crossover operator is rigorously proved. It is also the first example where self-adjusting parameter choice is proved to outperform any static parameter choice. However, it is not very well understood how the hyper-parameter settings influences the overall performance of the algorithm. Analyzing such multi-dimensional dependencies precisely is at the edge of what running time analysis can offer. In this work, we make a step forward on this question by presenting an in-depth study of the algorithm’s hyper-parameters using techniques in automated algorithm configuration.

Speaker bio: Dr Nguyen Dang is a post-doc in the Constraint Programming group at the University of St Andrews. Her main research focus is on automated algorithm configuration, algorithm selection and their applications in various contexts. These techniques make use of statistical methods and machine learning for fine-tuning of algorithm parameters, assessing parameters’ importance and building algorithm portfolios. Another line of her research is about solving combinatorial optimisation problems using metaheuristic algorithms.

Event details

  • When: 11th February 2020 14:00 - 15:00
  • Where: Cole 1.33b
  • Series: School Seminar Series
  • Format: Seminar