Computing Science and Mathematics
Seminars, 2021/2022

Autumn 19 image

Seminars will take place via Microsoft Teams, with a meeting link to be shared via the seminar-announce emails. Unless otherwise stated, from 15.00 to 16.00 on Friday afternoons during semester time, followed by informal discussions.

If you would like to give a seminar to the department in future or if you need more information,  
please contact the seminar organisers, or .

Autumn 2021

Date Speaker Title/Abstract
17 September

24 September
Wen-shin Lee Sparse Interpolation: design sparse antenna arrays for estimating directions of arriving signals

Estimating the directions of simultaneously arriving signals plays an important role in radar, remote sensing, radio frequency interference mitigation, wireless networks, machine perception of unmanned aerial vehicles or self-driving cars. In signal processing, antenna array systems have been designed to solve the problem of estimating the direction of arrival (DOA). A main constraint in designing regularly spaced antenna systems is the spatial Nyquist criterion, which requires the space between two sensors to be less than half of the signal wavelength. A disadvantage of densely spaced antenna elements is the effect of mutual coupling, normally reduced through costly extensive calibration of the system.

Using a regularly spaced antenna system for DOA estimation can be formulated as an exponential analysis problem, which can be tackled by the classical Prony method from approximation theory. Interestingly, the Ben-Or/Tiwari sparse interpolation algorithm in computer algebra is closely related to Prony's method. This connection has led to a major development in exponential analysis that can circumvent the Nyquist constraint, hence allow us to completely remove the dense Nyquist spacing requirement for DOA in antenna design.

This is joint work with Annie Cuyt, Ferre Knaepkens, Dirk I. L. de Villiers.
1 October

8 October

15 October
Vincenzo Crescimanna An information-theoretic introduction to representation learning

Representation Learning (RL)- the process of learning useful descriptions of the visible data - is a machine learning field with the most interest in recent years. Indeed, via RL it is possible to understand and extract the most relevant information from a large dataset or understand how to improve deep neural networks.

The goal of the talk is to give a general introduction to RL, trying to explain its relevance, the challenge in defining an optimal representation, and the way to learn these representations. In particular, following an information-theoretic approach, we will describe a possible optimal definition and a learning principle to get such a representation.
22 October

29 October
No seminar UG Reading week

5 November
No seminar PG Reading week

12 November

19 November
Sarah Thomson Predicting and Improving Vocational Rehabilitation Outcomes

Vocational rehabilitation aims to assist people in re-joining the workforce and maintaining employment. A large amount of data can be collected during this process, and the ADAPT (Automated Dynamic Adaptive Personalised Treatments) consortium seeks to harness that data to help predict and improve outcomes for future patients.

In this work, we have already used machine learning models to predict patient outcomes with success. Our next stage focuses on improving patient outcomes, and we have been prototyping a system for that over the past year. The system is called the Pathway Generator (TPG). TPG aims to personalise and optimise treatment pathways through rehabilitation by learning from historical data and using an evolutionary algorithm to optimise. In this way, we hope that TPG can serve as a decision-assisting tool for professionals planning treatments.

This talk will introduce the domain, elucidate the prototype optimiser system, and present some preliminary results.
26 November
No seminar Internal event

3 December

10 December

Previous Seminar Series

2023:  Spring   Autumn
2022:  Spring   Autumn
2021:  Spring   Autumn
2020:  Spring   Autumn
2019:  Spring   Autumn
2018:  Spring   Autumn
2017:  Spring   Autumn
2016:  Spring   Autumn
2015:  Spring   Autumn
2014:  Spring   Autumn
2013:  Spring   Autumn
2012:  Spring   Autumn
2011:  Spring   Autumn
2010:  Spring   Autumn
2009:  Spring   Autumn
2008:  Spring   Autumn
2007:  Spring   Autumn
2006:  Spring   Autumn
2005:  Spring   Autumn
2004:  Spring   Autumn
2003:  Spring   Autumn
2002:  Spring   Autumn
2001:  Spring   Autumn
2000:  Spring   Autumn
1999:  Spring   Autumn
1998:  Spring   Autumn
1997:  Spring   Autumn
1996:  Autumn

Top image: Image and vision processing.
Courtesy of Dr. Deepayan Bhowmik.

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Division of
Computing Science and Mathematics

Computing Science and Mathematics

Faculty of Natural Sciences
University of Stirling
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Scotland UK

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