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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 .

Spring 2022

Date Speaker Title/Abstract
Friday
28 January


Friday
4 February


Friday
11 February


Friday
18 February


Friday
25 February
Dr Ken Reid, Department of Animal Science, Michigan State University Genomic Prediction using Evolutionary Computation, Machine Learning and Traditional Statistical Approaches

The completion of the human genome project was a major milestone for humanity. However, the focus then quickly shifted to the more complex problem of understanding the new data we had access to. Traditionally, selection of animals for breeding specific traits (such as disease resistance or milk yield) was through careful observation of phenotypes. While this traditional approach has been successful, discerning which parts of DNA are causative for specific phenotypic traits has proven to be more effective. While some traits can be easily mapped to specific DNA segments, other traits have proven impossible to map thus far. In this seminar, we will explore these problems and consider various possible solutions for mapping causative quantitative trait loci to phenotypes in various species, while making use of modern hardware and methodologies.
Friday
4 March
No seminar - reading week

Friday
11 March


Wednesday
16 March (3-4pm)
Dr Sandy Brownlee, CSM, University of Stirling Modelling and Optimisation of Aircraft Ground Movement for Greener, Faster Airport Operations

Aircraft are designed to operate efficiently in the air, but a necessary part of any flight is the taxiing of aircraft between terminal and runway at either end of the journey. The allocation of routes to aircraft for their travel along taxiways is known as the Ground Movement problem, and it has a strong influence on the capacity of an airport and environmental impact, with taxiing accounting for a disproportionate level of emissions. The problem is particularly challenging. It has multiple conflicting objectives to optimise (such as minimising taxi time and fuel consumption). It also has considerable uncertainty, which arises from the complex operations of an airport. In this talk I will cover research carried out under the EPSRC funded "TRANSIT" project. I will cover the application of machine learning to accurately predict taxi times for aircraft, including identification of important features that drive the model, and a hybrid optimisation approach, combining evolutionary algorithms and exact short path algorithms, to generate efficient routes.

Note - this talk is being hosted by Michigan State University, so will be on a zoom link, to be circulated by email
Friday
25 March


Friday
1 April
Dr David Li Department of Biomedical Engineering, University of Strathclyde Advanced time-resolved imaging and spectroscopy techniques for life science

Super-resolution time-resolved imaging and spectroscopy techniques can reveal biological processes at the molecular level. They can unravel mechanisms underlying diseases and facilitate drug development. However, we also enter the low light regime (only a few photons are acquired), and new acquisition and analysis approaches are sought after.
Speaker Bio: David Li is a senior lecturer in optoelectronics at the Department of Biomedical Engineering, University of Strathclyde. He received his PhD in electrical engineering from National Taiwan University. He then joined the Industrial Technology Research Institute (ITRI), Taiwan, as an R&D engineer working on 1.25-12.5Gbps optical communication chipsets and wireless communication IP, knowledge transfer, and international joint projects.
From 2007 to 2011, he worked at the Institute for Integrated Micro and Nano Systems, University of Edinburgh, on the European projects "MEGAFRAME" and "METOXIA" to develop CMOS time-resolved imaging systems for biomedical applications.
From January 2014, he joined Strathclyde Institute of Pharmacy & Biological Science, after his first lecturing position in biomedical engineering and embedded systems at the School of Engineering and Informatics, the University of Sussex.
He is currently leading a BBSRC project to establish a SuPer-Resolution (< 50nm) time-resolved (< 5ps) ImagiNg and specTroscopy (SPRINT) system and is looking for new applications and collaborators to use SPRINT.
Friday
8 April


Friday
15 April


Friday
22 April


Friday
29 April


Wednesday
11 May (3-4pm)
Clifford Bohm, Post-Doctoral Student at Michigan State University Understanding the Evolution of Cognition with Structural and Information-Theory Based Analysis

In this talk, I will present work on developing methods to better understand the structure of evolved digital cognitive architectures such as Markov Brain, RNN, and CGP. First, I will present Fragmentation, an information analysis that can be used to identify how data flows through cognitive architectures. Then I will discuss the Comparative Hybrid Approach, an empirical technique that can shed light on how the components of a given architecture may account for differences in performance between architectures.
Wednesday
18 May (3-4pm)
Prof. Gabriela Ochoa Neuroevolution Trajectories and Landscapes

Neuroevolution, the use of evolutionary algorithms to design neural networks, has a long tradition in evolutionary computation with roots in the late 1980s and early 1990s. Most neuroevolution systems optimise both the neural network topology and its weights. However, when scaling up to contemporary deep models with millions of weights for supervised learning tasks, gradient-based weight optimisation outperforms evolutionary methods. In consequence, many recent neuroevolution systems use gradient-based weight optimisation and only evolve the topology. This talk overviews our recent work on modelling neuroevolution systems with search trajectory networks (STNs) and local optima networks (LONs) with the aim of providing a visual and quantitative understanding of search and optimisation in this domain.

Previous Seminar Series

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

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University of Stirling
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Scotland UK

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