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´╗┐Computer Vision and AI, Challenges and Opportunities: From early development to the recent advancements

Date Wednesday 04 May 2022
Time 17:30 - for a 18:15 start
Free admission, pizza and refreshments provided
Speaker

Prof. Eyad Elyan

About the speaker

Eyad Elyan is a Professor in Machine Learning and Computer Vision at the School of Computing at RGU. He is leading the Interactive Machine Vision research group and his primary research is in machine learning, deep learning, and applied computer vision. His work in the area of ensemble-based learning and learning from unstructured and imbalanced datasets have been successfully applied to various real-world applications across different domains, such as processing and analysing engineering diagrams, remote inspection for oil and gas installations and platforms, biometric applications, medical datasets analysis, and others. Over the past decade, he has attracted funds to support his research from different public funding bodies including Innovate UK, the Data Lab Innovation Centre, Oil and Gas Innovation Centre (OGIC), Net Zero Technology Centre, Historic Environment Scotland, and Others. He led and is currently leading several innovative collaborative projects with leading industry partners including PB, Chevron, DNV GL, Mintra Group, and others.

Eyad is a Fellow member of the British Higher Education Academy and serves as the Scotland Data Lab Innovation Centre Ambassador. He plays a lead organising role in various national and prestigious international events, reviewing for many leading international journals in the area of artificial intelligence, computer vision, and machine learning, and a reviewer for research councils such as EPSRC. He has given numerous talks in the area of Machine Learning, AI, and Big Data Analytics, supervised 9 PhD students to successful completion, and examined 15 PhD students across various UK academic institutes.

Location ONE CodeBase, ONE Tech Hub, Schoolhill, Aberdeen AB10 1JQ
Abstract

Advances in Deep Learning and Deep Convolutional Neural networks since 2012 have resulted in significant progress in the computers' ability to understand, recognise and analyse surrounding scenes. Deep Learning methods are becoming increasingly popular with the availability of large amounts of data and powerful GPU machines capable of running large-scale and complex experiments using millions of images and videos. This unprecedented progress has resulted in various applications demonstrating the computer's ability to analyse and predict humans actions, recognise faces, detect and track humans movements and subtle facial expressions and emotions, analyse crowd behaviours to detect anomalies, and much more.

The talk will focus on this very exciting area of AI and Machine Learning. First, an overview of computer vision, and methods for solving key challenging vision tasks will be gently introduced. This will be followed by a discussion of some of the most exciting aspects in this area, including Deep Learning, Deep Convolutional Neural Networks, and Deep Generative Adversarial Neural Networks, with a discussion of various interesting and recent examples of how these methods have been used to solve complex problems. Key challenging research problems will be then briefly presented, especially in the domain of computer vision for medical applications, robotics surgery, fake content generation and context understanding.

Real-world examples of some of the recent work that is taking place at the School of Computing-Robert Gordon University with leading industrial partners will then be presented. This will cover our work in the area automated remote inspections of Oil and Gas installations, biometric applications for authenticating remote users, reading and automatic analysis of complex engineering documents. Finally, resources and a roadmap on how to get into this exciting area of research will be outlined and discussed, including opportunities for collaboration with the school of computing.

Registration

Register for the event using EventBrite: https://4may22aberdeenbranch.eventbrite.co.uk/

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Visit to Ferryhill Railway Heritage Centre

Date Tuesday 22 Mar 2022
Time 18:00 - Members Event
Speaker N/A
Location Ferryhill Railway Heritage Centre

Directions

Please park in the Duthie Park (Riverside Drive) car park if arriving by car. The Centre is located next to Duthie Park, towards the Polmuir Road gate, the pedestrian entrance is accessed by a dedicated footpath (total distance 250m approximately) from the Community Gardens. Head up the hill turning right just after the play park, towards the Community Gardens, the footpath runs above the Play Park. Alternatively limited car parking is available on site for less able members by using the entrance in Polmuir Avenue. The post code is AB11 7WH.

Abstract

A visit has been arranged for BCS Aberdeen Members on Tuesday 29th March. Members will have the chance to see the ongoing restoration of the original Ferryhill Engine Shed, the completed Turntable and see the developing collection of historic locomotives, cranes, railway carriages and wagons. The Ferryhill Railway Heritage Trust took over the site from Aberdeen City Council and Network Rail in stages from 2007. Formerly Motive Power Depot 61B, the area has been in constant railway use since 1850. The Turntable, the Engine Shed and other adjacent buildings are listed, but were all at one time in danger of being demolished. The site is entirely operated by volunteers who come from all walks of life.

Booking is essential and will be by Eventbrite, as numbers will be limited. Refreshments will be provided.

Registration

Register for the event using Eventbrite: https://29march22aberdeen.eventbrite.co.uk/


Fish & Chips: Moving to electronic measurements in fisheries science

Date Tuesday 22 Feb 2022
Time 18:45 - Free admission
Speaker

Jens Rasmussen, Data Manager at Marine Scotland (Scottish Government)

Location Online - Zoom
Abstract

Marine Scotland collects data on the abundance of fish species in the waters around Scotland to be able to assess how much can be sustainably caught while ensuring the fish stocks remain healthy. Over the last decade, Marine Scotland has transformed these kinds of operations from a largely manual "pen and paper" approach to a digital approach whereby measurements are recorded electronically, and data flows quicker and easier. In this talk, Jens will share the journey of changing a workflow, both in the practical terms of installations and software to the broader concepts of collaborations between many different roles in an organisation, and the aim for continuous improvement.

Registration

Register for the event using EventBrite: https://22feb22aberdeen.eventbrite.co.uk/

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Computer Science career in the NHS: The Scientist Training Programme (STP)

Date Tuesday 25 Jan 2022
Time 18:30 - Free admission
Speaker

Andrew Simpson

About the speaker

Andrew studied BSc & MSc Computer Science, worked in IT industry for a while and he is now training as a clinical scientist in physiological measurement & clinical computing, under Route II, in the James Cook University Hospital's Medical Physics Department.

Location Online - Zoom
Abstract

Andrew Simpson will join us on Zoom to talk about the Scientist Training Programme in the NHS. The STP is a three year fully NHS funded training programme, leading to an MSc Clinical Science (Clinical Computing) and on successful completion, Statutory Registration with the Health and Care Professions Council (HCPC) as a Clinical Scientist. During training, the student is paid at NHS Band 6 (at a training Annex). The entry requirements are: An undergraduate degree in either Computer Science, Mathematics or Scientific Engineering.

Computer Scientists who are Clinical Scientists can and do:

  • Develop medical software and technology
  • Use that software/technology clinically on patients (in theatres, on wards, etc.) (Under Annex A of the Medical Device Regulations)
  • Understand and be involved in the research of the science behind the diagnostic test
  • Advise doctors on the results and on the technology available for patients.
All as a state registered healthcare professional in a truly unique, in demand and rewarding role.

Higher Specialist Scientific Programme (HSST)

As your career progresses, Clinical Scientists are eligible to further train as Consultant Clinical Scientists in Clinical and Scientific Computing, to demonstrate significant expertise at the intersection of Computer Science and Healthcare.

STP Equivalence

This is portfolio route by the Academy of Healthcare Sciences, where the pre-registrant demonstrates equivalency to the Scientist Training Programme outcomes. A master's degree is not required although the pre-registrant must demonstrate working to master's level.

Route II

This route allows a work-based competency learning route for those who have a masters and relevant experience. The pre-registrant works towards and compiles a portfolio of evidence, demonstrating competency in Clinical Computing to the level of Clinical Scientist.

Registration

Join the event via Zoom https://us02web.zoom.us/j/85962639769?pwd=WjN1VzA4WVdxNVVtVEZSR3QzUjZNUT09


BCS Aberdeen AGM 2021 followed by a talk on "Deep Learning Techniques for Data Sharing"

Date Tuesday 16 Nov 2021
Time 18:30 - Free admission
Speaker

Georgios Leontidis

About the speaker

Georgios Leontidis is an Associate Professor in Machine Learning and the director of the Interdisciplinary Centre for Data and AI at the University of Aberdeen.

Location Online - Zoom
Abstract

ANNUAL GENERAL MEETING

Our AGM is an important opportunity to hear about how the branch performed last year, and to join the Committee. New members, particularly Young Professionals are very welcome.

About the talk

In this exciting talk, Georgios will discuss how emerging deep learning methodologies and privacy preserving technologies can support decision making and enable data sharing. Currently, data sharing is a major bottleneck across several sectors, including energy and agri-food. Georgios will show how federated learning and fully homomorphic technologies might be used to support data sharing and also decision making. In addition, Georgios will discuss how advanced deep learning technologies, such as self-supervised learning can be used to detect anomalies in nuclear reactors.

Registration

Join the event via Zoom https://us06web.zoom.us/j/88908313252?pwd=L3g3RStMd2FjVnl2WXFCZ01mcVhOUT0

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