From early development to the recent advancements BCS Aberdeen branch.
Speaker
Eyad Elyan is a Professor in Machine Learning and Computer Vision at the School of Computing at RGU
Agenda
This event is a person to person event only- no webinar
17:30pm-18:15pm for networking and pizza
18:15pm-19:15pm talk and
19:15pm-19:30pm for Q&A
Synopsis
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.
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.
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This event is brought to you by: BCS Aberdeen branch
