Erica Yang

Doctor Erica Yang

ericayang1Dr Erica Yang is Head of Visualisation in the Scientific Computing Department at the Science and Technology Facilities Council (STFC) in the UK, and also an invited independent expert appointed by the European Commission for the H2020 programme. She has background in distributed system engineering, data science, and visualisation. Following a PhD in computer science from University of Durham, a post-doctoral research fellowship at University of Leeds, she joined STFC Rutherford Appleton Laboratory in 2007 as a senior scientist. She was prompted to the head of visualisation position in 2016 to bring together two major strands of work undertaken by the Department: high throughput complex data analysis and visualisation. She has delivered high profile computational projects with UK’s national science facilities and worked extensively with large laboratories in Europe and the US, in addressing data and compute intensive problems in big science experiments. She also has extensive collaboration links with civil engineering, automotive, and transportation industries, particularly in developing scalable visual analytics systems to tackle increasingly prevalent complex data analysis, visualisation, and exploration challenges in these domains. This talk will give an overview of the group’s current projects with particular highlights of the role visualisation plays in a variety of decision making processes.


Talk : Visualisation that underpins a new wave of visual computing technologies for big data analytics: methods and applications

The rapid rise of big data is transforming many aspects of our society, from science and engineering in world class laboratories to many sectors which do not conventionally have substantial presence of computing technologies, e.g. construction engineering and agriculture. The volume, velocity, variety, and veracity (the 4Vs) and perhaps, more importantly, the complexity of data and the diversity of domains that data come from have meant that humans increasingly depend on the effectiveness of visualisation methods and systems. The main thrust of this talk is to argue that visualisation, whilst it can be a subjective matter, has become a critical tool for decision makers to make sense of big data & analytics methods. Applications range from high throughput image analysis that takes place in large laboratories to streaming analytics that look at the motions of social media and increasingly Cyber Physical Systems (CPS) and Internet of Things (IoTs). This talk will examine closely the uncertainty of visualisation methods through comparing and contrasting the differences among several popular visualisation frameworks and tools, e.g. D3 for semantic text analytics, to ParaView for large scale volume rendering and in-situ visualisation for computational flow simulation. Finally, this talk covers the increasingly popular array of high end computing methods and parallel computational infrastructure that underpin the new wave of visual analytics capabilities which are essential for big data systems to deliver high throughput analytical insights from computational modelling, simulation and prediction.