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Quantitative Modelling in Commodity Markets

23-25 September 2019
London Marriott Hotel Canary Wharf, United Kingdom

What are the keys ways in which AI technologies such as machine learning and deep learning are disrupting the commodities industry?

Shell has been an early adopter of AI, as it attempts to speed up its digital transformation. From machine learning to computer vision, deep learning to virtual assistants and autonomous vehicles to robotics, Shell has been focused on a range of technologies that have supported advances in AI. Shell expects to realise substantial economic value by rapidly scaling and replicating AI and machine learning applications across its upstream and downstream businesses and improving operational performance.

What were some of the key challenges and lessons learnt from building the algorithm for Apache Spark?

Although not without its hurdles, the process of scaling and distributing large computational tasks to an Apache Spark cluster is, perhaps, surprisingly straightforward. However, therein in lies the danger. The temptation is to scale up to Apache Spark in the quickest way possible, without too much thought, which can lead to sub-optimal performance. By slightly modifying the architectural solution and better understanding the overhead in distributing the problem order of magnitude gains in computational efficiency can be achieved.

Outside of AI and machine learning, what are the key disruptors in the commodities industry at the moment?

Driven by the major forces of the energy transition and digitalisation, we'll likely see the re-making, over time, of the energy system. We aim to meet the energy needs of society in ways that are economically, socially and environmentally viable. Digitalisation will play a significant role in ensuring Shell is a world class investment opportunity and is fit for the future. Shell is on a digital transformation journey, driving digital culture and technology across our businesses.

What would you like to achieve by attending the marcus evans Quantitative Modelling in Commodity Markets Conference?

My intention in attending the conference is to learn from, and share my own insights with, other participants. I’m particularly looking forward to hearing from other organisations on the approaches they have taken to overcome common technical challenges.

An interview with:

Wayne Jones, Data Science Lead, Shell

Trading houses and financial institutions are searching for a competitive edge to stay relevant in the business, frequently that edge is being found in AI techniques, either replacing or being used in combination with the traditional methods of modelling. The key now is to ensure you understand where, how, and which AI techniques will improve your risk management and forecasting models in commodity markets, account for intermittency of renewables and improve your volatility and correlation models.

This marcus evans event will assess how commodity trading firms are utilising AI, ML and data science to enhance risk forecasting and modeling of their commodities in order to gain a competitive edge.

To view the Conference Agenda, click HERE! 

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We would be delighted to provide you with more information on the conference agenda.  Please fill in your details below and we will be in touch.

Wayne Jones is a data science lead in the Shell data science centre of excellence. He has a BSc (Hons) degree in Mathematics from the Bangor University of Wales, a MSc in ‘Mathematical Modelling for Industry’ from the University of Loughborough and a PhD in Ecological Modelling from the University of Strathclyde. Jones became a chartered statistician in 2008, and during his 12 years in Shell has worked on a wide variety of projects, including systematic trading strategies, inventory optimisation, renewables production forecasting and the analysis of environmental monitoring data.

To view the Conference Agenda, click HERE! 

For all enquiries regarding speaking, sponsoring and attending this conference contact:

Yiota Andreou
Email: Yiotaa@marcusevanscy.com
Telephone: +357 22849 404
Fax: +357 22 849 394