Abstract: 

With greater machine learning and big data software from the open source community, it is now easier than ever to build powerful predictive models to help people making decisions. On the other hand, even if one can predict the outcome accurately, it is not always trivial to make real life decisions, especially when there are a large number of choices and trade-off between multiple objectives. Bio-inspired optimization algorithms can be a great combination with the predictive modelling techniques under such scenario, allowing efficient exploration of the multi-dimensional choice space to find out the optimal frontier of the key objectives. Decision makers can then focus on these objectives rather than worrying about the choices at the execution level.

In this session we are going to walk through an example of multi-objective optimization problem in the context of a promotion campaign, using the open source package PyGMO (the Python Parallel Global Multiobjective Optimizer) from ESA. We will first briefly touch upon how to build a propensity model for such marketing activities. Then we will see how to optimize our promotion strategy with PyGMO, based on the prediction of the propensity model. We will also go a bit into the details of various algorithms available in PyGMO, as well as how to handle constraints.

Bio: 

Dr Jiahang Zhong is the leader of the data science team at Zopa, one of UK’s earliest fintech company. He has broad experience of data science projects in credit risk, operational optimization and marketing, with keen interests in machine learning, optimization algorithms and big data technologies. Prior to Zopa, he worked as a PhD and Postdoctoral researcher on the Large Hadron Collider Project at CERN, with focus on data analysis, statistics and distributed computing.