Morgan Stanley

Contact: Oli Bage <Oli.Bage@morganstanley.com>

In 2019:

Probable Causes

Theo Mauger (CCed) has agreed to help out on the project. "Mauger, Theo" <Theo.Mauger@morganstanley.com>

Would it be too vague to ask for students to work out how probabilistic computing can help in identify the most promising datasets (& potential correlations) in large public data catalogs that are most likely to help low-income or vulnerable communities?

We have a few charity partners we can reach out to before the start of the project to provide insight into which kinds of data are most useful to them, such as Princes Trust, the NSPCC and Community Links.

We might be assuming too much from the charities to have data scientists ready to go once the datasets are identified, but maybe an 'extra credit' can be to build a simple mobile app that they or the communities they support can use to access the data, or even use it to solve a common problem.

Idea for 2017:

The Deep Learning Society

Technology companies invest billions in self-driving cars and self-playing computer games, but surprisingly little in real social problems. Your task is to use the latest deep learning technologies to create an intelligent social work assistant that can recognise and act in situations of real need. We will provide a GPU-accelerated system suitable for use with deep learning frameworks like Google TensorFlow. You will train it using data from online social networks such as MumsNet, to recognise and anticipate situations where people are going to use words like “hopeless”, “depressed” or “suicide". With the help of deep learning, even simple a bag of words, together with metadata such as time of day, location and comment feedback will be sufficient to recognise trigger conditions in large data sets and mobilise assistance.

2016, sponsored Safer Chicken from Farm to Fork

In 2015, sponsored Live coding for blind children