Morgan Stanley: Difference between revisions

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


In 2019:
David Blake <David.M.Blake@morganstanley.com> was suggested for 2022


[[Probable Causes]]
It would be particularly great if we could find a design project that aligns with Morgan Stanley's "Giving Back" themes around community, health and sustainability
https://www.morganstanley.com/about-us/giving-back
https://www.morganstanley.com/about-us/sustainability-at-morgan-stanley


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?
Previous Clients: Oli Bage and Theo Mauger (both have now left)


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. 
In 2019: [[Probable Causes]]


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.
In 2017: [[The Deep Learning Society]]


Idea for 2017:
In 2016: [[Safer Chicken from Farm to Fork]]


[[The Deep Learning Society]]
In 2015: [[Live coding for blind children]]
 
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]]

Latest revision as of 14:43, 22 October 2021

Primary contact

David Blake <David.M.Blake@morganstanley.com> was suggested for 2022

It would be particularly great if we could find a design project that aligns with Morgan Stanley's "Giving Back" themes around community, health and sustainability https://www.morganstanley.com/about-us/giving-back https://www.morganstanley.com/about-us/sustainability-at-morgan-stanley


Previous Clients: Oli Bage and Theo Mauger (both have now left)

In 2019: Probable Causes

In 2017: The Deep Learning Society

In 2016: Safer Chicken from Farm to Fork

In 2015: Live coding for blind children

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