Causal machine learning: fully-funded PhD scholarship at the University of Birmingham

[Max Little]

An exciting opportunity at the cutting edge of mathematical data
science, in this fully-funded PhD project you will study and develop
novel machine learning algorithms for high-precision causal
relationships from observational data. Modern machine learning
techniques, such as deep learning, are purely associational, that is,
they learn to associate inputs to outputs. In practice, observational
data captures multiple relationships between variables other than the
intended relationship, and modern, high-capacity algorithms such as
deep learning are particularly prone to finding such relationships.
This is a serious problem for high-stakes applications such as
healthcare, where spurious, non-causal predictions can have
life-altering consequences. By contrast, appropriate experimental data
can eliminate these spurious effects but it is often logistically
difficult, if not impossible, to carry out such experiments. Methods
from causal inference based on formalisms such as Pearl’s do-calculus,
allow estimates of causal relationships from observational data, but
require fully probabilistic modelling of all the variables involved.
This is largely incompatible with modern, high-capacity machine
learning algorithms which are mostly non-probabilistic and where
training is performed using gradient descent. The aim of this research
is to develop non-probabilistic machine learning algorithms for causal
inference, which combine both the causal correctness of classical
causal inference methods, and the high capacity of modern machine
learning algorithms. The project will have immediate applications to
problems at the leading-edge of digital health, such as wearable
devices for monitoring the symptoms of neurological disorders.

You will join the group headed by Dr Max Little at the Computer
Science department of the University of Birmingham. The group combines
the mathematical theory of machine learning and signal processing,
with applications to digital health. There will be multiple
opportunities to interact with the wider community at Birmingham CS
and across disciplines. This well-established group has a track record
of successful supervision of PhD students from diverse backgrounds,
placing them in both industrial and academic positions.

The subject of this PhD project falls into the broad area of machine
learning, signal processing, probabilistic modelling and causal
inference, therefore, a solid background in physical applied
mathematics, mathematical statistics and/or physics would be
appropriate. Programming skills will be a definite advantage, as it
will be necessary to carry out extensive computational experiments.

We want our PhD student cohorts to reflect our diverse society. UoB is
therefore committed to widening the diversity of our PhD student
cohorts. UoB studentships are open to all and we particularly welcome
applications from under-represented groups, including, but not limited
to BAME, disabled and neuro-diverse candidates. We also welcome
applications for part-time study.

Eligibility: First or Upper Second Class Honours undergraduate degree
and/or postgraduate degree with Distinction (or an international
equivalent). We also consider applicants from diverse backgrounds that
has provided them with equally rich relevant experience and knowledge.
Full-time and part-time study modes are available.

If your first language is not English and you have not studied in an
English-speaking country, you will have to provide an English language
qualification.
________________________________

Funding Notes

The position offered is for three and a half years full-time study.
The value of the award is stipend; £15,285 pa; tuition fee: £4,407.
Awards are usually incremented on 1 October each year.

If you wish to discuss more about the project, please contact me (ma...@mit.edu).

Apply here:
https://sits.bham.ac.uk/lpages/EPS003.htm

Best
Max

--
Max Little (www.maxlittle.net)
Senior Lecturer, University of Birmingham
TED Fellow (fellows.ted.com/profiles/max-little)
Visiting Associate Professor, MIT
Author: Machine Learning for Signal Processing, Oxford University Press
global.oup.com/academic/product/machine-learning-for-signal-processing-9780198714934
Room 138, School of Computer Science
University of Birmingham
Birmingham B15 2TT
UK +44 7710 609564
Skype: dr.max.little