Sponsored by the Chinese Scholarship Council Fengpei graduated from Beijing Institute of Technology with a BSc in Mathematics and Applied Mathematics in 2017. During her degree, she spent a year at University College London. The following year she completed her MSc at Imperial College London in Pure Mathematics and her dissertation explored quantum dynamical semigroups generated by the Lindblad type unbounded generator. She is looking forward to exploring various applied topics, especially in stochastic process and numerical methods. Aside from maths, she also enjoys travelling, swimming and badminton.

Research project title:
Computational Optimal Transport

Supervisor(s):
Clarice Poon, Tony Shardlow

Project description:
Optimal transport seeks the best way of transforming one probability distribution into another. It provides a natural, elegant framework for comparing probability distributions while respecting the underlying geometry. Within machine learning and related fields, optimal transport distances (in particular the Wasserstein metric) have found successful application in things like image registration and interpolation and adversarial neural networks. However, computing optimal transport distances between arbitrary (i.e. not necessarily discrete) probability distributions remains a challenging problem. Fengpei is studying the use of the Sinkhorn algorithm for computing the optimal transport between continuous measures, given point samples. She is interested to explore how to combine optimal transport with several different methods. These include stochastic algorithms and acceleration techniques by screening out variables.

Fun fact(s):
● I have watched the Big Bang Theory over 10 times.
● I barely drink and have never got drunk.
● I have a puppy!