# About me

**Research**

My previous work explored a structured (categorical) approach of using recursion schemes to implement neural networks, letting compositionality be promoted in new ways. In particular, I show how neural networks can be represented as fixed-points of recursive data structures, and forward and back propagation as catamorphisms (folds) and anamorphisms (unfolds) over these structures.

**Papers**

Jan, 2022 Linked visualisations via Galois dependencies

R.Perera, M.Nguyen, T.Petricek, M.Weng

^{ POPL ‘22}

Aug, 2021 Composable, Modular Probabilistic Models [Poster]

M.Nguyen, R.Perera, M.Weng

^{ ICFP ‘21, Student Research Competition}

Jun, 2019 Modelling Neural Networks with Recursion Schemes [Poster]

M.Nguyen, N.Wu

^{ Masters Dissertation, University of Bristol}

**Talks**

2021 Composable, Modular Probabilistic Models

^{ IFL ‘21, ICFP ‘21 (Poster)}

**Teaching**

I give talks/seminars to the Programming Languages Research Group and undergraduates at the University of Bristol.

2020 - 2021

I've acted as the main supervisor for a 4th year student in their masters dissertation.

2017 - 2020

I've acted as a teaching assistant for the Functional Programming, Language Engineering, and Advanced Topics in Programming Languages units at the University of Bristol.

**Awards**

*ACM Student Research Competition, 1st Place (ICFP '21)*

2019

*Bloomberg Award - Best Machine Learning Paper, University of Bristol*

2018

*Graphcore Award - Best Group Project, University of Bristol*

2017

*Netcraft Award - Top Ten Achieving CS Students, University of Bristol*