**I am usually on the lookout for new PhD students, Postdocs, Masters and Bachelors students to join the team**

Please visit:

- MAC-MIGS Centre for Doctoral Training
- Centre for Doctoral Training in Biomedical AI
- Computational Applied Mathematics MSc

* BSc and MMath students normally get involved via final year projects; scroll down for examples of recent projects.*

*Professor of Applied Mathematics *

*PhD Student, MAC-MIGS programme, started 2019. Studying distributed algorithms for deep learning and constraint-based neural network training. Co-supervised by Michele Ottobre (HWU) and Amos Storkey (Informatics). *

*PhD Student, MAC-MIGS programme, started 2020. Research topic - machine learning for molecular dynamics. Currently studying symmetry preservation in machine-learned force fields. Co-supervised by Ben Goddard (Mathematics), Antonia Mey (Chemistry), James McDonagh (IBM Research) *

*PhD Student, MAC-MIGS programme, started 2020. Research topic- scalable Bayesian inference methods for high dimensional stochastic models. Currently studying sampling methods on manifolds. Co-supervised by Daniel Paulin (Statistics). *

*PhD Student, MAC-MIGS programme, started 2020. Research topic- simplification of causal graphs and analysis of neural computational models. Co-supervised by Michal Branicki (Mathematics). *

*PhD student, Biomedical AI CDT programme, started 2021. Research topic -incorporating machine learning into molecular dynamics for accelerated drug discovery; Brownian dynamics algorithms. Co-supervised by Antonia Mey (Chemistry) and Flaviu Cipcigan (IBM Research). *

*PhD Student, MAC-MIGS programme, started 2021. Studying variable step size numerical methods for SDEs. Co-supervised by Jonas Latz (HWU) and Des Higham (UoE). *

* PhD Student, MIGSAA CDT programme, started 2017. Research interests- Developing new optimization schemes for the training of deep neural networks, numerical methods for stochastic differential equations, deep learning theory, molecular dynamics, sampling methods.*

* postdoc visiting the group from 2019-2021 studied constrained algorithms for neural network training*

* Worked on generalized Langevin equation, nonequilibrium models, and numerical methods. Moved on to postdocs first at SAMSI and Duke University, then to UBC where he is now working with Christoph Ortner.*

* numerical methods for dissipative particle dynamics, adaptive (noisy gradient) sampling algorithms, machine learning training algorithms*

* algorithms and software design for ensemble study of neural networks and complex models; the force behind TATi the Thermodynamic Analytics Toolkit.*

* studied algorithms based on diffusion maps for accelerating the sampling of molecular models; she also set up a collaboration with DNV GL to study classification methods for wind form SCADA data and worked with Anton Martinsson to create the acwind python package for this purpose; currently working in Paris as a machine learning developer.*

* worked on sampling algorithms including an infinite swap simulated tempering algorithm; these methods were implemented in MIST by Iain Bethune. Also developed the acwind python software for wind turbine farm data analysis, with Zofia Trstanova.*

* high performance computing genius and chief architect of the MIST software suite for interfacing novel integration methods into existing molecular dynamics codes like LAMPPS and Gromacs. Studied a wide variety of algorithms using MIST. Defended his PhD in late 2020. Currently head of software development at a Perth startup.*

* collaborated on a variety of projects, include Langevin integrators and their application/analysis in molecular dynamics applications, including constrained MD methods. Moved to a postdoc at U. Chicago after his PhD and worked with J. Weare (further collaboration on an ensemble sampling method). Returned for projects in 2018-19 on wind data analysis, among other things. Now working in machine learning for an Edinburgh startup.*

I supervise projects in the Computational Applied Mathematics (CAM) MSc Programme .

** Xuexun Lu (now - PhD Student, Warwick University)** , 2022 : Bayesian Uncertainty Reduction Analysis by Langevin Dynamics, Sampling

** Alix Leroy (now - PhD Student, Edinburgh University)** , 2021 : Numerical methods for weak approximation of stochastic differential equations and their applications to urban modelling

** Kermani Nejad Mohammadreza (now - PhD Student, Bristol University)** , 2020 : Acceleration of Statistical Sampling with Application to Machine Learning

** Kevin Cheok Kim Tsang (now - PhD Student, Edinburgh University)** , 2019 : Efficient Learning Algorithms with Incomplete Gradients

I supervise a limited number of BSc projects.

** Gabrijel Boduljak **, 2022 : On Universality of Fully-Connected Neural Networks

** Manuel Brea Carreras **, 2022 : Feedforward Neural Networks - Approximation Properties and Robustness as Classifiers

** Neil Macintyre, Sam Kelso, Ruairidh McVean **, 2022 : Deep Neural Networks - Analytical and Numerical Studies

** Guiomar Pescador Barrios, Jay Holley, Leah Seah **, 2021 : Adaptive Single Hidden Layer Perceptrons

** Ben Honey, Charlotte Tyndale-Hardy and Sam Weston **, 2020 : Evaluating the Fairness of Political Constituencies Using Monte Carlo Sampling II

** Luke Donnely, Kate Hassell, Stephen Cameron **, 2019 : Evaluating the Fairness of Political Constituencies Using Monte Carlo Sampling I