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Current academic year...

[for the syllabus 🔗 ]

Machine learning and neural networks III (MATH3431)
Epiphany
(stochastic gradient, support vector machines, kernelised models, GP, neural networks)
Description 🔗 ; Lecture notes 🔗 ; Exercise sheets 🔗 ; Computer labs (workshops) 🔗

Spatio-temporal statistics IV (MATH4341)
Michaelmas
(point referenced data analysis; areal data analysis; point pattern data analysis; INLA)
Description 🔗  ;  Lecture notes 🔗 ;  Exercise sheets 🔗 ;  Computer labs (Comp. practicals) 🔗

BSc/MMath Project III/IV (MATH3382/MATH4072)  
  • Bayesian hierarchical modeling and analysis of spatial data 🔗
    2024/25

Doctoral students...
 
Yue Zhang (Jan 2022-now)
  • 2nd year PhD student shared supervision with Dr Samuel Jackson on Machine learning methods and spatial statistics
Linbin Lai (2023-now)
  • 1st year PhD student year PhD student shared supervision with Dr Konstantinos Perrakis on Machine learning methods and computational statistics
Ngoc Nguyen (2023-now)
  • 1st year PhD student year PhD student shared supervision with Dr Cuong Nguyen on Machine learning methods

Kieran Richards (2018-2022)

  •  Graduated; shared supervision with Prof Ian Vernon on MCMC, ABC, Spatial statistics with high dimensionality.
  • PhD thesis title: Approximate methods for otherwise intractable problems


   BSc, MMath, MSc project / dissertation general topics...

BSc/MMath Project III/IV (MATH3382/MATH4072) 
  • Bayesian hierarchical modeling and analysis of spatial data 🔗
    2024/25

  • Machine Learning for classification and clustering; methods & applications 🔗
    2023/24
  • Machine Learning for classification and clustering; methods, theory, & applications 🔗
    2023/24
  • Bayesian hierarchical modeling and analysis of spatial-temporal data 🔗
    2020/21

  • Statistical techniques and models for the analysis of functional data  🔗
    2021/22

  • Bayesian hierarchical modeling and analysis of spatial data 🔗
    2020/21

  • Bayesian computational methods  🔗
    2019

  • Bayesian statistics under model uncertainty and computations 🔗
    2020/21

MSc Scientific Computing and Data Analysis (G5K609) Dissertations
  • Statistical and Machine Learning methods for classification and clustering 🔗
    2022, 2024
  • Bayesian hierarchical modeling and analysis of spatial and spatio-temporal data  🔗
    2021, 2024

  • Calibration of computer models with Bayesian global optimization  🔗
    2020

MDS Master of Data Science (G5K823) Dissertations
  • Geostatistical methods for the analysis of spatial data 🔗
    2021


Summer research projects / internship topics...

London Mathematical Society undergrad research project
  • Bayesian computational methods for big data
    July & August, 2020
    • @ Department of Mathematical sciences, Durham University, UK
  • Gaussian processes emulation in big data
    July & August, 2017
    • @ Department of Mathematical sciences, Durham University, UK

International Association for the Exchange of Students for Technical Experience

  • Monte Carlo methods for big data
    July & August, 2017
    • @ Department of Mathematical sciences, Durham University, UK

Courses taught ...

MMath Master of Mathematics (G103) ; Mathematics and Statistics (G114)
BSc in Mathematical Sciences (G100) ; Mathematics and Statistics (G111)
Department of Mathematical sciences, Durham University, UK

***
[for the syllabus 🔗 ]

Undergraduate levels III, IV & postgraduate

Bayesian statistics III/IV/V (MATH3341/MATH4031/MATH43220) 
Michaelmas
(inference & methods)
Description 🔗  ;  Handouts 🔗 ;  Exercise sheet 🔗 ;  Computer practicals 🔗

Bayesian statistics III/IV/V (MATH3341/MATH4031/MATH43220) 
Epiphany
(MCMC & computations)
  • Teaching material available from Blackboard ULTRA
    2022

Topics in Statistics III/IV (MATH3361/MATH4071) 
Michaelmas
(categorical data analysis, likelihood methods & math stats)
Contingency tables:                       Handouts 🔗 ;  Exercise sheet 🔗 ;  Computer practicals 🔗
Likelihood methods & math stats: Handouts 🔗 ;  Exercise sheet 🔗

Undergraduate level IV

Spatio-temporal statistics IV (MATH4341)
Michaelmas
(point referenced data analysis; areal data analysis; point pattern data analysis; INLA)
Description 🔗  ;  Handouts 🔗 ;  Exercise sheets 🔗 ;  Computer labs 🔗
Description 🔗  ;  Handouts 🔗 ;  Exercise sheet 🔗 ;  Problem classes 🔗

Undergraduate level III

Machine learning and neural networks III (MATH3431)
Epiphany
(stochastic gradient, support vector machines, kernelised models, GP, neural networks)
Description 🔗 ; Handouts 🔗 ; Exercise sheet 🔗 ; Computer practicals 🔗
Description 🔗 ; Handouts 🔗 ; Exercise sheet 🔗 ; Computer practicals 🔗
Description 🔗  ;  Handouts 🔗 ;  Exercise sheet 🔗 ;  Computer practicals 🔗

Statistical methods III (MATH3051) 
Epiphany
(linear models, PCA)
  • Teaching material available from DUO
    2022
Handout for Mixtures & EM 🔗 ; Code for PCA on images 🔗

Undergraduate level I

Statistics (MATH1541)
Epiphany
  • Teaching material available from DUO
    2017, 2020


Short courses ...

Introduction to Bayesian Statistics (UTOPIAE)
  Summer, 2018
  • @Durham Training School, Department of Mathematical sciences, Durham University, UK
  • Learning material in GitHub 🔗 ; Handouts 🔗 ; Slides 🔗
  • Karagiannis G.P. (2022) Introduction to Bayesian Statistical Inference. In: Aslett L.J.M., Coolen F.P.A., De Bock J. (eds) Uncertainty in Engineering. SpringerBriefs in Statistics. Springer, Cham.


Introduction to Gaussian process regression (SURF)

August, 2016


Markov chain Monte Carlo (in course: Introduction to Uncertainty Quantification)

March, 2016


Web applets for the courses...

Web applets are available from : GitHub  🔗

[AppAnywhere 🔗]  ; [DUO 🔗]  ; [Blackboard ULTRA 🔗]