Education

Publication

• Cummins, C.P., Ajayi, O.J., Mehendale, F.V., Gabl, R. and Viola, I.M., 2020. The dispersion of spherical droplets in source-sink flows and their relevance to the COVID-19 pandemicLink opens in a new window. Physics of Fluids, 32(8), p.083302.

Conferences and Contributed Talks

• Turing CDT Conference | University of Birmingham, 14 - 15 November 2023 (linkLink opens in a new window) (and)
• First Workshop on Artificial Social Intelligence | University of Glasgow, 28 August 2023 (linkLink opens in a new window)

Poster Presentation: Improving Skeleton-based Action Recognition with Ordered Skeletal Joints

Most skeleton-based action recognition algorithms use a self-attention-based approach for embedding the skeletal graph to the spatial domain. But unlike sequences where their inherent order serve as suitable position encoding, a skeletal graph has no obvious order that can be used as a position encoding alongside the self-attention mechanism. My poster would showcase my proposed algorithm where we used a supervised contrastive learning approach to learn a suitable ordering for the nodes in the skeletal graph.

• SPAAM Seminar | University of Warwick, 8 December 2022 (and)
• Warwick (Undergraduate) Mathematics Society Seminar | University of Warwick, 6 December 2022
  Talk: "Deep Spatiotemporal Models for Surveillance"

Have you ever wondered how surveillance is achieved by most governments of the world? How they are able to regularly and accurately monitor your physical activities? Though conversations on politics, policies and ethical correctness are relevant for this discussion, rather we would focus on the design of the machine learning models that helps achieve surveillance. In particular, I would be discussing my research on human action recognition using graph neural networks in addition to temporal models.

• Signal and Information Processing Lab (SIPLab) Seminar | University of Warwick, 19 October 2022
  Talk: "Human Action Recognition Using Human Skeletal Graph"

In an effort to teach machines how to recognize human actions, many literatures have proposed how to achieve this using the human skeletal structure alongside the coordinates of specific joints on the human body. To make the best use of the skeletal structure, graph neural networks (GNNs) are proposed to help aggregate information across the human skeletal graph. This approach only deals with the spatial domain of the video data and leaves room for creativity in the temporal domain. The talk would focus on providing a background on the use of GNNs in human action recognition, and the approach I have adopted to get comparable results on benchmark datasets.

• MathSys CDT Annual Conference | University of Warwick, 25 - 27 April 2022

Teaching

Academic year 2023/24

Term 1

• Associate tutor for Intro to Computing for the 2023 MathSys MSc cohort [GitHub RepoLink opens in a new window]
  • giving an overview of bash scripting, python, GitHub and an introduction to HPC.
• TA for Methods of Mathematical Modelling 3 (MA265Link opens in a new window, second-year maths module) [GitHub RepoLink opens in a new window]

  • understand critical points of multivariable functions

  • apply various techniques to solve nonlinear optimisation problems and understand their applications, in economics and data science

  • use Lagrange multipliers and the Karush–Kuhn–Tucker conditions to solve constrained nonlinear optimisation problems

  • understand the basic concepts of approximation theory

  • obtain an understanding of different approximation techniques used in the digital sciences

  • support with marking assignments.
• TA for Matrix Analysis and Algorithms (MA398Link opens in a new window, third-year maths module) [GitHub RepoLink opens in a new window]
  • solve systems of linear equations, least-squares problems, and eigenvalue problems, using highly efficient solvers.
  • understanding how to construct relevant algorithms (using python) and problems central in numerical linear algebra and to analyse them with respect to accuracy and computational cost.
  • support with marking assignments.

Academic year 2022/23

Term 1

• Associate tutor for Intro to ComputingLink opens in a new window for the 2022 MathSys MSc cohort [GitHub RepoLink opens in a new window]
  • giving an overview of bash scripting, python, GitHub and an introduction to HPC.
• TA for Matrix Analysis and Algorithms (MA398Link opens in a new window, third-year maths module) [GitHub RepoLink opens in a new window]
  • solve systems of linear equations, least-squares problems, and eigenvalue problems, using highly efficient solvers.
  • understanding how to construct relevant algorithms (using python) and problems central in numerical linear algebra and to analyse them with respect to accuracy and computational cost.
  • support with marking assignments.
• TA for Mathematics by Computer (MA124Link opens in a new window, first-year maths module)
  • introduce the rudiments of computer programming (using python) to first-year undergraduates.
  • provide computer lab support sessions for python projects/assignments.
  • support with marking assignments.

Term 2

• TA for Differential Equations: Modelling and Numerics (MA261Link opens in a new window, second-year maths module)
  • help students understand the central concepts of mathematical modelling and analyse fundamental numerical methods.
  • implement and test numerical methods using python.
  • provide computer lab support sessions for python projects/assignments.
  • support with marking assignments.

Academic year 2021/22

Term 2

• TA for Differential Equations: Modelling and Numerics (MA261Link opens in a new window, second-year maths module)
  • help students understand the central concepts of mathematical modelling and analyse fundamental numerical methods.
  • implement and test numerical methods using python.
  • provide computer lab support sessions for python projects/assignments.
  • support with marking assignments.
• TA for Algorithmic Graph Theory (CS254Link opens in a new window, second-year computer science module)
  • help students understand the basics of graphs as it relates to practical examples. Design effective algorithmic techniques to study basic properties of graphs and for various optimisation problems on graphs.
  • support with marking assignments.

Other Activities (including non-academic)

Contact

Olayinka.Ajayi@warwick.ac.ukLink opens in a new window

Office: B2.09, Zeeman Building

Links

: Olayinka J. AjayiLink opens in a new window

: olayinkaajayiLink opens in a new window

: @ajayi_ola_joshLink opens in a new window