Academic CV

Vanessa Madu

Email: vanessa.madu18@imperial.ac.uk
LinkedIn: linkedin.com/in/vanessa-madu
GitHub: github.com/vanessamadu

Education

PhD in Modern Statistics and Statistical Machine Learning, Imperial College London

October 2023 - October 2027
Supervisors : Dr Adam Sykulski & Dr Marina Evangelou

• EPSRC CDT between Imperial College London (home institution) and the University of Oxford.

• Relevant Courses:

  Technical: Object-Oriented Python, High Performance Computing, Parallel Programming on HPC, Mathematica for Research & Teaching, Good Software Engineering Practice.

  Research Development: Literature Review, Responsible Research, Writing Theses in LaTeX.

  Teaching and Public Engagement: Core Public Engagement Masterclass, Public Engagement with Research, Learning and Teaching, Feedback and Assessment for learning.

MSci Mathematics, Imperial College London

October 2018 - June 2023
London, United Kingdom

• First class honours.

• Relevant Courses:

  Statistics: Time Series Analysis, Mathematical Foundations of Machine Learning (Coursework implemented using PyTorch)

  Ocean Science: Fluid Dynamics, Geophysical Fluid Dynamics, Vortex Dynamics, Hydrodynamic Stability

  Other: Mathematical Biology, Computational Linear Algebra, Introduction to Fortran

Research Projects

Robust Models for Time-Varying Signals, Imperial College London

March 2024 - June 2024
Supervisor : Professor Alessandra Luati

• Produced a new and thorough proof of the validity of probability density function (PDF) for a weighted infinite sum of iid uniform variables used in the working paper Robust filtering of a location parameter Catania, L., Harvey A.C. and Luati A. (2024), and provided new insightful proof details.
• Derived a general (novel) PDF for the infinite sum of iid symmetrically distributed random variables with the conditions on the distribution and weights for existence.

Physically Interpretable Error Metrics for Ocean Surface Models, Imperial College London

November 2023 - February 2024
Supervisor : Dr Adam Sykulski

• Proposed a system of model evaluation and uncertainty metrics for a phenomena-centred ocean surface modelling approach to maximise information gain when learning from ocean data.
• Demonstrated the value of these additional model evaluation metrics for simple drifter velocity model examples.

Computational Modelling of the Clustering of Floating Drifters on the Ocean Surface, Imperial College London

October 2022 - June 2023
Supervisor : Professor Pavel Berloff

• Constructed a dynamically inspired (novel) velocity field from a spectral density function to study the clustering of floating tracers in this more realistic field
• Applied data tapering methods from Time Series Analysis to limit the lifetime of each wave involved in constructing the environment, producing a more interesting velocity field
• Produced simulations of the velocity field using Fortran

Skills

• Programming Languages:

  Preferred: Python, Mathematica, Fortran

  Experience with: MATLAB, R

• Web Development: HTML, CSS, JavaScript, React
• Tools & Technologies: Git, LaTeX
• Analytical: : Data Processing, Analysis and Visualisation, Probabilistic Regression Analysis, Neural Networks with PyTorch, Object Oriented Programming

Awards

• President’s Award for Excellence in Societal Engagement, Imperial College London

  Awarded in 2020 and 2024

• RCSA Prize, Royal College of Science Association (2023)

  Awarded for academic excellence and contribution to the College community

• President’s Award, Royal College of Science Union (2023)
• Outstanding Achievement Award (Medal), Imperial College London (2022)

• Dean’s List, Faculty of Natural Sciences (2022)

  Top 10% of my cohort

• Rising Star Award, Rare (2020)

  Top 10 Black students in the UK

• Young Star Award, Women of the Future (2019)
• First Year Poster Prize, Imperial College Department of Mathematics (2019)

Research Funding

• EPSRC, PhD Studentship (2023 - 2027)

• Fully-Funded Workshop Places:

  US CLIVAR, Ocean Uncertainty Quantification Summer School, Miami, Florida (2024)

  NOC, CLASS Ocean Modelling Workshop, Southampton, UK (2024)

  Eddies and Waves: Theory, Models, and Observations, Hamburg, Germany (2023)

Outreach and Public Engagement

• Year 12 Sutton Trust Summer School - Mathematics Stream, Imperial College London

  Academic Lead (2024 - Present): Developed and taught an introductory session on Fluid Dynamics. Co-delivered sessions exploring Game Theory, Statistics, Infinity, Iterated Function Systems and Fractals.

  Academic Mentor (2022-2023): Gave the talk titled Around the Mathematical World in 18 minutes exploring the world of mathematics beyond the A-Level syllabus. Assisted in delivering academic sessions.

• Maths at the Carnival, Begbroke Science Festival, 2024

  Co-created and co-delivered a festival stall with CDT colleagues exploring Game Theory with young children and families.

• Primary School Maths/Science Engagement:

  Primary Maths Transition Program (2024): Delivered an in-classroom session on problem solving and puzzles to a Year 5 class.

  Primary Science Club (2024): Delivered sessions on the brain, energy, and light at an in-classroom afterschool science club.

• Engagement Leader, White City Invention Rooms, Imperial College London (2022 - Present):

  Co-created and co-delivered a Saturday science club session on complex (mathematical) systems for local families.

  Created a fluid dynamis activity for STEM Activity Packs distibuted to around 1500 local families.

  Co-delivered a Charity Fun Day science stall, Wood Lane Estate Community Centre.

• Co-creator and Guest Speaker, Hello World Hack, Imperial College London (2023)

  Proposed and co-created the Hello World Hack, a coding workshop aimed at girls aged 7-10, introducing them to mathematics and computing in a fun, engaging, and inspiring way.

  Gave a talk on What Maths has to do with Marbles, Oceans and Rubber Ducks.

Memberships

• Association of British Science Writers (2024)
• Royal Statistical Society (2024)
• Challenger Society for Marine Science (2024)

Languages

• English (Native)