AI Software Tool Creation Leveraging NASA MERRA-2 Data: A Seminar Series

AI Software Tool Creation: Leveraging NASA MERRA-2 Data by AI-vision

in collaboration with the UKSEDS

Iceberg Drift Forecasting Using Coupled Earth Observation and Machine Learning

Wed Jun 25th, 6.30pm BST

Explore the integration of NASA MERRA-2 data with ML to predict ocean surface drift, enabling forecasting and visualisation of spatiotemporal maritime trajectories to enhance situational awareness in ocean monitoring.

Rocket Launch Environment Simulator with ML Integration

Thurs Jun 26th, 6.30pm BST

Integrate NASA MERRA-2 data with RocketPy to simulate site-specific launch conditions and predict apogee using KNN for aerospace performance analysis

Optimising Solar-Powered UAV Performance with Data-Driven Modelling

Tues Jul 1st, 6.30pm BST

Discover how NASA solar datasets and advanced modelling techniques improve solar UAV wing design and energy efficiency. This session covers simulation using GPkit and anomaly detection in UAV sensor data with PCA for enhanced flight performance insights.

Wildfire Risk Prediction Using NASA Climate Data

Wed Jul 2nd, 6.30pm BST

Explore a deep learning pipeline that predicts wildfire risk using real NASA MERRA-2 climate data. This session covers data preprocessing, feature engineering, neural network training, and geospatial risk mapping with Python.

ML Meets Quantum Inspiring Climate & Aerospace QML Applications

Thurs Jul 3rd, 6.30pm BST

Explore cutting-edge quantum machine learning (QML) models that could serve as a foundation for tackling real-world challenges such as climate anomaly detection and predictive aerospace performance. This session will examine how these methods could be adapted for use with complex datasets such as NASA MERRA-2 reanalysis data and rocket telemetry, to support decision-making in high-dimensional, data-rich environments.

Seminars will be delivered by Nicola Jane Buttigieg

Nicola is a Research Affiliate at the King’s Institute for AI and a PhD student at King’s College London, where her research explores the transition from classical to quantum machine learning. She leads machine learning training for the King’s-Bolashak and 500 Scholars Programme. Previously, she directed upskilling and outreach initiatives at Staffordshire University London and Boston University’s Spark! Learning Lab. Nicola also developed a pre-university space technology diploma for the Girls’ Day School Trust, supported by the Royal Society STEM Partnership and AWS grants.