PhD in data science, expert in time series analysis and image data. Experienced in applying machine learning to industrial optimisation, with a passion for research and teaching to inspire enthusiasm for data and engineering efficiency.
Direction of data science and R&D for environmental water monitoring: adapting PhD research in time series classification and image tracking to real-time monitoring, onboard systems (C++, SQL, Zigbee, MQTT) and Python ML pipelines. Led a team of two data scientists and two masters internships, drove the Viewpoint/INRAE laboratory partnership, and co-wrote grant proposals worth 15% of company turnover.
Statistics and neural network models (Python, R) for time-series clustering and multivariate functional data, integrated into real-time monitoring. Presented at 8 international conferences; published as first author in Water Research and Science & Technology.
Authored and tutored remote Python learning through PyGame and artificial intelligence. Earlier: data science internship at Naldeo Technologies on predictive maintenance metrics.