Anh Van Thi Nguyen

PhD in Experimental Particle Physics specialising in statistical inference, stochastic modelling and Monte Carlo simulation, with a proven record of extracting weak signals from noisy, imbalanced data at scale, now applying machine learning and experimental design to commercial data problems.

PhD Researcher at The LUX-ZEPLIN (LZ) Collaboration, USA & The Boulby Underground Laboratory, UK (2020-01 – 2025-12)

• Analysed multi-terabyte datasets in Python to identify rare signal events against overwhelming backgrounds, applying statistical filtering and simulation-based techniques to highly imbalanced data.
• Implemented likelihood-based inference workflows, including hypothesis testing with the profile likelihood ratio approach, achieving a 25% improvement in detection sensitivity over previous analyses of the same model.
• Built physics-based simulation models of detector response and generated synthetic datasets to validate data-processing pipelines end to end.
• Developed the data-analysis pipeline adopted as the Boulby Laboratory's standard framework for radon progeny assays, improving reproducibility across detector workflows.
• Served as operations supervisor for real-time data-quality monitoring across a 250-scientist international collaboration, coordinating shifts across time zones to maintain data integrity.
• First-author publication (JINST, 2025).

Physics Teaching Assistant at University of Edinburgh (2020-01 – Present)

• Taught tutorials and problem-solving sessions across 10+ undergraduate courses, primarily in mathematical methods, data analysis, and Python programming, alongside core physics modules like quantum physics.
• Tailored explanations of abstract concepts to each audience's technical level, teaching cohorts of 30+ students from first-year to advanced undergraduate courses.

PhD in Experimental Particle Physics – University of Edinburgh (2020-01 – 2025-12)

MSci in Theoretical Physics – University College London (UCL) (2016-01 – 2020-12)