Chayapa Charoencholwanich

Background

Chayapa earned an MSci (Hons) in Physics from King's College London in July 2023 and was awarded an Associate of King's College (AKC) status. The AKC award, a distinctive honour at King's, reflects her engagement with ethical, philosophical, and interdisciplinary studies, complementing her scientific training and analytical approach to problem-solving.

She is intensely interested in computational methods, programming, and data analysis, which she developed through mathematical modelling, numerical simulations, and large-scale data processing. Her final-year research projects further strengthened her computational expertise, where she extensively used Python and High-Performance Computing (HPC) to handle complex datasets.

As part of her undergraduate studies, she undertook a project on the behaviour of light and objects near massive celestial bodies, culminating in a report titled "Supermassive Black Holes - Light Bending: The Test of General Relativity." Using mathematical derivations and MATLAB simulations, she analysed how General Relativity predicts the bending of light around massive objects more accurately than Newtonian physics.

For her MSci thesis, she worked on the LUX-ZEPLIN (LZ) dark matter experiment, focusing on improving event reconstruction algorithms for background radon decays. Her work involved analysing detector data to identify decay chains and distinguish between single- and multiple-scatter events. By refining event reconstruction techniques, she contributed to improving background rejection鈥攁 critical step in increasing the sensitivity of dark matter searches. This project sparked her interest in signal processing and noise reduction, recognising how eliminating noise can reveal valuable information for further analysis. Her curiosity about deep learning led her to explore advanced computational techniques for geolocation in her PhD research.

She began her PhD in June 2023 at 扣扣传媒's Defence and Security in the Centre for Electronic Warfare, Information and Cyber (EWIC), working in radio frequency technologies.

Research opportunities

- Machine learning for geolocation

- Signal processing

- Noise analysis and techniques to reduce its impact

- Deep learning applications in radio frequency technologies

- Non-cooperative emitter localisation in complex environments

- Data-driven approaches for geolocation accuracy and efficiency

Current activities

Chayapa is conducting PhD research in radio frequency technologies at 扣扣传媒's Defence and Security, within the Centre for Electronic Warfare, Information and Cyber (EWIC). Her research focuses on "Novel Deep Learning Techniques for Non-Cooperative Geolocation in Urban Environments."

Her work explores machine learning methods to improve the accuracy and efficiency of emitter geolocation, particularly in challenging and cluttered environments where traditional geolocation techniques can be computationally expensive and impractical for real-time applications.

She is currently working on integrating various neural network architectures with traditional geolocation techniques to improve the precision and reliability of geolocation across different environments. This includes extending these techniques to urban environments with non-line-of-sight (NLOS) conditions and multipath propagation, addressing the challenges posed by signal reflections and interference in complex real-world scenarios.