Xiaoxi(Lexie) Zhou

- Leading research on solving chemical reaction kinetics using Physics-Informed Neural Networks

- Build 2*2 drone from scratch - Finite analysis for drone arm pressure and fluid dynamics simulation for propellers - Calculate thrust for propellers and arms. In charge of buying drone parts like blades and motors

- Preparing Team Macau for International Young Physicists’ Tournaments. - Recruited as Macau’s Team Coach for IYPT 2025 due to being best participant on China’s team during IYPT 2024. - Mentored students in theoretical modeling, numerical simulation, and experimental skills on advanced undergraduate to graduate level research projects. - Led presentation and argumentation training sessions, playing the presenter role for all 17 IYPT problems to prepare students for different opposition cases. - Macau’s team won the best award in Macau’s history. - Some highlights of our work include studies on: Levitating Fluid — studying surface ripples in liquids under high-frequency vibrations, primarily Faraday waves; Water Bottle Rocket — inflating a water bottle with air before release, where water is forced out by pressure and propels the rocket, researching its maximum height, where we used k-method for turbulent analysis, and considered the energy brought by the mixture of air and water.

- Optimized algorithms to predict solar energy output over time, based on environmental conditions (wind, humidity, etc.) - Increasing power generated in mountainous regions by 5%

- Implemented deep learning methods, including multi-layer perceptrons, to predict energy distributions of particle collisions under different magnetic fields; collaborated with Professor Gunther Roland. - Paper “Energy Distribution of Particle Flow in PP Collider” accepted by 4th International Conference on Computing Innovation and Applied Physics (CONF-CIAP 2025)

- Collected data using the Large Hadron Collider studying neutrinos’ energy with Professor Andrew Hass. - Designed and compared Convolutional Neural Networks for particle flow prediction - Finished “Comparison Between Traditional Energy Reconstruction Methods and Machine Learning based Particle Flow Algorithm”