In 2018, I took part in the Science Mentorship Programme (SMP) together with two of my classmates. We worked on Acoustic Beaconing, the process of encoding data into soundwaves for transmission. Specifically, we looked at what factors affected the bit error rate with asynchronous binary frequency shift keying as our encoding method. We wrote our simulation code with Python 3.6.

After SMP, I participated in R@YDSP where my research partner and I worked on generative grammar-based fuzzing. We upgraded the Domato black box fuzzer by searching for more CSS and SVG elements, attributes, etc using a web scrapper and regular expressions. Furthermore, we implemented fuzzing of eXtensible Stylesheet Language Transformations (XSLT) with use of the Domato engine.

During the break between Secondary 4 and JC 1, I was attached to the robotics laboratory at SUTD for a month. Here, we were tasked in comparing different image classification neural network models for application in a reconfigurable staircase cleaning robot. The networks classified the stairs as being either straight, curving left, curing right, or negative (no stairs).

After JC 1, I was attached to DSTA's Cybersecurity Programme Center at DSTA. Specifically, I joined the hardware vulnerability analysis team and learnt about hardware security of routers. I was exposed to various hardware debugging interfaces such as UART and JTAG and used them to connect, communicate and control my target router.

During IMMC 2019, we were tasked with estimating the maximum carrying capacity of the earth for sustainable development. To do so, we took into account land area, food, water and energy to ensure the survival of the humans in Singapore. This competition went over 5.5 days and my team received a distinction for our efforts.

This year, the task was to model the behaviour of shoppers during a flash sale, predict the level of damage and propose a layout to minimise the level of damage to a store (given a list of items to buy). Our team used Python together with the A* Algorithm to simulate the shoppers.

Taking inspiration from a project work presentation, I attempted to create software that could read-off bus numbers in an image for application in smart-glasses for the visually impaired to more easily take buses at bus stops. This was done using object detection, optical character recognition and speech synthesis software, many of which are from Microsoft Azure.

To improve the experience of students during Home-Based Learning (HBL) due to COVID-19, I initiated a project to create a HBL dashboard where classes could add their frequently used HBL links, view a real-time timetable and utilise a virtual school bell (amongst other things). Technologies used included Flask, JQuery and Semantic UI.