I am a rising sophomore studying Robotics and Machine Intelligence at the National University of Singapore (NUS). I am passionate about building intelligent systems that bridge the gap between software and hardware—whether that involves autonomous robots, computer vision applications, or interactive embedded devices.

My work sits at the intersection of embedded systems, machine learning, and robotics. I enjoy tackling challenges that require a balance of algorithmic thinking and hands-on engineering. From training neural networks to designing circuit boards, I am energized by projects that demand diverse technical skills and creative problem-solving.

Computer Vision & Machine Learning
I am fascinated by how machines can learn to see and interpret the world. My interests span image classification, object detection, and building machine learning pipelines that work reliably in real-world conditions. I am particularly drawn to the practical challenges of deployment, such as handling noisy data, edge cases, and resource constraints that do not exist in textbook examples. Recently, my work has focused on the implementation and optimization of Vision Transformers.

Autonomous Robotics
There is a deep satisfaction in watching a robot navigate its environment independently. I am interested in the perception systems and path planning techniques that enable robots to understand and respond to their surroundings. I work with platforms like ROS 2 and enjoy the challenge of translating theoretical algorithms into working robotic systems. Currently, I am a member of the Software Team at Bumblebee Autonomous Systems, where I contribute to the development of autonomous underwater vehicles (AUVs).

Embedded Systems & IoT
I enjoy building intelligent devices from the ground up. Whether it is microcontroller programming, real-time audio processing, or designing communication protocols between boards, I am drawn to projects where hardware and software must work in tight coordination. I find unique satisfaction in optimizing code to run on resource-constrained devices while maintaining high levels of responsiveness and reliability.

Outside of my formal studies, I spend time exploring new frameworks and tools, working on personal projects, and staying current with developments in robotics and AI. I am particularly interested in how recent advances in machine learning can be practically applied to robotic systems and embedded devices. I enjoy collaborating with others who share similar interests and am always eager to learn from people with different perspectives and skill sets.

As I continue my studies, I am excited to deepen my knowledge in autonomous systems, computer vision, and embedded AI. My primary academic goal is to move beyond static automation and delve into the field of Robot Learning.

I am eager to explore how robots can move past pre-programmed instructions to acquire complex behaviors through interaction with their environment. I am particularly interested in how Reinforcement Learning and Imitation Learning serve as pathways toward achieving true machine intelligence. By researching these learning-based approaches, I hope to develop robots that can adapt to the unpredictability of the real world.