About Me
Motivated Lifelong Learner
I really enjoy coming up with ideas, and through technology, make the idea come to fruition. Recently, I became very interested in Cloud Computing and the concept of designing a scalable, secure and resilient system!
My Interests
Data Science & ML
I love exploring and uncovering hidden patterns to gain insights from vast amount of data to come to a decision/solution with a strong basis.
Cloud Infrastructure
I am very interested in Cloud Computing and the concept of designing a scalable, secure and resilient system! Currently deepening my understanding through AWS resources.
Backend
I enjoy implementing the backbone of applications, to ensure smooth data flow, efficient processing, and secured interactions.
My Experiences
Will be working on RAG specific projects.
Working under the supervision of Mr Xing Han Lu on WebChat, a multi-layered communication framework for dialogue and LLM web agents.
Spearheaded the low-level implementation of the core layer in Python, utilizing a custom-built publisher-subscriber system without external dependencies.
Enhanced model performance through targeted error analysis and refined preprocessing techniques, resulting in a 2,8% increase in True Matches and a 7,8% improvement in recall.
Developed and implemented DVC-tracked inference and evaluation pipelines for diverse in-house deep learning models, optimizing model deployment processes and performance assessment workflows.
Developed comprehensive unit tests and modified langchain’s FakeListLLM class to accommodate generalizable LLM unit test modules and implement event callbacks, enhancing system flexibility and testability.
Developed computer vision algorithms in OpenCV to perform color detection, contour extraction, and 2D coordinate mapping of colored objects to enable robotic arm calibration for pick and place operations.
Maintained and optimized existing code for an autonomous rover bot by refactoring and debugging to improve performance and precision.
Developed a cutting-edge Web Visualizer to facilitate real-time hosting of live camera feeds, live point clouds from the LiDAR Sensor, on-demand SLAM, and alarm system detection using React js, three js, and ros.
Built a robust backend infrastructure utilizing the ROS communication protocol, rosjs, and seamlessly integrated Livox SDK API for its detection capabilities on the Web Visualizer.
Calculated and retrieved the IMU of the LiDAR within the LiDAR Simulation with perfect accuracy rate, enabled the creation of a realistic SLAM map of the simulated landslide, and moving cars.
Successfully calibrated the live camera feeds result and overlay them on top of the point cloud feed based on theintrinsic and extrinsic parameters of the camera.