End-to-end model development and deployment, such as data analysis, model training, hyperparameters tuning, model selection and cloud deployment on Azure.

Worked with linear models, trees, random forest, XG boost and light gradient boosting machine (LGBM).

Pipeline External Corrosion: ML. Built pipeline external corrosion depth & length prediction models of >90% accuracy.

Pipeline Freespan Formulation: ML. Built subsea pipeline height & length prediction models of >70% accuracy.

HIP: Computer Vision. Worked on working person detection through object detection with YOLO, pose estimation and vLLM with GPT4o. Involved prompt engineering for better inference performance.

Ivy ML Models: Applied Lead. Led the team to expand ML model collection from scratch to 15+ within 4 months, including computer vision and NLP base models for business showcase.

Ivy Transpiler: Introduced the native compilation feature for optimising performance of ML models.

Model Hub: Full stack. Responsible in building client applications and API key authentication.

Console Application: Cloud. Involved in v0 development and API gateway authorisation.

Ivy Framework: Software Engineering. Unified PyTorch, Tensorflow, JAX and NumPy functions, making them easily accessible through a single library. All codes are also readily compatible with the native libraries.

Review Paper. Studied existing tech related to image and music generations, majority being text-conditioned.

Overview Paper. Developed POCs for Diffusion based Music-conditioned Image Generative AI and Transformer based Image-conditioned Music Generative AI.

Applied PageRank and HITS for analysing key driver in the evolution network.

Analysed the community structure of the network with modularity.

Explored the effect of different RL algorithms in competitive vs. cooperative MARL settings.

Algorithms: Advantaged Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimisation (PPO)