• Collaborated with a team of researchers and doctors at a leading local hospital to enhance a transformer-based drug response prediction model for cancer treatment. • Utilised advanced biomedical knowledge graph embedding approaches to generate specialised, context-aware embeddings that deepened insights into drug-gene interactions. • Conducted comprehensive evaluations of various SOTA heterogeneous graph embedding techniques. Reduced graph noise and derived context-specific subgraphs through n-hop neighbourhood and meta-path filtering. Successfully improved embedding quality by 80% (Hits@10), while also achieving substantial efficiency gains.• Refactored a novel graph representation learning methodology inspired from subword tokenisation strategies commonly used in the NLP domain to overcome an intricate gene token mapping process.• Developed a RAG-based chatbot powered by GPT-3.5 Turbo to assist migrant workers with limited English proficiency. Leveraged APIs and libraries like LlamaParse, LangChain, Azure OpenAI, and Chroma (vector database). • Led and facilitated mentoring sessions on topics such as Deep Neural Networks and Computer Vision, as well as the core principles behind generative models like Stable Diffusion.

• Volunteered as an early education tutor, teaching English to migrant children in under-resourced areas with limited educational access, while communicating in Mandarin to bridge language barriers and enhance comprehension.• Designed and implemented lesson plans for small groups, overseeing lesson delivery, managing instructional flow, and providing support with students' existing school assignments.

• Utilised Microsoft Excel for managing and analysing logistics and shipment data, generating visual summaries and insights through advanced analytical techniques.