With an wide experience at the intersection of Monetization, Systems Infra, Data/AI and Privacy/Law, I solve some of hardest technical problems for Meta Monetization, developing new end-to-end solutions to enable data to be generated, ingested, processed and used in the most effective and scalable way for Meta's Monetization Models to power Meta's next billion-dollar Monetization products in a privacy-safe way, especially in an increasingly fragmented legal & regulatory landscape.

I worked as a research assistant at Stanford University Computer Science Department. My research topic was on graph mining, especially on how to develop and extract trend and key characteristics of different types of large-scale graphs.

Led a team of four researchers to develop a maritime-focused news search engine and news aggregator for Singapore Ministry of Defense. The developed system fused various data mining and information retrieval techniques such as near-duplicate detection, HTML content extraction, topic modeling on short texts, named entity recognition to deliver reliability-ranked, real-time and maritime-focused news articles.

Developed novel and efficient algorithms to query massive graph databases and a prototype system on Java to experiment the efficiency of the new algorithms on social networks and bio-chemical graphs with millions of nodes

Collected millions of socially tagged images from Flickr and developed algorithms to extract and leverage these tags to improve both accuracy and usability of image search.

Developed novel and highly efficient and effective algorithms to extract information from XML databases using keyword search, especially when XML database contains irregular and evolutionary data.