Dr. Vincent Li

Dr. Vincent Li

Search and Machine Learning Scientist

Vinceli.org

Apeiros Lab

Biography

Dr. Vincent Li is an applied search scientist with a passion for solving complex business problems using search and artificial intelligence techniques. He earned his Ph.D. from RMIT University, where he conducted research on search, machine learning, and artificial intelligence techniques for search and ranking problems.

His research interests include search, machine learning, and artificial intelligence techniques, and he has a particular interest in applying state-of-the-art techniques, such as large language models like GPT, to solve business problems.

Dr. Li has extensive industry experience providing consultation services to companies on search and recommendation problems. He has helped numerous companies across Australia optimize their search capabilities and improve their search engine’s performance. One notable example of his work includes rebuilding the search engine of Australia’s largest supermarket, Coles, using AI techniques.

As the organizer of the Melbourne Search & Recommendation Meetup Group and the author of a tech blog site, productsearch.ai, Dr. Li is committed to advancing the field of search and AI. With his practical approach and extensive knowledge, he is a trusted and respected expert in the field, and a valued partner for companies seeking to optimize their search and recommendation capabilities.

Interests

  • Artificial Intelligence
  • Computational Linguistics
  • Information Retrieval
  • Recommender Systems
  • Ranking Problems

Education

  • PhD in Computer Science, 2016

    RMIT University

  • Master with Honor in Computing, 2014

    Australian National University

  • BSc in Software Engineering, 2007

    Soochow University

Skills

R

70%

Python

70%

Statistics

80%

Machine Learning

60%

Search

60%

Photography

20%

Experience

 
 
 
 
 

Senior Data Scientist

Canva

Mar 2022 – Jan 2023 Sydney

Responsibilities include:

  • Online and offline evaluation for search and recommendation systems
  • Apply state-of-art Machine Learning techniques to search relevance
  • Enhance data platform for search and recommendation data tracking and analysis
 
 
 
 
 

Search Science Consultant

Coles Ltd

Aug 2020 – Feb 2022 Melbourne

Responsibilities include:

  • Building AI-powered search engines for multiple purposes
  • Apply state-of-art Machine Learning and AI techniques to search and matching problems to improve business metrics and users’ satisfactions
  • Scientific approaches for search evaluation
 
 
 
 
 

Applied Search Scientist

SEEK Ltd

Oct 2017 – Apr 2020 Melbourne

Responsibilities include:

  • Search engine performance evaluation
  • Search engine performaance optimisation
  • Conduct research to improve our understanding of users and how to properly evaluate search engine
 
 
 
 
 

Researcher

Abbrev8

Oct 2016 – Jan 2017 Melbourne
Conduct research to understand the performance of a salient entity recognition component of the company

Recent Posts

Multi-stage ranking for relevance and user satisfaction for search

Ranking is a fundamental and critical component of modern search engines. The effectiveness of ranking is directly linked to the search relevancy and user satisfaction.

Why search is so hard

Search is one of the key areas where big companies have spent lots of investment on. Working as an applied search scientist, I was always questioned why we still spend so much time on a problem that has been solved by many platforms like Elasticsearch, Luence and many others? The truth is, unless you are familar with the area, you probably won’t realise how challenging the problem is.

Projects

Recent & Upcoming Talks

Recent Publications

Quickly discover relevant content by filtering publications.

Self-labeling methods for unsupervised transfer ranking

Merging algorithms for enterprise search

Popular Topics

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