Dr Ming Liu

STAFF PROFILE

Position

Lecturer, Machine Learning

Faculty

Faculty of Sci Eng & Built Env

Department

School of Info Technology

Campus

Melbourne Burwood Campus

Qualifications

Doctor of Philosophy, Monash University, 2019

Biography

Dr Ming Liu is an early career researcher who works on Natural Langauge Processing and Machine Learning. He proposed the "Learn to actively learn" approahch for active learning and developed a few text summarization models/pipelines (e.g. SummPip, SciSummPip), both of which are widely used in low-resource text generation settings. His reseach has attracted multiple grants, including 2022 Deakin MiniARC, 2023 ARC Linkage (LP220200746). Dr Ming has interest in solving real world text mining problems, paticularly in domain specific settings. 

Read more on Ming's profile

Research interests

  • Natural Langauge Processing
  • Small Efficient Language Modelling
  • Continual Learning
  • Text Generation 
  • Adversarial Learning
  • Scientific Text Mining
  • Multimodality 
  • Conversational Systems

Teaching interests

  • Machine Learning
  • Natural Language Processing
  • Deep Learning
  • Applied Data Analysis
  • Semi-structured Data Analasis
  • Data Wrangling

Publications

No publications found

Funded Projects at Deakin

Australian Competitive Grants

Building resilience in at-risk rural communities through improving Media Communication on Climate Change Policies

A/Prof Xiao Liu, Dr Hilya Mudrika Arini, Dr Ming Liu, Dr Fitri Trapsilawati, A/Prof Chathu Ranaweera, A/Prof Kevin Lee, A/Prof Hassan Vally, Dr Anna Klas, Dr Adam Cardilini, Dr Yun Mulyani, Dr Arif Nurwidyantoro, Dr Yunita Sari, Prof Catherine Bennett, Dr Justin Lawson, Dr Gabi Mocatta

KONEKSI Australia-Indonesia Research Collaboration Grants

  • 2023: $175,000

Industry and Other Funding

Large Language Models in Engineering.

Dr Shang Gao, Dr Ming Liu, Mr Xinzhe Li

Aurecon Australasia Pty Ltd

  • 2024: $2,250
  • 2023: $16,000

Supervisions

Associate Supervisor
2024

Stella Ho

Thesis entitled: Continual Learning for Low-Resource Natural Language Processing

Doctor of Philosophy (Information Technology), School of Information Technology