Du, Lan

Faculty of Information Technology

Room 270, Woodside Building - 20 Exhibition Walk

Monash University, Clayton, VIC 3800

Tel: +61 3 9905 9971

Email: given_name dot surname at Monash dot edu

Dr Lan Du is an Associate Professor at Monash University and is affiliated with the machine learning group in the Department of Data Science and AI. As a faculty member, he is responsible for teaching and research in the field of machine learning. Additionally, Dr Du served as the faculty's director of Postgraduate Studies, overseeing all the postgraduate programs offered by the Faculty of IT and chairing the faculty's graduate program committee.

Before joining Monash University, Dr. Du worked as a postdoctoral research fellow with Professor Mark Johnson at Macquarie University. During his time there, he conducted research in natural language processing and machine learning. Dr Du earned his PhD in computer science from the Australian National University, where he worked under the supervision of Professor Wray Buntine and Dr. Huidong Jin. His research focused on developing Bayesian nonparametric models for topic modelling.

His research interest lies in the joint area of machine/deep learning and natural language processing, which includes (but is not limited to)

  • Active learning: both theories and applications
  • Uncertainty Estimation for NLP and CV on either uni-modal or multimodal data
  • Knowledge distillation in CV and NLP
  • Multi-view/modal learning

Dr Du has a strong interest in cross-disciplinary research, which involves applying machine learning and artificial intelligence techniques to solve real-world problems in different disciplines. He believes that the key to unlocking the full potential of AI lies in bridging the gap between advanced AI research and its practical application.

He has collaborated with researchers in fields including Medicine, Chemistry and Marketing, to develop AI solutions to address complex problems in these areas. His research focuses on developing innovative machine learning algorithms and models that can analyze and make sense of complex data sets to improve decision-making, predict outcomes, and drive innovation in various fields. By working across disciplines, he hopes to create new opportunities for applying AI and machine learning in practical, real-world settings, and to help ensure that these technologies are used in a responsible, ethical, and beneficial way.

Information for future PhD candidates

As an associate professor in machine learning, Dr Du is always looking for talented and motivated PhD students, who are passionate about pursuing research in these areas. His research group focuses on developing innovative algorithms and models for analyzing complex data sets, particularly on ML and its applications. Students who join Dr. Du's research group can expect to work on cutting-edge research projects in machine learning, using state-of-the-art techniques and tools to develop novel solutions to real-world problems.

To succeed in Dr Du's research group, students should be self-motivated, creative, and dedicated to advancing the state of the art in machine learning. They should also be able to work independently and collaboratively and have a strong background in computer science, mathematics, or a related field.

The PhD candidates can be found via (click the hyperlink for more information)

If you are interested in pursuing your PhD at Monash, and if you think you are competitive enough (E.g., the WAM of your master's study is equivalent to H1 honour, and you have gained some research experience) to apply for the scholarship, then

  • Read how to apply, and review your own eligibility
  • If you are eligible, send me your CV, both the bachelor's and the master's transcripts, and a list of publications if any.
Besides, all the candidates should be proficient in programming, particularly Python. More information can be found here.

Recent Research highlights

  • 2024
    • One paper is accepted by the ACM Multimedia 2024 (NEW!)
      • DiffTV: Identity-Preserved Thermal-to-Visible Face Translation via Feature Alignment and Dual-Stage Conditions
  • 2023
    • One paper is accepted by the IEEE Transactions on Multimedia
      • Reconstructed Graph Constrained Auto-Encoders for Multi-View Representation Learning
    • One paper is accepted by International Journal of Computer Vision
      • Multi-Target Knowledge Distillation via Student Self-Reflection
    • One paper is accepted by the IEEE Transactions on Multimedia
      • Hierarchical Locality-aware Deep Dictionary Learning for Classification
    • One paper is accepted by the IEEE Transactions on Neural Networks and Learning Systems
      • Prototypes-Guided Memory Replay for Continual Learning
  • 2022
    • ARC Discovery Project 2023
      • "Harnessing Business Insights from Unstructured Customer Data"
    • One paper is accepted by AAAI 2023
      • "AUC Maximization for Low-Resource Named Entity Recognition"
    • One paper is accepted by Neural Networks
      • "Discriminative and Geometry-Preserving Adaptive Graph Embedding for Dimensionality Reduction"
    • Two paper are accecpted by EMNLP 2022
      • "Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables"
      • "Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios"
    • One paper is accecpted by NeurIPS 2022
      • "Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture"
    • One paper is accecpted by IEEE Transactions on Neural Networks and Learning Systems (NEW!)
      • "Collaborative Knowledge Distillation via Multi-Knowledge Transfer"
    • One Paper is accepted by CIKM 2022
      • "Semi-supervised Continual Learning with Meta Self-training"
    • One Paper is accepted by European Journal of Radiology
      • "Comparison of state-of-the-art machine and deep learning algorithms to classify proximal humeral fractures using radiology text"
    • One Paper is accepted by IEEE Transactions on Intelligent Transportation Systems
      • "Hierarchical Graph Augmented Deep Collaborative Dictionary Learning for Classification"
    • One paper is accecpted by Expert Systems With Applicationss
      • "A Representation Coefficient-Based K-Nearest Centroid Neighbor Classifier"
  • 2021
    • One paper is accecpted by Transactions on Asian and Low-Resource Language Information Processing
      • " Mulan: a Multiple Residual Article-Wise Attention Network for Legal judgment Prediction"
    • One paper is accecpted by International Journal of Machine Learning and Cybernetics
      • "Locality-Constrained Weighted Collaborative-Competitive Representation for Classification"
    • One paper is accecpted by International Journal of Distributed Sensor Networks
      • "Feature Fusion-Based Collaborative Learning for Knowledge Distillation"
    • One paper is accepted by NeurIPS 2021
      • "Diversity Enhanced Active Learning with Strictly Proper Scoring Rules"
    • Four papers were acceptd by EMNLP 2021
      • "Neural Attention-Aware Hierarchical Topic Model", Main Conference
      • "Multilingual Neural Machine Translation: Can Linguistic Hierarchies Help?", Findings
      • "Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence", Findings
      • "Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence", Findings
    • One Survey paper on neural topic modelling was accepted by IJCAI 2021 survey track
      • "Topic Modelling Meets Deep Neural Networks: A Survey"
    • One paper was accepted by International Journal of Intelligent Systems
    • One paper was accepted by PAKDD 2021
    • One paper was accepted by TKDD
  • 2020
    • Recent success in two large government funds:
      • Medical Research Future Fund (MRFF): 1.9M
      • National Health and Medical Research Councile (NHMRF) idea grant: 636K
    • One paper was accepted by Data Mining and Knowledge Discovery
    • AAAI 2021 senior PC
    • One paper was accepted by EMNLP 2020
    • One paper was accepted by SIGIR 2020
    • One paper was accepted by AISTATS 2020

Grants and Awards

  • Harnessing Business Insights from Unstructured Customer Data
    • Type: ARC DP, Government
    • Funding amount: AUD 309K
  • Predicting fracture outcomes from clinical Registry data using Artificial Intelligence Supplemented models for Evidence-informed treatment (PRAISE) study
    • Type: NHMRC, Government
    • Funding amount: AUD 636K
  • Towards a National Data Management Platform and Learning Health System
    • Type: MRFF, Government
    • Funding amount: AUD 1.9M
  • The who, why, what, where and when of primary youth mental health care: The 5W research program
    • Type: NHMRC, Government
    • Funding amount: AUD 857K
  • Plant & Equipment Utilisation Review Project
    • Type: industry, Broadspectrum
    • Funding amount: AUD 130K
  • Leveraging electronic medical records and routine administrative data towards a population approach for monitoring dementia frequency, risk factors and management
    • Type: NHMRC, Government
    • Funding amount: AUD 617K
  • 2013 Google Natural Language Understanding-focused awards
    • Type: industry, Google
    • Funding amount: USD 225K