Rate My Professor Lan Du

Dr. Lan Du is an Associate Professor in Data Science and AI in the Department of Data Science and AI, Faculty of Information Technology at Monash University. He earned his PhD in Computer Science from the Australian National University in 2012, with a thesis on Nonparametric Bayesian Methods for Structured Topic Models, supervised by Professor Wray Buntine and Dr. Huidong Jin. He also holds a Bachelor of Information Technology with First Class Honours from the Australian National University in 2007 and a Bachelor of Communication and Information Technology from Flinders University in 2006. Prior to joining Monash University as a Lecturer in Data Science in September 2015, he served as a Postdoctoral Research Fellow in the Department of Computing at Macquarie University from December 2011 to August 2015, working with Professor Mark Johnson on natural language processing and machine learning.

Dr. Du's research centers on machine learning and artificial intelligence, particularly in natural language processing, with key interests in active learning theories and applications, uncertainty estimation for NLP and computer vision on uni-modal or multimodal data, knowledge distillation in CV and NLP, and multi-view/modal learning. His work applies these techniques to cross-disciplinary challenges in public health, marketing, chemistry, and medicine. He is recognized as an Australian leading researcher in text analytics, focusing on learning and understanding semantics of free language texts. Notable grants include the ARC Discovery Project 2023 'Harnessing Business Insights from Unstructured Customer Data' (AUD 309,000), Google Natural Language Understanding awards 2013 (USD 225,000), and Monash Engineering-IT-Science ECR Interdisciplinary Research Seed Fund 2017 (AUD 9,000). He has received scholarships such as ANU-NICTA PhD Scholarship (2008-2011). Key publications encompass 'Improving Topic Models with Latent Feature Word Representations' (Transactions of the Association for Computational Linguistics, 2015), 'Differential Topic Models' (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015), 'AUC Maximization for Low-Resource Named Entity Recognition' (AAAI, 2023), 'Prototypes-Guided Memory Replay for Continual Learning' (IEEE Transactions on Neural Networks and Learning Systems, 2023), and 'Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture' (NeurIPS, 2022). With over 3,995 citations on Google Scholar, his contributions bridge advanced AI research and practical applications. At Monash, he directs Postgraduate Studies, chairs the graduate program committee, and serves as Chief Examiner for FIT5149 Applied Data Analysis and FIT5196 Data Wrangling. He is a Senior Member of IEEE.