Professor Dacheng Tao has made significant contributions in data mining, computer vision, and machine learning over the years. His research has focused on learning succinct, robust, and effective representations for data sampled from high dimensional or high order spaces, and collected from multiple tasks or sources. He has contributed insightful new ways to explaining why, when and how a learning model performs well, and has developed useable algorithms for practical applications, such as recommender systems, web image search, and visual information mining.
His research results have expounded in one monograph and 500+ publications at leading journals and conferences, with several best paper awards, such as the Best Theory/Algorithm Paper Runner-Up Award at IEEE ICDM 2007, the ICDM 2013 Best Student Paper Award, the 2014 ICDM 10-Year Highest-Impact Paper Award, and the 2018 IJCAI Distinguished Paper Award. He has been a Highly-Cited Researcher in Engineering since 2014 and in Computer Science since 2015. He received the 2015 Australian Scopus-Eureka Prize. He is a Fellow of the Australian Academy of Science, the IEEE, and the AAAS.
In addition, he received the prestigious Australian Research Council Laureate Fellowship in 2017. He is the inaugural Director of the UBTECH Sydney Artificial Intelligence Centre.
2018 IEEE ICDM Nomination and Evaluation Committees