Large Scale Non-linear Classification and its Applications in Complex Biomedical Data
Dr. Liang Lan
Lenovo Machine Intelligence Lab
1430-1530, 12 Sep 2017
SCT909, Cha Chi Ming Science Tower, Ho Sin Hang Campus
Classification is a fundamental problem in machine learning, data mining and artificial intelligence. Non-linear classification is particularly important since non-linear concepts often occur in real-world complex problems. With data being generated at tremendous rate nowadays, there is a great need for building accurate, efficient and scalable non-linear classification models in many research areas, such as health informatics, environmental sciences, bioinformatics and graph mining. In this talk, I will present our recent work on scaling up kernel methods and kernel learning using low-rank approximation in big data classification. I will also talk about classification of complex biomedical data, which include disease mapping on mobile data and protein function prediction by integrating multiple data sources.
Dr. Liang Lan is currently an advisory researcher with Lenovo Machine Intelligence Lab, Hong Kong. He was a research scientist at Institute for Infocomm Research, Singapore from 2014 to 2016, and a researcher with Huawei Noah’s Ark Lab, Hong Kong from 2013 to 2014. He received his Ph.D. degree in Computer and Information Sciences from Temple University, Philadelphia, PA, USA in 2012 and his B.S. degree in bioinformatics from Huazhong University of Science and Technology, Wuhan, China in 2007. His research interests include large scale machine learning algorithms, in particular in support vector machines, kernel learning, deep learning and their applications in health informatics, environmental sciences, bioinformatics and graph mining. He is the author/coauthor of 15+ papers published in top venues, including AIJ, JMLR, TNNLS, ICML, AISTATS, and etc. He is the recipient of the Institution of Engineers Singapore (IES) Prestigious Engineering Achievement Award 2015 and 2016 and the ASEAN Outstanding Engineering Achievements Award 2016.