Empowering Biomedical Data Analysis with Multiple Heterogeneous Sources
Z. Alan Zhao
School of Computing, Informatics, and Decision Systems Engineering
Arizona State University
Abstract
The rapid advance of computer based high-throughput technology and the ubiquitous use of the Web have
provided unparalleled opportunities for human to expand capabilities in production, services, communications,
and research. In this process, immense quantities of high dimensional data are accumulated, challenging stateof-the-art machine learning techniques to efficiently produce useful results. Examples include data from documents, microarrays, proteomics, and brain images. Recent development in biomedical research has made
disparate data and knowledge sources available. Properly integrating information from multiple sources helps
address the curse of dimensionality, and increase the reliability and stability of learning outputs. This talk will
focus on addressing some new challenges arising in real-world applications with unique perspectives and novel
solutions, including multisource gene selection, heterogeneous data fusion via multiple kernel learning, and
their applications in acute lymphoblastic leukemia (ALL) study and Alzheimer's disease (AD) study.
Bio:
Z. Alan Zhao is a PhD candidate of Computer Science and Engineering at Arizona State University. He received
his Bachelor and Master degrees of Engineering from Harbin Institute of Technology (HIT). After his graduation
from HIT, he joined Alcatel Shanghai Bell Co., Ltd. as a software engineer. Alan won the Outstanding PhD Student award from the School of Computing and Informatics ASU in 2008, and received the Best-Paper Award
from BICoB-2010. His primary research interests lie in designing and developing novel dimension reduction and
data mining algorithms handling extremely high-dimensional data. His research has resulted in over 20 research papers published in the top conferences and journals, including one of the first semi-supervised feature
selection algorithm and the first multi-class probabilistic kernel discriminant analysis algorithm that can be
found in the literature. He served as a reviewer for over 10 journals and conferences. He co-chair for the
PAKDD Workshop on Feature Selection in Data Mining 2010, Hyderabad, India.