SysGen Seminar – Phoebe Chen – 30th June, 2017
La Trobe University
Friday 30th June
Theatre 2 (Room 219), Level 2, 200 Berkeley Street, The University of Melbourne
Pattern Discovery for Biomedical and Genomics Applications
Solving modern biomedical problems, especially those involving genome data, requires advanced computational and analytical methods. The huge quantities of data and escalating demands of modern biomedical research increasingly require the sophistication and power of computational techniques for their pattern discovery. In this talk, I will demonstrate recent methodologies and data structures for gathering high-quality approximations and modelling of genomic information, and will use these innovations as the basis for developing methods to cluster and visualize biomedical data in pattern discovery.
Professor Phoebe Chen is Professor and Chair at the Department of Computer Science and Information Technology, La Trobe University, Melbourne Australia. She was Head of Department from Sep 2010 to April 2012. Prof Chen is the Chief Investigator of ARC Centre of Excellence in Bioinformatics. Phoebe received her BInfTech degree with First Class Honours and PhD from the University of Queensland. Professor Chen has been doing multi-discipline researches for more than 20 years. Her research focus is to find effective solutions for visualizing, integrating, analyzing and mining big data, complex structures and functions for scientific and biomedical applications. She has been working in many emerging areas such as bioinformatics, scientific visualization, pattern recognition, health informatics, multimedia and databases. She has published over 220 research papers. She is steering committee chair of Asia-Pacific Bioinformatics Conference (founder) and International conference on Multimedia Modelling. Current Research: Visual and Data Analysis in Disease Diagnosis, Medical Image Analysis with Pattern Recognition, Drug Design by Visualization and Machine Learning Approach, Mining Protein and Genome Regulatory Networks using Graphic Models, Knowledge Discovery for RNA Structure and Function, Deep Learning Approach and its Applications, Speech Therapy and Data Mining.
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