Machine Learning for Scientific Discovery

Scientific research is an iterative process alternating between a set of laws about the natural world (domain knowledge), and a set of measurements of the phenomenon (data). One key part of the process is the creativity of the research scientist. Instead of aiming at replacing the scientist, we aim to augment the abilities of a scientist by using machine learning to improve both: extracting knowledge from data, and using domain knowledge to generate better data. The current scientific method (that goes back 400 years to Francis Bacon) is likely to change in the face of increasing automation, and we will take a peek at three technical results illustrating the three parts of machine learning for scientific discovery. This talk invites you to imagine what it means to embed computing into the scientific process.


Cheng Soon Ong is a principal research scientist at the Machine Learning Research Group, Data61, CSIRO. He is also an adjunct associate professor at the Australian National University.

His PhD in Computer Science was completed at the Australian National University in 2005. He was a postdoc at the Max Planck Institute of Biological Cybernetics and the Fredrich Miescher Laboratory in Tübingen, Germany. From 2008 to 2011, he was a lecturer in the Department of Computer Science at ETH Zurich, and in 2012 and 2013 he worked in the Diagnostic Genomics Team at NICTA in Melbourne. Since 2014, Cheng Soon is researching ways to enable scientific discovery by extending statistical machine learning methods with the Machine Learning Research Group in Data61, CSIRO in Canberra. Prior to his PhD, he researched and built search engine and Bahasa Malaysia technologies at Mimos Berhad, Malaysia. He obtained his B.E. (Information Systems) and B.Sc. (Computer Science) from the University of Sydney, Australia.

Date & time

11am–12pm 17 Jun 2019



Cheng Soon Ong

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