Machine learning and decision-making, considered essential parts in artificial intelligence, play an increasingly important role in providing solutions to complex problems in a broad range of domains. This includes but is not limited to image recognition, self-driving cars, virtual personal assistants, games and adaptive natural resource management.
Recently, the field has made significant advances through an interplay of theory, algorithms and applications. For example, mathematical and statistical theories play a multi-faceted role: powering the design, analysis and improvement of algorithms; providing insights on when and why algorithms work; constructing guidelines on how to effectively apply algorithms in applications from different disciplines. In addition, the emergence of challenging applications stimulates the development of more powerful algorithms and new theories.
This symposium calls for the participation of PhD students working on the theory, algorithms and applications in the field of machine learning and decision-making. It aims to bring together students from all related disciplines to present their research to a wide audience, as well as to connect, interact, and exchange ideas with other students. The symposium will include a series of invited talks, tutorials, student contributed talks, and a number of events to encourage discussion and collaboration between students.
Jun Ju (The University of Queensland)
Yeming Lei (The University of Queensland)
David Maine (The University of Queensland)
Symposium webpage: https://david-maine.github.io/symposium/
If you have any questions, please contact David Maine at firstname.lastname@example.org
This symposium is supported by MATRIX and AMSI through the MATRIX-AMSI PhD Student Research Collaboration Scheme: matrix-inst.org.au/phd-student-research-collaboration-scheme-guidelines/