Dr. Michelle Blom is a computational scientist and applied mathematician whose work brings together mathematical programming and algorithms to solve complex combinatorial optimisation problems across varied domains. Her career has focused on industry-based collaborative research across varying technology and research levels from fundamental reserach to deployed systems. Prior to her current role, she was a Senior Research Fellow in the School of Computing and Information Systems at the University of Melbourne.
Dr. Blom has led an interdisciplinary team of researchers across Australian and international universities for over a decade to develop post-election audit techniques capable of verifying the outcomes of preferential elections. Elections face emergent risks from increasing digitisation and cyber-security challenges. Election outcomes should be accompanied by evidence that they accurately reflect the will of the voters. Post-election audits provide that evidence.
A post-election risk-limiting audit (RLA) is an integration of algorithmic and statistical methods that limits the maximum probability that an audit fails to detect an incorrect reported outcome. Dr. Blom has developed the first available methods for conducting RLAs for preferential (ranked-choice) elections such as Instant Runoff Voting (IRV) and the Single Transferable Vote (STV). These election systems are used in Australia to elect candidates to the lower and upper houses of parliaments, and are increasingly being adopted in the United States.
Dr. Blom’s work on RLAs for preferential elections has been recognised by the IEEE, through which she received the prestigious IEEE Cybersecurity Award for Practice in 2023, the University of Melbourne's Faculty of Engineering and IT (FEIT), through which she was awarded the 2023 FEIT Excellence Award for Research, and the University of Melbourne, through which she was awarded the 2024 Excellence in Interdisplinary Research award.
Dr. Blom has over a decade of experience in solving complex short- to long-term production planning problems, predominantly in the resources sector. Her work focuses on the use of decomposition, local search, and mathematical programming to find near optimal solutions to problems that cannot be solved with commericially available off-the-shelf solvers.
Her interests extend to the extraction of strategic behavioural knowledge from machine-learnt policies, and its subsequent generalisation for application to new contexts.
Alongside her role at Monash University, Dr. Blom is a Software Engineer at Democracy Developers where she develops tools to foster greater civic engagement in Australia's democratic processes.