A picture of me.
Contact Details
Dr. Michelle Blom
Research Fellow, Melbourne Mining Integrator sub-program lead (Agile Mine Planning and Logistics)
School of Computing and Information Systems
University of Melbourne, Australia
Office: 6.14, Doug McDonnell Building (Bld 168), University of Melbourne, Parkville campus
Email: michelle DOT blom AT unimelb DOT edu DOT au
Research Interests

I lead a research program on Agile Mine Planning and Logistics at the University of Melbourne, within the Melbourne Mining Integrator at Melbourne School of Engineering. I completed my PhD in the School of Computing and Information Systems at the University of Melbourne in 2011. My research involves developing new algorithms and heuristics for planning and scheduling in a range of domains, including: the scheduling of production in large open-pit mining supply chains, across both short- and long-term horizons; and the transport of inventory across complex logistics networks subject to dynamic, and adversarial, disruption.

I collaborate with colleagues from the University of Melbourne and Monash University on the design and implementation of algorithms for computing exact margins of victory in Instant Runoff Vote (IRV), and Single Transferable Vote (STV) elections. These algorithms significantly outstrip the current state-of-the-art. Knowing the margin of any election is extremely valuable, allowing us to determine whether discrepancies in successive counts of a set of ballots (e.g., due to lost ballots) throw the overall result into doubt. This collaboration has resulted in statistical methods for efficiently auditing IRV elections, and we are currently working on extending this to STV. To demonstrate the need for rigorous statistical audits of our elections, our work has also considered how an intelligent adversary could manipulate our election outcomes via the use of corrupt ballot scanners or voting machines.

Alongside an interest in optimisation via mathematical modelling and mixed-integer programming, one of my current research interests is the use of machine learning techniques to learn 'hyper-heuristics' in a range of domains. Given a set of low level routines, procedures, tactics, or strategies, these hyper-heuristics determine when an autonomous agent should switch between one strategy to another given its changing environment or context.

Data Sets

Data set for Multi-Objective Short-Term Production Scheduling for Open-Pit Mines: A Hierarchical Decomposition-Based Algorithm (Download).

Data set for Towards Computing the Margin of Victory for STV Elections (Download).

Masters Projects

I have the following projects available to masters students. Please contact me if you are interested in one of these projects.

An Online Auditing Tool for Australian Elections

Publications, Technical Reports, Articles

2019

Michelle Blom, Peter J. Stuckey, and Vanessa J. Teague. Election Manipulation 100. Proceedings of the 4th Workshop on Advances in Secure Electronic Voting at Financial Cryptography and Data Security 2019 (VOTING-19). 2019. PDF

2018

(in press) Michelle Blom, Peter J. Stuckey, and Vanessa J. Teague. Towards Computing Margins of Victory in STV Elections. INFORMS Journal on Computing (to appear). 2018

(in press) Michelle Blom, Slava Shekh, Don Gossink, Tim Miller, Adrian Pearce. Inventory Routing for Defence: Moving Supplies in Adversarial and Partially Observable Environments. Journal of Defense Modeling and Simulation (to appear). 2018.

Michelle Blom, Peter Stuckey, Vanessa Teague. Ballot-polling Risk Limiting Audits for IRV Elections. EVOTE-ID 2018. Full version, Conference version.

Michelle Blom, Peter Stuckey, Vanessa Teague. Computing the Margin of Victory in Preferential Parliamentary Elections. EVOTE-ID 2018. Conference version. Winner of Best Paper Award in the Track on Security, Usability and Technical Issues, and the Track on Administrative, Legal, Political and Social Issues.

(in press) Michelle Blom, Adrian Pearce, Peter Stuckey. Short-Term Planning for Open Pit Mines: A Review. Accepted for publication in the International Journal of Mining, Reclamation and Environment, 2018. Preprint, Link

(in press) Michelle Blom, Adrian Pearce, Peter Stuckey. Multi-Objective Short-Term Production Scheduling for Open-Pit Mines: A Hierarchical Decomposition-Based Algorithm. Accepted for publication in Engineering Optimization, To Appear, 2018. Online (Email me to request a copy)

2017

Michelle Blom, Adrian Pearce, Peter Stuckey. Short-Term Scheduling of an Open-Pit Mine with Multiple Objectives. Engineering Optimization, 49(5):777-795, 2017 (available online, or email me to request a copy).

Andrew Conway, Michelle Blom, Rajeev Gore, Katya Lebedeva, Lee Naish and Vanessa Teague. An analysis of New South Wales electronic vote counting. ACSW 2017. (ArXiv version).

Slava Shekh, Michelle Blom. Decentralised Decision Making in Defence Logistics. Cognitive Partnerships Workshop at IJCAI 2017. PDF.

Michelle Blom, Peter J. Stuckey, and Vanessa Teague. Computing the Margin of Victory in Preferential Parliamentary Elections. ArXiv version. Later version accepted at EVOTE-ID 2018 (PDF).

Michelle Blom, Peter J. Stuckey, and Vanessa Teague. Towards Computing Victory Margins in STV Elections. ArXiv version. Later version published in INFORMS Journal on Computing.

2016

Michelle Blom, Adrian Pearce, and Peter Stuckey. A Decomposition-Based Algorithm for the Scheduling of Open-Pit Networks over Multiple Time Periods. Management Science, 62(10):3059-3084, 2016. Preprint

Michelle Blom, Peter Stuckey, Vanessa Teague, Ron Tidhar. Efficient Computation of Exact IRV Margins. ECAI, 2016. PDF

Berj Chilingirian, Zara Perumal, Ronald L. Rivest, Grahame Bowland, Andrew Conway, Philip B. Stark, Michelle Blom, Chris Culnane, and Vanessa Teague. Auditing Australian Senate Ballots. ArXiv article. Link (accompanying article in The Register).

Pursuit article Paper audits crucial for automated counting by Vanessa Teague with Michelle Blom, Andrew Conway, Chris Culnane, Rajeev Goré, Robert Merkel, Ron Rivest, Philip Stark, and Damjan Vukcevic. 2016. Link

Andrew Conway, Michelle Blom, Rajeev Goré, Ekaterina Ledbedeva, Lee Naish, Vanessa Teague. Software can affect election results. ElectionWatch article, Technical Report, NSWEC media release

Pre 2015

Michelle Blom, Adrian Pearce, Peter Stuckey. Short Term Scheduling in Open-Pit Mines with Multiple Objectives. Poster at CP-15. PDF

Michelle Blom, Christina Burt, Adrian Pearce, and Peter Stuckey. A Decomposition-Based Heuristic for Collaborative Scheduling in a Network of Open-Pit Mines. Informs Journal on Computing, 26(4):658-676, 2014. PDF Supplementary

Michelle Blom. Arguments and Actions: Decoupling Preference and Planning through Argumentation. PhD Thesis, University of Melbourne, 2011. PDF

Michelle Blom, and Adrian Pearce. Relaxed Regression for a Heuristic GOLOG. STAIRS, 2010. PDF

Michelle Blom and Adrian Pearce. An Argumentation-Based Interpreter for Golog Programs. IJCAI, 2009. PDF

Michelle Blom. An Argumentative Knowledge-Based Model Construction Approach for Bayesian Networks. EUMAS, pages 549-563, 2008. PDF

Michelle Blom. Optimising the Interpretation of Golog Programs with Argumentation. EUMAS, pages 597-611, 2008. PDF