Dr Michelle Blom

  • Room: Level: 06 Room: 14
  • Building: Doug McDonell Building
  • Campus: Parkville

Research interests

  • AI Algorithms
  • Algorithms for Electoral Analysis
  • Optimisation for Supply Chains

Personal webpage

http://people.eng.unimelb.edu.au/michelleb/

Biography

Dr Michelle Blom is a Research Fellow in the School of Computing and Information Systems at The University of Melbourne. Michelle completed her PhD in the Department of Computer Science and Software Engineering at the University of Melbourne in 2011. Her thesis explored the use of argumentation-based approaches for automated decision-making and planning. Her 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. An additional area of interest is the development of algorithms to support the analysis and auditing of election outcomes.

Recent publications

  1. Blom M, Stuckey P, Teague V. Ballot-Polling Risk Limiting Audits for IRV Elections. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2018, Vol. 11143 LNCS. DOI: 10.1007/978-3-030-00419-4_2
  2. Blom M, Stuckey P, Teague V. Computing the Margin of Victory in Preferential Parliamentary Elections. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2018, Vol. 11143 LNCS. DOI: 10.1007/978-3-030-00419-4_1
  3. Blom M, Shekh S, Gossink D, Miller T, Pearce A. Inventory routing for defense: Moving supplies in adversarial and partially observable environments. Journal of Defense Modeling and Simulation. 2018. DOI: 10.1177/1548512918798056
  4. Blom M, Pearce A, Stuckey P. Multi-objective short-term production scheduling for open-pit mines: a hierarchical decomposition-based algorithm. ENGINEERING OPTIMIZATION. Taylor & Francis Ltd. 2018, Vol. 50, Issue 12. DOI: 10.1080/0305215X.2018.1429601
  5. Blom M, Pearce A, Stuckey P. Short-term planning for open pit mines: a review. International Journal of Mining, Reclamation and Environment. Taylor & Francis. 2018. DOI: 10.1080/17480930.2018.1448248
  6. Conway A, Blom M, Naish L, Teague V. An analysis of New South Wales electronic vote counting. ACM International Conference Proceeding Series. 2017, Vol. Part F126226. DOI: 10.1145/3014812.3014837
  7. Blom M, Pearce A, Stuckey P. Short-term scheduling of an open-pit mine with multiple objectives. ENGINEERING OPTIMIZATION. Taylor & Francis Ltd. 2017, Vol. 49, Issue 5. DOI: 10.1080/0305215X.2016.1218002
  8. Blom M, Pearce A, Stuckey P. A Decomposition-Based Algorithm for the Scheduling of Open-Pit Networks Over Multiple Time Periods. MANAGEMENT SCIENCE. Institute for Operations Research and the Management Sciences. 2016, Vol. 62, Issue 10. DOI: 10.1287/mnsc.2015.2284
  9. Blom M, Teague V, Stuckey P, Tidhar R. Efficient Computation of Exact IRV Margins. 22nd European Conference on Artificial Intelligence (ECAI). IOS Press. 2016, Vol. 285. Editors: Kaminka GA, Fox M, Bouquet P, Hullermeier E, Dignum V, Dignum F, Vanharmelen F. DOI: 10.3233/978-1-61499-672-9-480
  10. Blom M, Burt C, Pearce A, Stuckey P. A Decomposition-Based Heuristic for Collaborative Scheduling in a Network of Open-Pit Mines. INFORMS Journal on Computing. INFORMS Applied Probability Society. 2014, Vol. 26, Issue 4. DOI: 10.1287/ijoc.2013.0590
  11. Blom M, Pearce A. Relaxing regression for a heuristic GOLOG. Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the 5th Starting AI Researchers' Symposium. IOS Press. 2010.
  12. Blom M, Pearce A. An Argumentation-Based Interpreter for Golog Programs. 21st International Joint Conference on Artificial Intelligence (IJCAI-09). IJCAI Incorporated. 2009.