Dr Roman Marchant Matus

PhD Sydney (2016)
Lecturer
Data Scientist
Centre for Translational Data Science
Faculty of Engineering and Information Technologies

J12 - The School of Information Technologies
The University of Sydney

Telephone +61 2 8627 4344

Website Centre for Translational Data Science

Curriculum vitae Curriculum vitae

Biographical details

Dr Marchant completed a PhD at the School of Information Technologies, University of Sydney in 2015. His current research at the Centre for Translational Data Science explores applying data science to the social sciences, currently focusing on predicting crime and understanding criminal behaviour. His area of expertise is Sequential Bayesian Optimisation (SBO), which is a novel probabilistic method for finding the optimal sequence of decisions that maximise a long-term reward. Although SBO has been readily applied to robotics and environmental monitoring, it can be applied to any optimisation problem.

Dr Marchant has considerable experience in machine learning and data science. Since 2011 he has participated as an active researcher at National ICT Australia (NICTA). Here, he actively contributed in the development of several projects that engage with industry and government agencies:

  • Environmental Monitoring for EPA: A platform that continuously learns patterns over space and time for predicting air pollution in the hunter valley region.
  • Network Optimisation for the Department of Environment, Water and Natural Resources of South Australia: Optimise a network of sensors that monitor groundwater reservoirs.
  • Anomaly Detection for Ecotech: Autonomous system that detects anomalies in environmental sensors.

His PhD thesis, entitled 'Bayesian Optimisation for Planning in Dynamic Environments', proposes the use of a probabilistic framework for finding the optimal sequence of decisions to monitor a time changing phenomena in an efficient manner. During his PhD, he published in top machine learning, robotics and artificial intelligence conferences, including:

  • Roman Marchant, Fabio Ramos, and Scott Sanner. Sequential Bayesian Optimisation for Spatial-Temporal Monitoring. In Conference on Uncertainty in Artificial Intelligence (UAI), 2014.
  • Roman Marchant and Fabio Ramos. Bayesian Optimisation for Informative Continuous Path Planning. In IEEE International Conference on Robotics and Automation (ICRA), 2014.
  • Jefferson Souza, Roman Marchant, Lionel Ott, Denis F. Wolf, and Fabio Ramos. Bayesian Optimisation for Active Perception and Smoot Navigation. In IEEE International Conference on Robotics and Automation (ICRA), 2014.
  • Roman Marchant and Fabio Ramos. Bayesian Optimisation for Intelligent Environmental Monitoring. In IEEE Conference on Intelligent Robots and Systems (IROS), 2012.

Throughout his career, Dr Marchant has received several awards, which include Best Student Presentation at the University of Sydney Student Conference 2013 and 2014 and the Google Publication Prize 2013. He was selected by the Chilean Government for a full PhD scholarship in 2011 and then received a Top-up scholarship from NICTA in 2012.

Research interests

There are hidden patterns in large datasets in every domain. Using machine learning algorithms, Dr Roman Marchant is uncovering previously unknown patterns that will help us to understand the key drivers and dynamics of crime, with the ultimate goal of effectively allocating resources for the prevention of criminal behaviour.

"The goal of my research is to extract useful information from large quantities of data. My current focus is on criminology, where statistical models can help us to understand criminal behaviour and determine the key drivers and dynamics of crime. In particular, I am interested in achieving a future reduction in crime arising from data-driven and informed policy decisions.

"Understanding crime as a spatial-temporal phenomenon can help us to determine what are the more dangerous locations at specific times of day. More importantly, it can help us to identify the key drivers of crime. Policy makers will then be able to make more informed decisions, and thus achieve an overall reduction in crime.

"For example, by examining datasets relating to domestic violence assaults, authorities can target specific problems in society that are proven launch pads to domestic violence. Through such direct treatment of specific problems, the number of people affected by domestic violence will diminish.

"Some of the research questions I hope to answer include: What are the key drivers of crime? What types of crime will be predominant in the future? What are the drivers that affect young children and their early involvement with crime? How do life events and other important factors influence the criminality levels of people over time?

"Ultimately I expect to provide evidence that supports the efficient expenditure of resources towards crime reduction."

Teaching and supervision

COMP5318 - Machine Learning and Data Mining

Selected grants

2017

  • Review of the Juvenile Justice NSW Objective Detainee Classification System; Clancey G, Marchant Matus R; NSW Dept of Juvenile Justice/Prequalification Scheme: Performance and Management Services.

Selected publications

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Journals

  • Moitinho-Silva, L., Steinert, G., Nielsen, S., Hardoim, C., Wu, Y., McCormack, G., Lopez-Legentil, S., Marchant Matus, R., Webster, N., Thomas, T., et al (2017). Predicting the HMA-LMA status in marine sponges by machine learning. Frontiers in Microbiology, 8(MAY), 1-14. [More Information]

Conferences

  • Morere, P., Marchant Matus, R., Ramos, F. (2017). Sequential Bayesian Optimisation for POMDP and Environment Monitoring with UAVs. 2017 IEEE International Conference on Robotics and Automation (ICRA 2017), Piscataway: (IEEE) Institute of Electrical and Electronics Engineers. [More Information]
  • Souza, J., Marchant Matus, R., Ott, L., Wolf, D., Ramos, F. (2014). Bayesian optimisation for active perception and smooth navigation. 2014 IEEE International Conference on Robotics and Automation (ICRA), Piscataway: IEEE. [More Information]
  • Marchant Matus, R., Ramos, F. (2014). Bayesian Optimisation for Informative Continuous Path Planning. 2014 IEEE International Conference on Robotics and Automation (ICRA), Piscataway: IEEE. [More Information]
  • Marchant Matus, R., Ramos, F., Sanner, S. (2014). Sequential Bayesian Optimisation for Spatial-Temporal Monitoring. 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014), Corvallis, Oregon, United States: AUAI Press.
  • Marchant Matus, R., Ramos, F. (2012). Bayesian Optimisation for Intelligent Environmental Monitoring. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, Piscataway, NJ: (IEEE) Institute of Electrical and Electronics Engineers. [More Information]

2017

  • Moitinho-Silva, L., Steinert, G., Nielsen, S., Hardoim, C., Wu, Y., McCormack, G., Lopez-Legentil, S., Marchant Matus, R., Webster, N., Thomas, T., et al (2017). Predicting the HMA-LMA status in marine sponges by machine learning. Frontiers in Microbiology, 8(MAY), 1-14. [More Information]
  • Morere, P., Marchant Matus, R., Ramos, F. (2017). Sequential Bayesian Optimisation for POMDP and Environment Monitoring with UAVs. 2017 IEEE International Conference on Robotics and Automation (ICRA 2017), Piscataway: (IEEE) Institute of Electrical and Electronics Engineers. [More Information]

2014

  • Souza, J., Marchant Matus, R., Ott, L., Wolf, D., Ramos, F. (2014). Bayesian optimisation for active perception and smooth navigation. 2014 IEEE International Conference on Robotics and Automation (ICRA), Piscataway: IEEE. [More Information]
  • Marchant Matus, R., Ramos, F. (2014). Bayesian Optimisation for Informative Continuous Path Planning. 2014 IEEE International Conference on Robotics and Automation (ICRA), Piscataway: IEEE. [More Information]
  • Marchant Matus, R., Ramos, F., Sanner, S. (2014). Sequential Bayesian Optimisation for Spatial-Temporal Monitoring. 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014), Corvallis, Oregon, United States: AUAI Press.

2012

  • Marchant Matus, R., Ramos, F. (2012). Bayesian Optimisation for Intelligent Environmental Monitoring. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, Piscataway, NJ: (IEEE) Institute of Electrical and Electronics Engineers. [More Information]

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