CARMA Research Group
Leader: Ali Eshragh
This research group aims to establish research projects in the broad area of Data Science spanning from data mining and statistical data analysis to machine learning and optimisation. The research activities in this group are spanning from theoretical projects to practical applications and industrial consultations. In particular, our members work in:
(i) Bayesian statistics,
(ii) Correspondence analysis,
(iii) Distribution theory,
(iv) Ecological statistics,
(v) Markov decision processes and reinforcement learning,
(vi) Optimisation for machine learning,
(vii) Prescriptive analytics: Data-driven decision making,
(viii) Statistical learning and data mining,
(ix) Systems and process improvement,
(x) Time series forecasting.
We are pleased to announce that we intend to organise ongoing yearly workshops titled "Data Science Down Under" with specific underlying theme for each year. The details of our inaugural year, 2019, are provided on the workshop website below:
Newcastle City Hall (Newcastle, NSW)
A special public event in conjunction with the Mathematics in Industry Workshop. Please register for free to attend this public lecture. Please arrive by 5:00 pm for 5:30 pm. PLEASE NOTE NEW VENUE: Hunter Room, Newcastle City Hall (290 King Street, Newcastle)."Mathematics in Industry: Optimisation in Action - Unlocking Value in the Mining, Energy, and Agriculture Industries"
Prof. Ryan Loxton
Optimisation is a branch of applied mathematics that focuses on using mathematical techniques to optimise complex systems. Real-world optimisation problems are typically enormous in scale, with hundreds of thousands of inter-related variables and constraints, multiple conflicting objectives, and numerous candidate solutions that can easily exceed the total number of atoms in the solar system, overwhelming even the fastest supercomputers. Mathematical optimisation has numerous applications in business and industry, but there is a big mismatch between the optimisation problems studied in academia (which tend to be highly structured problems) and those encountered in practice (which are non-standard, highly unstructured problems). This lecture gives a non-technical overview of the presenter's recent experiences in building optimisation models and practical algorithms in the oil and gas, mining, and agriculture sectors. Some of this practical work has led to academic journal articles, showing that the gap between industry and academia can be overcome.