Our research is focused on new theoretical advances in machine learning and data mining, motivated by important practical applications, on the basis that challenging applications foster novel algorithms which in turn enable new applications.
Specific research topics include:
- Artificial intelligence, data mining & machine learning
- Algorithms for classification and numeric prediction
- New methods for combining domain knowledge with data mining
- Time series data analysis
- Probability, reasoning under uncertainty, and Bayesian networks
- Reinforcement learning
- Practical applications of data mining and machine learning in science, engineering & medicine.
June 2017: New Research Opportunities
June 2017: Research positions in Data Mining, Machine Learning and Artificial Intelligence
Post References: DMML-ROC-17-MS/DMML-ROC-17-SE
Up to three new research positions are available in the Data Mining & Machine Learning Research Group, led by Dr Michael Madden in the National University of Ireland Galway, to contribute to a substantial European Union research project called ROCSAFE, which involves an exciting combination of aerial robotics, ground-based robotics, sensing systems, image analysis, and artificial intelligence.
There are positions available for a software engineer and for research scholars (MSc by research with possibility to transition to PhD). The software engineer will receive a salary commensurate with experience. The MSc students will receive a tax-free scholarship of €16,000 per year, with fees also paid. Additional funding is provided for research-related travel.
The research and software development work is likely to involve topics such as: (1) development of communications protocols and simulation of a realistic multi-agent system; (2) routing of autonomous vehicles for optimal collection of multi-resolution image and sensor data; (3) context-aware decision support driven by sensor data analytics.
This is an excellent opportunity to combine research with software development, working in a flexible and stimulating research environment, while collaborating with academic and industrial partners across Europe.
Candidates for the research scholarships must have a strong honours bachelor degree (equivalent to H1) in Computer Science, Software Engineering, Mathematics, or a similar discipline, and preferably a relevant MSc degree also. Candidates must have excellent mathematical ability, strong software development skills, great communication skills, and a strong interest in research. Preferably, candidates should demonstrate prior experience in data mining and machine learning.C
Candidates for the software engineer position must have a strong bachelor degree in computing or similar, as well as demonstrating relevant experience in software development in programming languages such as Java and/or C++, and excellent communication and interpersonal skills.Expected Start Date: Flexible between 1 July and 1 Sept 2017.
The positions will remain open until filled, but first consideration will be given to those received by 12 June 2017.
About the ROCSAFE Project: ROCSAFE (Remotely Operated CBRNe Scene Assessment & Forensic Examination) is funded by the European Union’s Horizon 2020 programme. Led by Dr Michael Madden in NUI Galway, it is making advances in autonomous robotics (ground-based and air-based), probabilistic reasoning, intelligent decision support, and miniaturised sensors, all of which will work together to gather forensic evidence in the event of a chemical, biological, radiation/nuclear or explosive (CBRNe) incident. ROCSAFE’s overall goal is to fundamentally change how CBRNe events are assessed, and ensure the safety of crime scene investigators, by reducing the need for them to enter dangerous scenes to gather evidence. There are 13 partners in total involved in the project across Ireland, Italy, Portugal, Spain and Germany, along with a further group of advisory board members. There is more information at http://www.nuigalway.ie/remoteforensics/.
Software for Bayesian Network Classification
Over several years, I have developed open-source machine learning software for inductively learning various forms of Bayesian networks from data, and for classification using BNs.
- Learn Naive Bayes, Tree-Augmented Naive Bayes (TAN), General Bayesian Networks, and BN-Augmented Naive Bayes structures
- Multiple scoring metrics, including the K2 metric, BDeu and CMI
- Visualisation of structures using GraphViz (DOT and DOTTY)
- Supplementary code to work with data from other formats, to perform cross-validation, to construct learning curves, and to construct ROC curves
For a description of the classifiers, and a discussion of their relative performance, see: “On the Classification Performance of TAN and General Bayesian Networks” , Michael G. Madden. Knowledge Based Systems, 2009.
Please cite that paper if you use this software in your work.