Dr
Alessandra
Mileo

Primary Department
Faculty of Engineering and Computing
Role
Assistant Professor
Work Area/Key Responsibilities
Computing
Dr. Alessandra Mileo
Phone number: 01 700
5936
Campus
Glasnevin Campus
Room Number
L2.45

Academic biography

Dr. Alessandra Mileo is currently a Lecturer in the School of Computing, a Funded Investigator at the INSIGHT Centre for Data Analytics, and a Funded Investigator at the I-Form Advanced Manufacturing Centre, Dublin City University. She was previously a Senior Research Fellow, Adjunct Lecturer and Unit Leader at the INSIGHT Centre for Data Analytics, National University of Ireland Galway.  She holds an MSC and a PhD in Computer Science from the University of Milan, Italy.
Before Joining the Digital Enterprise Research Institute (DERI, NUIG) in 2010, she was a Post-doctoral Researcher at the Nomadis Lab, Dept. of Informatics, Systems and Communication of the University of Milano-bicocca, conducting interdisciplinary research activities involving Ambient Intelligence and Knowledge-driven Sensor Fusion, which converged in the succesful EU project proposal EasyReach, AAL, 2009-2-17, as well as the establishment of a spin-off company in 2009, now called Contexta. She continued her active contribution to successful EU proposals within DERI and later INSIGHT, acquiring a competitive EU project in the Smart Cities realm (CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications) and she has been Principal Investigator for the primary industry collaboration within the Research Centre portfolio (Enabling the Internet of Everything: a Linked Data infrastructure for networking, managing and analyzing streaming information and follow up industry-funded projects).
In the last 5 years, Dr. Mileo has developed a research programme in Knowledge Representation and Stream Reasoning, leveraging Semantic Technologies, expressive reasoning and statistical relational learning to design new approaches for scalable stream reasoning.
Dr. Mileo has secured almost 1 million euros in funding including national (SFI, IRC), international (EU, NSF) and industry-funded projects, publishing 90+ papers often in high impact journals and conferences. Dr. Mileo is an active PC member of over 20 top-ranked conferences and high-impact journals in her areas of interests within Artificial Intelligence, including Stream Reasoning, Complex Systems, Logic Programming, Semantic Web, Probabilistic Rule Learning, Knowledge Discovery and Knowledge Representation. including the Journal of Web Semantics (JWS), IEEE Intelligent Systems (IEEE IS), Theory and Practice of Logic Programming (TPLP), and she is Area Editors of the Databases and Semantic Web track of the Association of Logic Programming Newsletter. Dr. Mileo has been involved as a Program Chair, Panelist and Doctoral Consortium Mentor and Chair in a number of conferences, workshops and tutorials. As a member of the W3C, she is involved in standardization activities such as the W3C Community Group on RDF Stream Processing. She is Steering Committee member of the Web Reasoning

Research interests

Current research focuses on Stream Reasoning and Representation Learning.
In the Stream Reasoning area, I have been mainly working on scalable and interoperable ways of transforming web data streams into actionable knowledge. Research areas around this velocity aspect of Big Data involve distributed and parallel inference as well as the design of hybrid methods for qualitative and quantitative reasoning.Ongoing collaborations are currently in place on applying research outcomes in real world applications that have a very high economical and social potential impact. These include health monitoring, smart cities, sustainable IT and smart enterprise.In the are of Representation Learning, I am interested in investigating new comprehensive approaches to data analysis that combine knowledge-driven and data-driven representations. Beyond the ability to crunch and learn from massive data, the new generation of intelligent machines will need to be able to explain their outcomes, debug errors and reduce data bias. Knowledge Representation, Rule Learning and Neural Networks can play a key role in designing machines that exhibit both cognitive and neural reasoning, but many open questions remain on how to most effectively combine these two capabilities. Along with these challenges, such new approaches will bridge the gap between two faces of AI (connectionist and symbolic) that are historically diverging.