Dr. Freddy Lecue

Dr Freddy Lecue (PhD 2008, Habilitation 2015) is a principal scientist and research manager in large scale reasoning Systems in Accenture Technology Labs, Dublin - Ireland. He is also a research associate at INRIA, in WIMMICS, Sophia Antipolis - France.

His research area is at the frontier of intelligent / reasoning systems, and Internet of Things. In particular he is interested in: Cognitive Computing, Knowledge Representation and Reasoning, Internet of Everything, Large Scale Processing, Software Engineering, Service-Oriented Computing, Information Extraction and Integration, Recommendation System, Cloud and Mobile Computing.

Short bio

Before joining Accenture as a principal scientist and research manager in large scale reasoning system in Junuary 2016, he was a research scientist and lead investigator in large scale reasoning systems at IBM Research - Ireland. His research has received IBM internal recognition: IBM research division award in 2015 and IBM Technical Accomplishment award in 2014. His research received external recognition: best paper awards from ISWC (International Semantic Web Conference) in 2014, and ESWC (Extended Semantic Web Conference) in 2014, as well as semantic Web challenge awards from ISWC in 2013 and 2012.

Prior to joining IBM Research he was Research Fellow at The University of Manchester from 2008 to 2011 and Research Engineer at Orange Labs (formerly France Telecom R&D) from 2005 to 2008.

He received his Research Habilitation (HdR - Accreditation to supervise research) from the University of Nice (France) in 2015, and a PhD from École des Mines de Saint-Etienne (France) in 2008. His PhD thesis was sponsored by Orange Labs and was awarded by the French Association in Artificial Intelligence.

Recent Projects

Cognitive Driving

The Cognitive Driving project provides cognitive mobility enabling a new generation of vehicles to recommend (and justify) personalized routes based on an analysis and interpretation of (i) open data from real-time traffic and various IoT devices (e.g., weather station, car sensors), (ii) social data from tweets feeds, (iii) driver-related data such as her/his body information (e.g., anxiety) from wearables and also (iv) calendar data. The application will then suggest personalized routes that fit drivers' ability while ensuring safer and secure traffic for other vehicles in the city.

Michael Barry, Randy Cogill, Rodrigo Ordóñez, Joe Naoum-Sawaya, Mark Purcell, Martin Stephenson


STAR-CITY (Semantic Traffic Analytics and Reasoning for CITY) is a system supporting semantic traffic analytic and reasoning for city. It fuses (human and machine-based) sensor data streams using variety of formats, velocities and volumes. The system provides insight on historical and real-time traffic conditions, supporting efficient urban planning. STAR-CITY demonstrates how the severity of road traffic congestion can be smoothly analyzed, diagnosed, explored and predicted using knowledge graph technologies. The system is being experimented in Dublin (Ireland), Bologna (Italy), Miami (USA), Rio (Brazil) across various engagements.

Simone Tallevi-Diotallevi, Jer Hayes, Robert Tucker, Veli Bicer, Marco Luca Sbodio, Pierpaolo Tommasi
Best In-Use paper award at ISWC (International Semantic Web Conference) in 2014
Best In-Use paper award at ESWC (Extended Semantic Web Conference) in 2014
Semantic Web challenge awards at ISWC (International Semantic Web Conference) in 2013
Smart traffic analytics in the semantic web with STAR-CITY: Scenarios, system and lessons learned in Dublin City. J. Web Sem. 27: 26-33 (2014)

Predictive Reasoning

The Predictive Reasoning project ingests, combines, and correlates a large volume of heterogenous real-time data (e.g., traffic data, city data such as events, road works, and weather related data) through a knowledge graph based model. Data mining, machine learning, knowledge representation and reasoning techniques are combined to obtain scalable and accurate prediction. The system outperforms state-of-the-art predictive analytics technologies by making sense out of context e.g., weather, city events, incidents and road works. One direct application has been traffic delay prediction in Dublin (Ireland), Bologna (Italy) and Rio (Brazil).

Jeff Z. Pan, Jiewen Wu
Predicting Knowledge in an Ontology Stream. IJCAI 2013.

Explanative Reasoning

The Explanative Reasoning project aims at understanding and explaining how decisions are captured through intelligent systems (e.g., mathematical models). This project does not focus on systems that give the right (optimal, cheapest, fastest) answer but to systems that can explain why and how it is the right answer. We target the general audience i.e., simple answers to complex questions. To this end we combine Artificial Intelligence techniques from statistics and logics-based inference models i.e., learning and reasoning. Real-work applications have been focusing towards the explanation of (i) traffic delay in Dublin (Ireland), Bologna (Italy) and Rio (Brazil), and (ii) flight delay and cancellation in major airline companies.

Randy Cogill, Simone Tallevi-Diotallevi, Jer Hayes, Marco Luca Sbodio, Pierpaolo Tommasi
Diagnosing Changes in An Ontology Stream: A DL Reasoning Approach. AAAI 2012.