Institut Català d'Oncologia.

Analysis of oncological data with Data Mining methods.

Coordinator: R. Gavaldà.
January 2016 - January 2017.

Hospital de Sant Pau.

Analysis of Electronic Health Records with Data Mining methods. 

Coordinator: R. Gavaldà.

December 2015 - December 2016

Onfan S.L.
Development of a recommender system
Coordinator: M. Arias
April 2014 - March 2016

Acuity Trading

Analysis of financial data

Participants: Argimiro Arratia

May 2015 - October 2015

Media reports:

• Acuity Trading blog
• Finance Magnates
• Leaprate
• Finance Feeds

Transport Simulation Systems

Application of machine learning techniques to traffic prediction in transportation networks

Participants: Ricard Gavaldà, Rafael Mena

April 2015 - Present

Gas Natural Fenosa.

Algorithms for anomaly detections.
Coordinator: Josep Carmona (ALBCOM group).

Participant: R. Gavaldà.
September 2013 - today

Urbiotica

Coordinator: R. Gavaldà
Algorithms for object detection in magnetic sensors for smart cities.

September 2013 - July 2015.

Datknosys S.L.

Collaboration in Social Network Analysis and related topics.
Coordinator: R. Gavaldà.

January 2012 - July 2013

Ericsson.

Research and development on Data Stream Mining.
Coordinator: R. Gavaldà
October 2010 - February 2013.

Code3 informatica.

Detection of anomalies in ATM behavior.
Coordinator: R. Gavaldà.

July 2010 - October 2010.

Consolidated Edison Company of New York (2005-2007)

Participants: Marta Arias (work done while M. A. was at the Center for Computational Learning Systems).

The project consisted in the development and implementation of a system capable of ranking feeders according to their likelihood of failure in real-time. The system used a series of machine learning techniques ranging from boosting to online learning, and required a significant amount of software engineering. For more information on the project, check out the following publications and this link:

• Real-time Ranking with Concept Drift Using Expert Advice. H. Becker, M. Arias. KDD 2007, Aug 2007.
• Predicting Electricity Distribution Feeder Failures Using Machine Learning Susceptibility Analysis. P. Gross, A. Boulanger, M. Arias, D. L. Waltz, P. M. Long, C. Lawson, R. Anderson, M. Koenig, M. Mastrocinque, W. Fairechio, J. A. Johnson, S. Lee, F. Doherty, A. Kressner. AAAI 2006, Jul 2006.

This project was partly supported by a research contract from Consolidated Edison Company of New York.

TISSAT (2005-2006)

Participants: Gemma Garriga, José L. Balcázar.

The project consisted in the developtment of an integrated system to process in real time a huge incoming stream of alerts produced by current intrusion detection systems. A key component of this system includes an unsupervised clustering algorithm that combines a temporal sliding window, entropy tests, and expert rules to track the on-the-fly evolution of alert groups. More information can be found at this link.

This project was partly supported by a PROFIT grant from the Ministerio de Ciencia e Innovación of Spain.