Ateneo machine learning lab opens doors to industry partners

The Ateneo Laboratory for Intelligent Visual Environments (ALIVE) is opening its research initiatives to industry partners and collaborators as it works to develop machine learning solutions for real-world applications.

The initiative was highlighted during the Second Ateneo Breakthroughs lecture held on 26 February at Escaler Hall, where computer scientist Dr. Patricia “Pai” Angela R. Abu presented a talk titled “Smarter Sight: Building Intelligent Visual Systems for Public Good.”

Abu, an associate professor and chair of the Ateneo de Manila University Department of Information Systems and Computer Science, leads the ALIVE laboratory in developing machine learning tools focused on computer vision and image processing.

According to Abu, interdisciplinary collaboration is essential in building reliable machine learning systems that can operate effectively outside controlled laboratory environments.

Machine learning systems often require large datasets, extensive labeling and repeated training before they can adapt to real-world variables such as lighting changes, weather conditions and shifting environments.

ALIVE has already developed several projects applying machine learning to healthcare and urban systems.

Among them are a dental imaging support tool and a deep-learning model designed to help detect bone metastasis. The laboratory has also developed V-PROBE (Vehicle and Pedestrian Real-Time Observation and Behavioral Evaluation), a system that monitors traffic flow, predicts parking availability and identifies congestion risks.

Abu said collaborations with industry partners will allow the laboratory to test its research in operational settings where factors such as privacy, data security, hardware limitations and real-time performance must be addressed.

Industry partnerships can also provide domain expertise, data pipelines and deployment environments that help transform experimental research into practical applications.

ALIVE said the collaboration push aims to expand the use of machine learning in areas such as healthcare, transportation and public services.