Temo is a Data Scientist and an Urban Computing Engineer. Trained in the fields of Robotics, Machine Learning and Artificial Intelligence, his research interest relies in using methods derived from these disciplines to improve the liveability of cities in the context of Big Data in the Smart City.

Before joining the Future Cities Lab he collaborated with a digital start-up based in London seeking to empower users with their personal data and digital identities as being shared with the social networks. For this purpose, he developed a user’s identity verification protocol built with Bayesian methods, Differential Privacy and Machine Learning as his MSc dissertation project. He obtained his Masters degree in Computational Intelligence and Robotics from University of Sheffield, UK, and his bachelor degree in Mechatronics Engineering from Instituto Tecnológico y de Estudios Superiores de Monterrey, México. For more information about Temo and his work, please refer to his LinkedIn website.

Research  

Temo is part of the Engaging Mobility group where his research focuses on developing Big Data-informed Agent Simulations for Transport Planning (Big-Data MATSim). In the era of ubiquitous sensing and computing, urban sensors offer us partial observations of reality from different perspectives, the challenge lies in how to integrate the big data sources to construct a complete picture of reality and use this information to inform large-scale transportation models. Through Cross-Domain Data Fusion, Machine Learning, Probabilistic Inference, and other data mining and AI techniques, the aim is to uncover urban mobility patterns from urban sensors such as mobile phone traces and smart card transactions to plan for better transportation systems.

Awards

Bechtel Prize 2016, for academic performance in the MSc Computational Intelligence and Robotics, University of Sheffield

Publications