Luís Roque

Ph.D. Computer Engineering @ FEUP

Luis is currently doing a Ph.D. in Computer Engineering, focused on Probabilistic Machine Learning. He is passionate about technology, data, machine learning, and management. Luís co-founded HUUB and, for 6 years, was the CEO of the company. He raised 5M€ from VCs and corporate VCs (such as Maersk, the biggest logistics service provider in the world) and grew the company to more than 70 employees. HUUB is disrupting the Fashion Supply Chain of the future, using technology to optimize the flow of goods worldwide. The company was ranked as one of the fastest-growing tech companies in the EMEA region in 2019 (placed at 54). HUUB was acquired by Maersk in 2021. The continuous academic evolution is the backbone of Luis's path, with a Master of Science in Industrial Engineering and Management, a Postgraduate degree in Business Intelligence & Analytics, and a Web Development specialization.

Luís Roque

Ph.D. Computer Engineering @ FEUP

Luis is currently doing a Ph.D. in Computer Engineering, focused on Probabilistic Machine Learning. He is passionate about technology, data, machine learning, and management. Luís co-founded HUUB and, for 6 years, was the CEO of the company. He raised 5M€ from VCs and corporate VCs (such as Maersk, the biggest logistics service provider in the world) and grew the company to more than 70 employees. HUUB is disrupting the Fashion Supply Chain of the future, using technology to optimize the flow of goods worldwide. The company was ranked as one of the fastest-growing tech companies in the EMEA region in 2019 (placed at 54). HUUB was acquired by Maersk in 2021. The continuous academic evolution is the backbone of Luis's path, with a Master of Science in Industrial Engineering and Management, a Postgraduate degree in Business Intelligence & Analytics, and a Web Development specialization.

Talk: Machine Learning: the path from research to production

Dia 1
16h30
15 novembro

The usage of Machine learning by companies around the world has been increasing every year (globally, machine learning jobs are projected to be worth almost $31 billion by 2024). Yet, the process of research to productization of such models is still a challenge. First of all, there are significant differences between research and production environments. Examples of this separation are the main drivers of the work, how the data is presented, the computational requirements, and the evaluation metrics used to measure success. On top of that, an organization needs to fully understand the trade-offs between accuracy, cost, maintainability, and interpretability. Only then, the company is ready to infuse existing products with ML intelligence or to build ML-first products.