Fernanda Andaló

Fernanda Andaló

Lead AI Engineer

The LEGO Group

Professional Summary

I work as a Lead AI Engineer at the LEGO Group, where I lead AI R&D for digital play experiences, from research to product deployment across several initiatives, including the latest LEGO® DUPLO® Train sets launched worldwide.

I am also the Co-founder and Chief Scientist at SciPet, where I have coordinated machine learning research on animal biometrics for social good. This research resulted in CrowdPet, an app designed to support protective actions for stray animal populations and assist in planning public animal welfare initiatives.

In addition, I collaborate as a researcher with the Institute of Computing at Unicamp, and served as an elected member of the IEEE Information Forensics and Security Technical Committee (IFS-TC) (2023–2025) and as Chair of the IEEE Women in Engineering (WIE) South Brazil Section (2016-2017).

My professional goal is to continue advancing AI methods and their real-world applications, contributing to technology that creates a positive impact on society.

Ph.D. Computer Science

2007-2012

Institute of Computing, Unicamp

Visiting Ph.D. Research Fellow

2010-2011

School of Engineering, Brown University

M.Sc. Computer Science

2005-2007

Institute of Computing, Unicamp

B.Sc. Computer Science

1999-2004

Computer Science Department, University of Brasília (UnB)

Interests

Computer Vision Machine Learning Biometrics Deep Learning Generative AI
Recent Publications

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Recent Courses & Talks

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[Podcast] Hipsters: Fora de Controle 60

[In Portuguese] IA na LEGO, Machine Learning com pets, regulação de IA na Califórnia – Hipsters: Fora de Controle #60

[Podcast] Let's Data Podcast 003 — Fernanda Andaló

[In Portuguese] Dentre os assuntos, conversamos sobre como é trabalhar na LEGO (na Dinamarca), quais projetos de Machine Learning que eu trabalhei ou estou trabalhando na empresa, …

[Talk] The Open Set problem in Machine Learning and a case study in image classification for Hidden Side

Deep learning introduced a dramatic improvement in methods for automated image recognition in Computer Vision. Despite this progress, there is an immense gap between the accuracy …