Science and Technology

The President of the Republic presents the “Vencer o Adamastor” Award at Técnico

The “Vencer o Adamastor” Award ceremony took place on February 1, in the Great Hall of Instituto Superior Técnico. The award winner was the researcher Gonçalo Correia.

The first edition of the “Vencer o Adamastor” Award took place on February 1, at Técnico – Alameda campus (Great hall). This award is an initiative of the Instituto de Engenharia de Sistemas e Computadores (INESC) and the Público newspaper, which recognised the research work of Gonçalo Correia, Invited Professor at Técnico and researcher at the Instituto de Telecomunicações (IT), in the field of Artificial Intelligence (AI).

“We can only do Science by defeating every day adamastores (untamed titans) […] there is no better metaphor for what researchers, scientists, research centres and universities do”, said the President of Técnico, Professor Rogério Colaço.

The awarded work “Modelos neuronais mais transparentes e compactos usando a esparsidade” (“Making Neural Models more transparent and compact using sparsity”) aimed at “making neural models more transparent, more compact and more efficient”, explained the award winner. Gonçalo Correia was part of a “research team awarded with an ERC starting grant in 2017, led by Professor André Martins (winner of a second ERC consolidator grant, in 2023), who was also Gonçalo Correia’s PhD supervisor” recalled the President of INESC, Professor Arlindo Oliveira.

“I hope to have contributed to a more responsible AI, with a smaller carbon footprint and greater transparency”, shared the Técnico alumnus. “I intended to take steps towards neural networks 2.0: more efficient, more transparent, more compact”, he added.

The award was delivered by the President of the Republic, Professor Marcelo Rebelo de Sousa, who praised the “ecological awareness” of the award winner and stressed “urgent action is needed to tackle environmental challenges […], which is a cross-cutting issue”.

Gonçalo Correia finished his PhD in Electrical and Computer Engineering in 2022 with the thesis titled “Learnable Sparsity and Weak Supervision for Data-Efficient, Transparent, and Compact Neural Models”.

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