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Interpretable Machine Learning for Astrophysics, using Symbolic Regression and Graph Neural Networks

Speaker
Miles Cranmer (Princeton University)
Event date
Venue
online (only)
Event type
Seminars

In this talk I will argue two points. 1) Symbolic regression, a machine learning technique that fits data by iteratively searching the space of all possible analytic equations, should be a standard machine learning algorithm in astrophysics. 2) Symbolic regression can be extended to high-dimensional spaces, such as to models for N-body simulations, using the method we have developed.

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APS Outstanding Referee

Refereeing

C. Herdeiro was nominated a 2021 Outstanding Referee by the American Physical Society (APS). The Outstanding Referee program was instituted in 2008 to recognize scientists who have been exceptionally helpful in assessing manuscripts for publication in the APS journals. This highly selective program annually recognizes about 150 of the roughly 71,000 currently active referees.

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Worldwide study on influential scientists

Impact

A list of the impact of scientists' careers was revealed in late 2020 in a study coordinated by John Ioannidis, from Stanford University (USA).  It includes 34 scientists from the University of Aveiro (UA), including 2 from Gr@v.  Read the UA coverage here.

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CIDMA Meeting 2021

CIDMA meeting

The 2021 CIDMA meeting was held on January 21st 2021, online. Our group was represented by Felipe Freitas who discussed the applicability of machine learning techniques from particle physics and gravitational waves to medical applications. Watch here the talks of the 2021 CIDMA meeting.

Strong Gravity
High Energy Physics
Other

Merry Christmas and happy 2021

Christmas

The seasonal greetings postcard of the Portuguese Physics Society had a little help from Gr@v once again! Merry Christmas, happy holidays and happy new year of 2021!

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