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Assistant Professor in Machine Learning Arno Solin nominated for Young Academy Finland

Young Academy Finland has selected Arno Solin as a member for the four-year period of 2021-2025.
Arno_Solin_School_of_Science_2019_photo_Matti_Ahlgren_Aalto_University-4.jpg

When ´ˇ°ů˛Ô´Ç S´Ç±ôľ±˛Ô received an email in June inviting him to join Young Academy Finland, it didn’t take long for him to answer yes.

“It’s a big honor”, Solin says. “There was no way I could have turned down the offer.” 

Young Academy Finland is a fairly new organization, established by the Finnish Academy of Sciences and Letters in 2017, with an endowment from the Emil Aaltonen Foundation. The main goal of Young Academy Finland is promoting the role of science in the society. Members are young academics from different disciplines who show great promise in their field.  

°ż´Ú tłó±đ values of Young Academy Solin particularly appreciates focus on multidisciplinary actions and promoting science in our society. Solin is known to actively take part in the public discussion and popularizing of AI and machine learning. 

Facilitating dialogue between different disciplines is important, because the scientific community often finds itself in filter bubbles, Solin argues. He has noticed this is the case not only between different disciplines, but even inside the field of Computer Science. 

“That’s just how we are. Scientists like to hang out with people who have similar research interests”, he laughs.  

Universities have an important role in trying to solve issues and making a difference in society. Solin believes that a multidisciplinary approach is key when solving societal issues.  

“When the scientific community takes a stand for something, the impact is much higher if we engage people from all different disciplines.” 

Solin is also interested in a program that The Young Academy is running, which brings academics to schools to talk about their research.  

This fall is exciting for Solin also for another reason. He is starting a new research project as an Academy Research Fellow to look at how machine learning models could become faster and more reliable in real-world applications.  

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