Mirko Uljarević is clinical assistant professor of psychiatry and behavioral sciences at Stanford University in California.
Mirko Uljarević
Senior Research Fellow
School of Psychological Sciences at the University of Melbourne in Victoria, Australia
From this contributor
Teasing apart insistence on sameness with Mirko Uljarević
The hallmark autism trait has multiple facets, Uljarević and his colleagues have found.
Teasing apart insistence on sameness with Mirko Uljarević
By
Lauren Schenkman
30 October 2023 | 6 min read
In search of ‘social’ subtypes of autism
Grouping autistic people based on their social abilities may reveal subtypes of the condition.
In search of ‘social’ subtypes of autism
By
Thomas Frazier, Mirko Uljarević
21 July 2020 | 5 min read
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Sharing Africa’s brain data: Q&A with Amadi Ihunwo
These data are “virtually mandatory” to advance neuroscience, says Ihunwo, a co-investigator of the Brain Research International Data Governance & Exchange (BRIDGE) initiative, which seeks to develop a global framework for sharing, using and protecting neuroscience data.
Sharing Africa’s brain data: Q&A with Amadi Ihunwo
These data are “virtually mandatory” to advance neuroscience, says Ihunwo, a co-investigator of the Brain Research International Data Governance & Exchange (BRIDGE) initiative, which seeks to develop a global framework for sharing, using and protecting neuroscience data.
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Lauren Schenkman
20 May 2025 | 6 min read
Cortical structures in infants linked to future language skills; and more
Here is a roundup of autism-related news and research spotted around the web for the week of 19 May.
Cortical structures in infants linked to future language skills; and more
Here is a roundup of autism-related news and research spotted around the web for the week of 19 May.
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20 May 2025 | 2 min read
The BabyLM Challenge: In search of more efficient learning algorithms, researchers look to infants
A competition that trains language models on relatively small datasets of words, closer in size to what a child hears up to age 13, seeks solutions to some of the major challenges of today’s large language models.
The BabyLM Challenge: In search of more efficient learning algorithms, researchers look to infants
A competition that trains language models on relatively small datasets of words, closer in size to what a child hears up to age 13, seeks solutions to some of the major challenges of today’s large language models.
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Alona Fyshe
19 May 2025 | 7 min read