Connie Kasari is professor of human development and psychology at the University of California, Los Angeles. She is the principal investigator for several multi-site research programs and a founding member of the university’s Center for Autism Research and Treatment.
Connie Kasari
From this contributor
How much behavioral therapy does an autistic child need?
People tend to believe that, regardless of the treatment, more is always better. But is it?

How much behavioral therapy does an autistic child need?
Learning when to treat repetitive behaviors in autism
Some restricted and repetitive behaviors may have hidden benefits for people with autism, so scientists should work to find a happy medium between acceptance and change.

Learning when to treat repetitive behaviors in autism
School’s in
School-based interventions are arguably the best way to reach the truly underserved, under-represented and under-resourced children with autism, says Connie Kasari.
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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.
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.
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.