Lilia Iakoucheva is associate professor of psychiatry at the University of California, San Diego.
Lilia Iakoucheva
Associate professor
University of California, San Diego
From this contributor
The future of autism therapies: A conversation with Lilia Iakoucheva and Derek Hong
If a therapy for autism’s core traits makes it to market, it will likely take one of three forms, the researchers say.
The future of autism therapies: A conversation with Lilia Iakoucheva and Derek Hong
By
Peter Hess
14 April 2023 | 5 min read
Tangled web of proteins holds clues to autism’s complexity
Understanding how mutations in genes linked to autism perturb the different versions of proteins the genes form could reveal new targets for treatments.
Tangled web of proteins holds clues to autism’s complexity
By
Lilia Iakoucheva
28 February 2017 | 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.
By
Jill Adams
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.
By
Alona Fyshe
19 May 2025 | 7 min read