Mengxin Li is a New York based illustrator and motion graphic designer originally from China. She graduated from Savannah College of Art and Design in 2017 with a MFA degree in Illustration. Mengxin enjoys creating conceptual illustration with a sense of humor, she also agrees that motion graphic techniques could bring out a lot of potential for visual storytelling.
Mengxin Li
Animator, illustrator
From this contributor
How autism’s definition has changed over time
Don’t judge this book by its decidedly dull cover: Across its pages, some of the most dramatic changes in the history of autism have played out. This short animation chronicles how a diagnostic manual has defined and redefined autism over the years.
How autism’s definition has changed over time
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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.