Emma Pierson
@emmapierson.bsky.social
Assistant professor of CS at UC Berkeley, core faculty in Computational Precision Health. Developing ML methods to study health and inequality. "On the whole, though, I take the side of amazement."
https://people.eecs.berkeley.edu/~emmapierson/
https://people.eecs.berkeley.edu/~emmapierson/
🚨 New postdoc position in our lab at Berkeley EECS! 🚨
(please reshare)
We seek applicants with experience in language modeling who are excited about high-impact applications in the health and social sciences!
More info in thread
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(please reshare)
We seek applicants with experience in language modeling who are excited about high-impact applications in the health and social sciences!
More info in thread
1/3
August 22, 2025 at 2:11 PM
🚨 New postdoc position in our lab at Berkeley EECS! 🚨
(please reshare)
We seek applicants with experience in language modeling who are excited about high-impact applications in the health and social sciences!
More info in thread
1/3
(please reshare)
We seek applicants with experience in language modeling who are excited about high-impact applications in the health and social sciences!
More info in thread
1/3
SF fog coming up to swallow us in time lapse.
July 7, 2025 at 3:19 AM
SF fog coming up to swallow us in time lapse.
Migration data is critical in the health, environmental, and social sciences.
We're releasing a new dataset, MIGRATE: annual flows between 47 billion pairs of US Census areas. MIGRATE is:
- 4600x more granular than existing public data
- highly correlated with external ground-truth data
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We're releasing a new dataset, MIGRATE: annual flows between 47 billion pairs of US Census areas. MIGRATE is:
- 4600x more granular than existing public data
- highly correlated with external ground-truth data
1/2
March 28, 2025 at 4:04 PM
Migration data is critical in the health, environmental, and social sciences.
We're releasing a new dataset, MIGRATE: annual flows between 47 billion pairs of US Census areas. MIGRATE is:
- 4600x more granular than existing public data
- highly correlated with external ground-truth data
1/2
We're releasing a new dataset, MIGRATE: annual flows between 47 billion pairs of US Census areas. MIGRATE is:
- 4600x more granular than existing public data
- highly correlated with external ground-truth data
1/2
We have a new method, HypotheSAEs, for identifying *interpretable text features that predict a target variable* (aka hypothesis generation).
What features of a headline predict engagement?
What features of a clinical note predict whether a patient will develop cancer?
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What features of a headline predict engagement?
What features of a clinical note predict whether a patient will develop cancer?
1/
March 18, 2025 at 6:26 PM
We have a new method, HypotheSAEs, for identifying *interpretable text features that predict a target variable* (aka hypothesis generation).
What features of a headline predict engagement?
What features of a clinical note predict whether a patient will develop cancer?
1/
What features of a headline predict engagement?
What features of a clinical note predict whether a patient will develop cancer?
1/
New piece in Nature: @leahpierson.bsky.social and I argue that philanthropic funders should shield science from cuts the Trump administration may make to climate science, infectious disease, etc.
Free access link: rdcu.be/d6aul
Longer version on my website: shorturl.at/muwts
Free access link: rdcu.be/d6aul
Longer version on my website: shorturl.at/muwts
January 14, 2025 at 6:49 PM
New piece in Nature: @leahpierson.bsky.social and I argue that philanthropic funders should shield science from cuts the Trump administration may make to climate science, infectious disease, etc.
Free access link: rdcu.be/d6aul
Longer version on my website: shorturl.at/muwts
Free access link: rdcu.be/d6aul
Longer version on my website: shorturl.at/muwts
Our article on using LLMs to promote health equity is out in New England Journal of Medicine AI!
85% of equity-related LLM papers focus on *harms*.
But also vital are the equity-related *opportunities* LLMs create: detecting bias, extracting structured data, and improving access to health info.
85% of equity-related LLM papers focus on *harms*.
But also vital are the equity-related *opportunities* LLMs create: detecting bias, extracting structured data, and improving access to health info.
January 13, 2025 at 5:51 PM
Our article on using LLMs to promote health equity is out in New England Journal of Medicine AI!
85% of equity-related LLM papers focus on *harms*.
But also vital are the equity-related *opportunities* LLMs create: detecting bias, extracting structured data, and improving access to health info.
85% of equity-related LLM papers focus on *harms*.
But also vital are the equity-related *opportunities* LLMs create: detecting bias, extracting structured data, and improving access to health info.
Oregon stars. Happy holidays to all!
December 25, 2024 at 4:09 AM
Oregon stars. Happy holidays to all!