Assistant Professor @Stanford CS @StanfordNLP @StanfordAILab Part time @humansand LLMs for Humans
Joined December 2016
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Diyi Yang retweeted
announcing &tutor, a tutor we trained against persimmon!
there are many reasons I'm so excited about this. here are a few:
1. this is a case study into the differentiated bet that we're making at humans&: having good models of users (e.g. learners) allows us to train AI models that can help people over long time horizons (e.g. teach in a way that imparts new understanding)
2. education is a near and dear application close to my heart, and I've been wanting to work on something like this since starting my PhD
3. @314Bansal is the 🐐🐐🐐 and it was so much fun working together on this
Diyi Yang retweeted
If you are a prospective student looking for Ph.D. positions, I cannot stress this enough: Consider joining Zhang Lab to pioneer the next frontier! It would be a great, insightful, and fruitful journey!
PhDone!
Very grateful to all my committee members!
@Diyi_Yang @zsoltkira @kartik_goyal_ @PoloChau @sanmikoyejo
Congrats to Dr. Zhang @StevenyzZhang!!! Super proud of what you have done in the last few years! Excited about what you will do next ♥️
PhDone!
Very grateful to all my committee members!
@Diyi_Yang @zsoltkira @kartik_goyal_ @PoloChau @sanmikoyejo
Diyi Yang retweeted
So excited for this research! I've always been annoyed at the supremacy of verified rewards, and now we can finally show it.
In this work, we develop ways of measuring: how much of a reward signal is verifiable, how much can we put into words, and how much is shared but tacit, or impossible to articulate?
Diyi Yang retweeted
More to come! I've been thinking a lot about the future of work and the responsibility of the whole ecosystem to help answer these questions (and fast). It was an honor working with @jennjwang, @erikbryn, @Diyi_Yang, @andreas_h0wpt, and Phil.
Diyi Yang retweeted
O*NET tracks the tasks that are required by different jobs. It has been invaluable for research. But it also was clear that it could be improved a lot--and the need to improve it was much greater than ever. So very glad to see this, looks like we'll all be using it.
Diyi Yang retweeted
Such impactful work from @jennjwang and team!
Also very proud to see the AI Interviewer built by @DavidAnugraha (arxiv.org/abs/2602.21136) be used more in the wild. Aggregating worker experiences at scale is super important and needs to be handled with care. Excited to see this direction progress in the coming years!
Understanding AI’s impact on work starts with knowing what workers actually do.
Excited to share TaskNet 🥳 We use AI interviews to map tasks across 6 occupations, uncovering emerging tasks and overlooked work.
Amazing work by @jennjwang and the team!!!
Understanding AI’s impact on work starts with knowing what workers actually do.
Excited to share TaskNet 🥳 We use AI interviews to map tasks across 6 occupations, uncovering emerging tasks and overlooked work.
Amazing work by @jennjwang and the team!!!
Diyi Yang retweeted
TaskNet is live!
By accurately mapping the tasks workers perform at their jobs, the project will help us gain a better understanding of AI's impact on work.
Explore Tasknet here: tasknet.stanford.edu
Diyi Yang retweeted
AI can reshape people's life in various ways, but its impact on job may be one of the most profound. It's not just about how we work, but also about human autonomy and dignity.
Back in 2025, we collaborated with @erikbryn 's team to investigate automation vs augmentation across the U.S. workforce (futureofwork.saltlab.stanfor…). One key limitation, however, was that the work was grounded on existing human work curated by O*NET and do not took emerging new work into consideration.
That's why I am so excited to see TaskNet by the amazing @jennjwang! It's more than a carefully curated, one-off dataset and offers a scalable way to track how work evolves, especially at a time when the pace of change is accelerating.
Check it out!! digitaleconomy.stanford.edu/…
Diyi Yang retweeted
A labor of love with Mercor and the O*NET Center! TaskNet brings together academic research, industry experience, and occupational data expertise to turn workers’ experiences into task information that can inform workforce policy and training.
Workers in six occupations do 79 tasks the government's official occupational taxonomy has yet to add. That work is worth about $140B a year in wages.
That taxonomy is called O*NET, the U.S. Department of Labor's database of the tasks each occupation involves. Workers use it to help with job transitions, and economists use it to estimate which jobs AI will change. If the lists are incomplete, so are the estimates.
Today we're releasing TaskNet, an open-source task catalog built on O*NET and updated for how AI is changing work.
Built with researchers from Stanford, the O*NET Resource Center, and NBER. Thank you to @robinsonaisaac @erikbryn @diyi_yang @jennjwang @andreas_h0wpt @Digeconlab @stanfordnlp @stanfordhai @stanfordAIlab for contributing to this work.
Read the blog: mercor.com/blog/introducing-…
Diyi Yang retweeted
Workers in six occupations do 79 tasks the government's official occupational taxonomy has yet to add. That work is worth about $140B a year in wages.
That taxonomy is called O*NET, the U.S. Department of Labor's database of the tasks each occupation involves. Workers use it to help with job transitions, and economists use it to estimate which jobs AI will change. If the lists are incomplete, so are the estimates.
Today we're releasing TaskNet, an open-source task catalog built on O*NET and updated for how AI is changing work.
Built with researchers from Stanford, the O*NET Resource Center, and NBER. Thank you to @robinsonaisaac @erikbryn @diyi_yang @jennjwang @andreas_h0wpt @Digeconlab @stanfordnlp @stanfordhai @stanfordAIlab for contributing to this work.
Read the blog: mercor.com/blog/introducing-…
Diyi Yang retweeted
We see this project as a step toward a broader effort with the O*NET Center to keep occupational information current and grounded in what workers actually do.
📄 Paper: doi.org/10.2139/ssrn.7670419
💻 Code: github.com/jennjwang/tasknet…
🕸️ Website: digitaleconomy.stanford.edu/…
Diyi Yang retweeted
Updating task lists changes AI exposure estimates—in both directions.
With TaskNet, estimated exposure rises for software developers and lawyers, two occupations where AI’s effects are widely debated, and falls for personal care aides.
Diyi Yang retweeted
TaskNet adds 79 new tasks to O*NET 31.0, such as developers designing prompts and evaluating AI model outputs.
It also refines existing tasks, breaking down broad tasks like “represent clients in court” into preparing witnesses and presenting arguments for lawyers.
Diyi Yang retweeted
Jobs are modeled as bundles of tasks. But as jobs change, how do we keep those task lists up to date?
Workers often experience new responsibilities and tools before they reach their job descriptions.
We start by listening to them. 🧵
Diyi Yang retweeted
🚀 Are you a PhD student pushing the boundaries of AI, robotics, graphics, or accelerated computing?
NVIDIA's Graduate Fellowship offers up to $60K in funding, mentorship from NVIDIA researchers, and a summer research internship!
⏳ Apply by October 30!
blogs.nvidia.com/blog/applic…
