Top 100 AI · United States
Chelsea Finn
Stanford 
- Score
- 7,098−31 today
- Rank
- 81/198 ↓4
- over 90 days
- +318+4.4%
Chelsea Finn is an Assistant Professor of Computer Science and Electrical Engineering at Stanford University and co-founder of Physical Intelligence. She specializes in AI, robotics, and machine learning, frequently sharing research findings, benchmarks, and practical applications of robotic systems. Her content includes detailed analyses of AI model performance, robotics tasks, and educational resources like lecture videos and workshop talks.
Data as of Oct 26, 2026.
The raw numbers · last 90 days
- Median views
- 38.6K
- per post
- Median engagements
- 405
- per post
- Posts
- 11
- over 90 days
- Followers
- 129.4K
- all scored platforms
What drives the score
Percentiles 0–100 against all tracked creators.
- Reach · 30%85
Average views per post over 90 days
- Engagement · 30%69
Average reactions per post
- Audience · 30%75
Total followers, relative to population
- Consistency · 10%66
Posting frequency (25 posts / 90 days = 100)
Platforms
129.4K total followers
| Platform | Followers | Score |
|---|---|---|
| X@chelseabfinn | 106K | 8,265t2 |
| LinkedIn@cbfinn | 23.4K | 2,600t9 |
Last 30 days
Finn's top posts
X · 14 days ago#1CFChelsea FinnThe Pi API is officially in beta! 🤖 Many people have already been using it to fine-tune π models for their robots -- below are a few We’re hoping to expand testing significantly. Let us know if you are interested! Bl…
29.3K views583 engagements
- X · 3 days ago#2
Generative value classifiers are a new parametrization of Q-functions that don't require action conditioning. We think these may combine gracefully with large, pre-trained policies and learn/generalize better from a r…
CFChelsea Finn25.2K views405 engagements
- X · 18 days ago#3
As robots become more reliable, DAgger becomes far less efficient. eg 90% success means the person spends >90% of the time passively watching the robot Key idea: failures come from similar initial states & we c…
CFChelsea Finn38.6K views399 engagements
- X · 23 days ago#4
Scientific discovery is one of the most exciting applications of LLMs, but we lack data & evals on the scientific process. ScholarCatalyst captures which prior papers can advance new research on a problem, rather …
CFChelsea Finn28.6K views344 engagements
X · 6 days ago#5CFChelsea FinnHow do we scale up the context of LLMs? Agentic search scales to arbitrarily large amounts of info, but can easily miss relevant info. Idea💡: Optimize organization of info for downstream utility. Leads to 30+% gains …
24.4K views341 engagements
Head-to-head
