Quanquan Gu ✓
@QuanquanGu · Los Angeles, CA · joined 26 Aug 2017
Founder of Geodesic Intelligence @Geodesiclab | Professor @UCLA | Opinions are my own
26 363Followers
2 512Following
2 555Posts total
1MViews on collected posts
Posts recentes
Procurando novos posts…
Some problems are worth solving as fast as we possibly can.
Drug discovery is one of them.
Let’s find the hardest problems and solve them together!
127.8K views · 116 likes
· 3 reposts · 2 replies
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We’re launching the Geodesic Grand Challenges.
What are the hardest questions in drug discovery that, if solved, could change what’s possible over the next 5–10 years?
We’re asking you to help define them.
Selected questions will become official Geodesic Grand Challenges. Each
183.6K views · 211 likes
· 13 reposts · 2 replies
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This is the direction I’m most excited about for AI for Science: closing the loop between models and real-world experiments!
6K views · 58 likes
· 4 reposts · 2 replies
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@QuanquanGu I don't care about Fields Medalists or not posed the problem. I don't care about AI usage. I care about whether the students gained new knowledge, got hooked, gained experience and will continue enriching human understanding of math. If yes, this was a good case!
18K views · 256 likes
· 5 reposts · 3 replies
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@QuanquanGu not worth celebrating if you ask the fields medalists
4.6K views · 62 likes
· 1 reposts · 2 replies
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@QuanquanGu Congrats to them! Let a billion mathematicians bloom
https://t.co/ujCfJMfsGQ
11.4K views · 97 likes
· 5 reposts · 3 replies
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Just got this incredible news from UCLA Math Circle.
Two high school students, Aayush Bathija and Prince Rohatgi, working with postdoc Daniel Soskin through the UCLA Math Circle, have solved a problem that Fields Medalist June Huh had previously worked on without solving.
The
443.1K views · 3.5K likes
· 399 reposts · 98 replies
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I’m happy to see pharma and many leading scientists taking a much more open view of AI for drug discovery.
If AI can help us understand biology faster, design better therapeutics, and get medicines to patients years earlier, accelerating discovery is exactly the point.
Many
9.1K views · 87 likes
· 2 reposts · 8 replies
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25 Fields Medalists are worried AI solving math too fast could damage mathematics.
I see the opposite happening in coding.
Imagine scientists delaying a cancer cure by 100 years just so they can experience discovering it themselves.
It would be a disaster for humanity.
86.7K views · 2.1K likes
· 167 reposts · 139 replies
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Here is a potential roadmap to reversing aging by 2040, created by GPT-6 Pro with Image 2.5.
It is important to point out that we will already reach longevity escape velocity by around 2035, so if you can make it to then, you are very likely to live for hundreds of more years! h
57.8K views · 1K likes
· 161 reposts · 72 replies
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The Fields Medallists fear AI will break mathematics. I think it may let millions in. https://t.co/4C8k8BN7a6
68.1K views · 477 likes
· 56 reposts · 52 replies
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Em comparação com contas do mesmo porte
12 posts dos últimos 90 dias, ao lado da faixa de 10K–100K seguidores. aparece para muita gente, mas poucos desses espectadores reagem.
Mediana de visualizações37 902esta conta996mediana para 10K–100K
Alcance, %143.77%esta conta3.84%mediana para 10K–100K
Engajamento, %1.00%esta conta1.51%mediana para 10K–100K
| Métrica | Esta conta | Mediana para 10K–100K | Proporção |
|---|---|---|---|
| Mediana de visualizações por post | 37 902 | 996 | 38.0× |
| Alcance (visualizações ÷ seguidores) | 143.77% | 3.84% | 37.4× |
| Taxa de engajamento | 1.00% | 1.51% | 0.66× |
Outras contas desta faixa → Comparar com outra conta → Como estas referências são construídas →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
68.1K12 Sep
57.8K
86.7K
3.3K
9.1K
443.1K
11.4K
4.6K
18K13 Sep
6K16 Sep
183.6K
127.8K
Last 12 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.88%12 Sep
2.18%
2.81%
0.37%
1.07%
0.93%
0.92%
1.40%
1.47%13 Sep
1.07%16 Sep
0.13%
0.09%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.51%.
What the audience does
Likes71.3%8 015 in total
Reposts7.3%816 in total
Replies3.4%383 in total
Quotes1.4%152 in total
Bookmarks16.6%1 868 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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