Song Mei

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Song Mei

Song Mei

@Song__Mei

Assistant Professor at UC Berkeley, Department of Statistics and EECS. Researcher at OpenAI working on LLM training.

Berkeley, CA Katılım Ocak 2015
693 Takip Edilen4K Takipçiler
Song Mei
Song Mei@Song__Mei·
🧄🧄🧄🚀🚀🚀
Yu Bai@yubai01

🧄GPT-5.2 is here – one small step on version number, one giant leap in capabilities. 🚀 With *incredible* @Song__Mei @yaodong_yu @Yuf_Zh @ofirnachum and rest of the @OpenAI team, we applied new techniques to bring our frontier reasoning model to the next level. GPT-5.2-Thinking is much stronger on intelligence, agentic coding, professional use, long-context understanding, and extended thinking. It’s also better on science/theory research – try pairing with it! Congrats also to @yanndubs @ericmitchellai @.ishaan @christinahkim, and heartfelt thanks to the leadership @_aidan_clark_ @max_a_schwarzer @markchen90 @merettm @sama for making this come together!

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Song Mei
Song Mei@Song__Mei·
Today’s the day — GPT-5 is here! One of the reasons I joined OpenAI was to train the next generation of GPT. It became my first big project, and to my surprise, I became one of the core contributors — together with @yubai01 and many amazing colleagues — developing unexpected but cool techniques that made it into the final training stack. Hard to believe that just two months here could lead to such a big impact.
OpenAI@OpenAI

GPT-5 is here. Rolling out to everyone starting today. openai.com/gpt-5/

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Yu Bai
Yu Bai@yubai01·
Today is the day -- we are excited to bring gpt5 to you. Fortunate to have led several workstreams in GPT5 Thinking and Mini model training. Among many other improvements, with @Song__Mei @minyoung_huh @SebastienBubeck and co, we applied some unexpected but cool techniques to make the model smart, chatty, and a good model all-around. Also honored to have worked together with the crew @yanndubs @ElaineYaLe6 @christinahkim @ericmitchellai @michpokrass @max_a_schwarzer and everyone else, it was fun coming together and doing things! Let us know how you like or dislike it -- this will not be the last model we're gonna train.
OpenAI@OpenAI

GPT-5 is here. Rolling out to everyone starting today. openai.com/gpt-5/

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Song Mei
Song Mei@Song__Mei·
Thanks, everyone! Just a quick note—I only contributed a little bit on oss, but I’m glad I had the chance to be involved.
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COPSS
COPSS@COPSSNews·
Finally, our winner for the 2025 George W. Snedecor Award is Hongtu Zhu! 🎉🎉🎉
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Song Mei
Song Mei@Song__Mei·
This is a reminder that the FSML workshop (fsmlims.wixsite.com/fsml25, co-located with JSM) paper submission deadline, Mar 3 AoE, is in less than 2 days. This is a non-archival workshop so feel free to submit any interesting statsML paper already submitted elsewhere. It is a great opportunity to get travel funding to JSM and communicate your recent research with the community. Submission portal here: #tab-recent-activity" target="_blank" rel="nofollow noopener">openreview.net/group?id=imsta…
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Yuhang Cai
Yuhang Cai@YuhangWillCai·
We show the implicit bias of GD for generic non-homogeneous deep nets (results of such were previously limited to homogenous ones). In particular, our results cover those with residual connections and non-homogeneous activation functions. It's a joint work with Kangjie Zhou, @uuujingfeng Jingfeng Wu, @Song__Mei Song Mei, Michael Lindsey, and Peter L. Bartlett! Arxiv: arxiv.org/abs/2502.16075.
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Song Mei
Song Mei@Song__Mei·
Excited to share our review paper on LLM for statisticians!
Jason Weston@jaseweston

🚨 New Paper 🚨 An Overview of Large Language Models for Statisticians 📝: arxiv.org/abs/2502.17814 - Dual perspectives on Statistics ➕ LLMs: Stat for LLM & LLM for Stat - Stat for LLM: How statistical methods can improve LLM uncertainty quantification, interpretability, trustworthiness & more. - LLM for Stat: How LLMs can enhance statistical workflows: from data collection, synthesis, annotation to statistical modeling, with applications to medical research Presents key LLM advances: Architecture, Training, Reasoning, and Self-Alignment: (1) 🧠Evolution of LLM architectures with Transformers and Self-Attention (2) LLM training pipeline from pre-training, SFT, to RLHF and Preference Optimization. (3) 💭 System 2 Prompting and Chain-of-Thought for test-time scaling . (4) 🚀 LLM Self-Alignment for achieving super-human intelligence Statisticians play a key role in the development of large-scale AI models: (1) 💡 Statistical insights improve LLM uncertainty quantification & interpretability (2) 🤖 Watermarking for AI-generated content detection (3) ⚖️ Privacy & algorithmic fairness to ensure responsible AI adoption LLMs can also empower statistical science by: (1) 📈 Scaling up data collection, synthesis, and annotation. (2) 🖥️ Automating statistical coding & exploratory analysis (3) 🔬 Facilitating medical research By bridging statistics & AI, we can: ✅ Improve better LLMs with statistical methodologies. ✅ Leverage LLMs for statistical applications in high-stakes domains

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Song Mei
Song Mei@Song__Mei·
Honored to receive the Sloan Fellowship! Grateful for the support I’ve received along the way. With this opportunity, I look forward to advancing research in the intersection of math, statistics, and AI. #SloanFellow
Sloan Foundation@SloanFoundation

🎉Congrats to the 126 early-career scientists who have been awarded a Sloan Research Fellowship this year! These exceptional scholars are drawn from 51 institutions across the US and Canada, and represent the next generation of groundbreaking researchers. sloan.org/fellowships/20…

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Keşfet

@yubai01 @SebastienBubeck @yanndubs @ElaineYaLe6 @christinahkim @ericmitchellai @michpokrass @max_a_schwarzer