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🚨 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,

Excited to share that our paper extending the Isotonic Mechanism (arxiv.org/pdf/2304.11160) has been accepted in JRSSB. Delighted that *improving ML/AI conference review processes* is being recognized as a valuable research direction in statistics.

Enjoyed a lot reading this work on differential privacy.

Congratulations to the 2025 Class of IMS Fellows! Each Fellow has demonstrated distinction in research in statistics or probability or has demonstrated leadership that has profoundly influenced the field. See the new Fellows here: imstat.org/2025/05/05/con…

We're hiring a postdoc focused on the statistical foundations of large language models, starting this fall. Join our team exploring the theoretical and statistical underpinnings of LLMs. If interested, check our work: weijie-su.com/llm/ and drop me an email. #AIResearch

I just wrote a position paper on the relation between statistics and large language models: Do Large Language Models (Really) Need Statistical Foundations? arxiv.org/abs/2505.19145 Any comments are welcome. Thx

As AI models become more humanlike, traditional detection tools are falling behind. A study from Weijie Su, Qi Long & more introduces a framework to evaluate and strengthen watermarking methods, making them more resilient to edits and easier to detect: whr.tn/441Bbd3