Christopher Ré
Professor of Computer Science
Leads Hazy Research on efficient foundation model architectures, data-centric AI and ML systems.
Christopher Ré's lab at Stanford works on generative & foundation models, accelerators, chips & architecture and efficient training & inference. 57 papers since 2023, 49 at top venues, mostly ICLR and ICML. 11 PhD students. 29 alumni. Applications close .
At a glance
- papers, 2023–2026
- 15/ year
- at T1 venues
- 86%
- PhD students
- 11
- MS students
- 0
- alumni listed
- 29
Papers by yearfrom DBLP, 2023–2026
- 3 papers2026 · T1 2 · collecting
- 13 papers2025 · T1 13
- 16 papers2024 · T1 13
- 25 papers2023 · T1 21
T1 venuesOtherVenues: ICLR, ICML, NeurIPS, VLDB, AAAI
Research focuspaper titles that use each topic's words
- Generative & foundation models26 papers
- Efficient training & inference13 papers
- Accelerators, chips & architecture3 papers
- model architectures0 papers
- data-centric0 papers
Paper list
From DBLP.
20263 papers
- Trans. Mach. Learn. Res.A Unifying Framework for Parallelizing Sequential Models with Linear Dynamical Systems (opens in a new tab)
- ICLR (T1)An Information Theoretic Perspective on Agentic System Design (opens in a new tab)
- ICLR (T1)Cartridges: Lightweight and general-purpose long context representations via self-study (opens in a new tab)
202513 papers
- ICLR (T1)Aioli: A Unified Optimization Framework for Language Model Data Mixing (opens in a new tab)
- ICML (T1)An Architecture Search Framework for Inference-Time Techniques (opens in a new tab)
- ICLR (T1)Context Clues: Evaluating Long Context Models for Clinical Prediction Tasks on EHR Data (opens in a new tab)
- ICML (T1)Cost-efficient Collaboration between On-device and Cloud Language Models (opens in a new tab)
- CVPR (T1)HMAR: Efficient Hierarchical Masked Auto-Regressive Image Generation (opens in a new tab)
- ICML (T1)KernelBench: Can LLMs Write Efficient GPU Kernels? (opens in a new tab)
- ICLR (T1)LoLCATs: On Low-Rank Linearizing of Large Language Models (opens in a new tab)
- JMLR (T1)Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers (opens in a new tab)
- ICLR (T1)Restructuring Vector Quantization with the Rotation Trick (opens in a new tab)
- ICLR (T1)Scaling Laws for Precision (opens in a new tab)
- ICLR (T1)ThunderKittens: Simple, Fast, and Adorable Kernels (opens in a new tab)
- ICLR (T1)Towards Learning High-Precision Least Squares Algorithms with Sequence Models (opens in a new tab)
202416 papers
- VLDB (T1)Automating the Enterprise with Foundation Models (opens in a new tab)
- ICML (T1)Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT (opens in a new tab)
- WACVCollage Diffusion (opens in a new tab)
- ICLR (T1)Context-Aware Meta-Learning (opens in a new tab)
- ICLR (T1)FlashFFTConv: Efficient Convolutions for Long Sequences with Tensor Cores (opens in a new tab)
- ICML (T1)Mechanistic Design and Scaling of Hybrid Architectures (opens in a new tab)
- FAccTModel ChangeLists: Characterizing Updates to ML Models (opens in a new tab)
- ICML (T1)Prospector Heads: Generalized Feature Attribution for Large Models & Data (opens in a new tab)
- NeurIPS (T1)RedPajama: an Open Dataset for Training Large Language Models (opens in a new tab)
- ICML (T1)Simple linear attention language models balance the recall-throughput tradeoff (opens in a new tab)
- NeurIPS (T1)Smoothie: Label Free Language Model Routing (opens in a new tab)
- ICML (T1)State-Free Inference of State-Space Models: The *Transfer Function* Approach (opens in a new tab)
202325 papers
- NeurIPS (T1)A case for reframing automated medical image classification as segmentation (opens in a new tab)
- ICLR (T1)Ask Me Anything: A simple strategy for prompting language models (opens in a new tab)
- NeuroImageBenchmarking explanation methods for mental state decoding with deep learning models (opens in a new tab)
- ICML (T1)CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks (opens in a new tab)
- ICML (T1)Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time (opens in a new tab)
- ICLR (T1)Effectively Modeling Time Series with Simple Discrete State Spaces (opens in a new tab)
- NeurIPS (T1)Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification (opens in a new tab)
- COLT (T1)Fast Algorithms for a New Relaxation of Optimal Transport (opens in a new tab)
- ICML (T1)FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU (opens in a new tab)
- IEEE Trans. ComputersGRIP: A Graph Neural Network Accelerator Architecture (opens in a new tab)
- NeurIPS (T1)H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models (opens in a new tab)
- AAAI (T1)HAPI Explorer: Comprehension, Discovery, and Explanation on History of ML APIs (opens in a new tab)
Where alumni went29 listed
Apply to StanfordFall 2027
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