Kaiming He
Douglas Ross (1954) Career Development Professor of Software Technology; Associate Professor
Studies computer vision and deep learning, including representation learning, generative models, and network architectures.
Kaiming He's lab at MIT works on generative & foundation models and accelerators, chips & architecture. 25 papers since 2023. Applications close .
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 0%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 11 papers2026 · T1 0 · collecting
- 9 papers2025 · T1 0
- 4 papers2024 · T1 0
- 1 papers2023 · T1 0
T1 venuesOther
Research focuspaper titles that use each topic's words
- Generative & foundation models9 papers
- Accelerators, chips & architecture0 papers
- network architectures0 papers
- representation learning0 papers
- studies computer0 papers
Paper list
From the lab homepage.
202611 papers
- Computer Vision and Pattern Recognition (CVPR)ARC Is a Vision Problem!
- Computer Vision and Pattern Recognition (CVPR)Back to Basics: Let Denoising Generative Models Denoise
- Computer Vision and Pattern Recognition (CVPR)Bidirectional Normalizing Flow: From Data to Noise and Back
- Transactions on Machine Learning Research (TMLR), accepted inDenoising Hamiltonian Network for Physical Reasoning
- Conference on Neural Information Processing Systems (NeurIPS)ELF: Embedded Language Flows
- Conference on Neural Information Processing Systems (NeurIPS)Generative Modeling via Drifting
- International Conference on Machine Learning (ICML)GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training
- Computer Vision and Pattern Recognition (CVPR)Improved Mean Flows: On the Challenges of Fastforward Generative Models
- European Conference on Computer Vision (ECCV)Natural Image Pretraining Improves Abstract Reasoning
- International Conference on Machine Learning (ICML)One-step Latent-free Image Generation with Pixel Mean Flows
- European Conference on Computer Vision (ECCV)Video Generation Models are General-Purpose Vision Learners
20259 papers
- International Conference on Learning Representations (ICLR)A Decade's Battle on Dataset Bias: Are We There Yet?
- International Conference on Learning Representations (ICLR)Deconstructing Denoising Diffusion Models for Self-Supervised Learning
- International Conference on Learning Representations (ICLR)Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens
- Transactions on Machine Learning Research (TMLR), accepted inFractal Generative Models
- International Conference on Machine Learning (ICML)Highly Compressed Tokenizer Can Generate without Training
- International Conference on Machine Learning (ICML)Is Noise Conditioning Necessary for Denoising Generative Models?
- Conference on Neural Information Processing Systems (NeurIPS)Mean Flows for One-step Generative Modeling
- International Conference on Learning Representations (ICLR)TetSphere Splatting: Representing High-Quality Geometry with Lagrangian Volumetric Meshes
- Computer Vision and Pattern Recognition (CVPR)Transformers without Normalization
20244 papers
- Conference on Neural Information Processing Systems (NeurIPS)Autoregressive Image Generation without Vector Quantization
- Conference on Neural Information Processing Systems (NeurIPS)Physically Compatible 3D Object Modeling from a Single Image
- Conference on Neural Information Processing Systems (NeurIPS)Return of Unconditional Generation: A Self-supervised Representation Generation Method
- Conference on Neural Information Processing Systems (NeurIPS)Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers
20231 paper
- Computer Vision and Pattern Recognition (CVPR)Scaling Language-Image Pre-training via Masking
Where alumni went
Alumni are not listed on the lab homepage.
Apply to MITFall 2027
MIT EECS Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
- TOEFL/IELTS
- Fee
- USD 90
Confirmed on the program page.
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Data
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- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
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- Alumni
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