Kilian Weinberger
Kilian Weinberger studies representation learning, including shared work on distance metrics and densely connected neural networks. Weinberger studied mathematics and computing at Oxford and completed doctoral work in machine learning with Lawrence Saul at Pennsylvania. Before Cornell, he worked at Yahoo Research and Washington University. Weinberger served as ICML president from 2023 to 2025. He was an ACM and AAAI Fellow by 2024. His research includes resource constraints, Gaussian processes, deep learning and perception for autonomous vehicles.
- Now at
- Cornell University
- Works
- 240
- Citations
- 81,857
- h-index
- 72
Publication numbers from OpenAlex, as of 14 October 2026.
Timeline
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As of October 2026
Machine learning and earlier research roles
Weinberger studied mathematics and computing at Oxford and completed doctoral work in machine learning with Lawrence Saul at Pennsylvania. Before Cornell, he worked at Yahoo Research and Washington University.
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As of October 2026
Learning how to compare data
He contributed to Large Margin Nearest Neighbor learning, which brings similarly labeled inputs together and separates different labels. His work also includes Word Mover’s Distance, which compares text documents through a transportation problem over word embeddings.
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2017
DenseNet
Gao Huang, Zhuang Liu, Laurens van der Maaten and Weinberger published Densely Connected Convolutional Networks at CVPR. The network passes earlier layers’ feature maps to later layers through direct connections. The paper received the shared CVPR best paper award.
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2023–2025
Research community leadership
Weinberger served as ICML president from 2023 to 2025. He was an ACM and AAAI Fellow by 2024. His research includes resource constraints, Gaussian processes, deep learning and perception for autonomous vehicles.
Sources and credits
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