基本信息
浏览量:25
职业迁徙
个人简介
Research
I model and embed geometry in machine learning. My recent research aims to accelerate drug discovery and material generation, for which geometrical modeling is a core pillar. My deep learning solutions explicitly incorporate geometrical and physical first principles that naturally constrain the 3D structures and the interactions of molecules, proteins, or materials. Euclidean symmetries and other laws governing single and multibody systems are injected in my models to increase quality, efficiency and user trust. My research has wide implications in other related areas such as robotics or 3D graphics.
Geometric characteristics of real world data sometimes go beyond our 3D intuitions. In my past research, I also challenged fundamental geometrical assumptions of representation learning. I advocate for going beyond the traditional Euclidean space to prevent the loss of important structural information for certain ubiquitous types of data. I created principled and efficient deep learning and embedding methods rooted in Riemannian geometry, for instance by leveraging the power and flexibility of hyperbolic and elliptic spaces. My models have been improving machine learning solutions in various areas such as computer vision or natural language processing.
研究兴趣
论文共 26 篇作者统计合作学者相似作者
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引用量
主题
期刊级别
合作者
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CoRR (2024)
引用0浏览0EI引用
0
0
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162 (2022)
semanticscholar(2021)
引用0浏览0引用
0
0
CoRR (2021)
引用18浏览0EI引用
18
0
arXiv (Cornell University) (2021)
引用103浏览0引用
103
0
arXiv (Cornell University) (2020)
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作者统计
#Papers: 26
#Citation: 2685
H-Index: 18
G-Index: 20
Sociability: 4
Diversity: 2
Activity: 20
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