基本信息
浏览量:868
职业迁徙
个人简介
My research area is Artificial Intelligence with a focus on large-scale constraint-based reasoning, optimization, and machine learning. Recently, I have become deeply immersed in the establishment of the new field of Computational Sustainability and in AI for Science.
Computational Sustainability is a new interdisciplinary research field, with the overarching goal of studying and providing solutions to computational problems for balancing environmental, economic, and societal needs for a sustainable future. Such problems are unique in scale, impact, complexity, and richness, often involving combinatorial decisions, in highly dynamic and uncertain environments, offering challenges but also opportunities for the advancement of the state-of-the-art of computer and information science. Work in Computational Sustainability integrates in a unique way various areas within computer science and applied mathematics, such as constraint reasoning, optimization, machine learning, and dynamical systems. Concrete examples of computational sustainability challenges range from planning and optimization for wildlife preservation and biodiversity conservation, to poverty mapping, to combining (deep) data-intensive learning with inference, reasoning, and optimization to accelerate the discovery of new renewable materials such as solar fuels.
Computational Sustainability is a new interdisciplinary research field, with the overarching goal of studying and providing solutions to computational problems for balancing environmental, economic, and societal needs for a sustainable future. Such problems are unique in scale, impact, complexity, and richness, often involving combinatorial decisions, in highly dynamic and uncertain environments, offering challenges but also opportunities for the advancement of the state-of-the-art of computer and information science. Work in Computational Sustainability integrates in a unique way various areas within computer science and applied mathematics, such as constraint reasoning, optimization, machine learning, and dynamical systems. Concrete examples of computational sustainability challenges range from planning and optimization for wildlife preservation and biodiversity conservation, to poverty mapping, to combining (deep) data-intensive learning with inference, reasoning, and optimization to accelerate the discovery of new renewable materials such as solar fuels.
研究兴趣
论文共 356 篇作者统计合作学者相似作者
按年份排序按引用量排序主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
Marc Grimson,Rafael Almeida,Qinru Shi,Yiwei Bai,Héctor Angarita, Felipe Siqueira Pacheco, Rafael Schmitt,Alexander Flecker,Carla P. Gomes
AAAI 2024no. 20 (2024): 22067-22075
Lingkai Kong,Yuanqi Du,Wenhao Mu,Kirill Neklyudov, Valentin De Bortol, Haorui Wang,Dongxia Wu,Aaron Ferber,Yi-An Ma,Carla P. Gomes,Chao Zhang
CoRR (2024)
引用0浏览0EI引用
0
0
AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systemspp.756-765, (2023)
引用0浏览0EI引用
0
0
2023 International Conference on Machine Learning and Applications (ICMLA)pp.392-399, (2023)
加载更多
作者统计
合作学者
合作机构
D-Core
- 合作者
- 学生
- 导师
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn