基本信息
浏览量:2
职业迁徙
个人简介
From a methodological perspective, my current research focuses on the development of machine learning models and algorithms that both robust and efficient. The REML Lab studies multiple aspects of robustness including robustness to uncertainty and missing data, as well as multiple aspects of efficiency including data efficiency, computational scalability, and communication efficiency. The Lab’s foundations are in probabilistic machine learning. Recent research topics include modeling sparse and irregularly sampled time series, real-time active learning, hierarchical zero-shot learning, and Bayesian deep learning.
The Lab’s research is informed by multiple real-world applications domains and machine learning deployment contexts including clinical and mobile health, embedded systems, and the Internet of Things. See the REML Lab web page for details on lab members, current and completed projects and publications.
研究兴趣
论文共 92 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
Peter Abadir,Esther Oh,Rama Chellappa, Niteesh Choudhry,George Demiris,Deepak Ganesan,Jason Karlawish,Benjamin Marlin, Rose M Li,Najim Dehak,Alicia Arbaje,Mathias Unberath,
Alzheimer's & dementia : the journal of the Alzheimer's Association (2024)
Drug and alcohol dependence (2023): 110898-110898
SenSyspp.1041-1046, (2022)
Conference on Machine Learning and Systems (MLSys) (2022)
引用0浏览0EI引用
0
0
Karine Tung,Steven De La Torre,Mohamed El Mistiri, Rebecca Braga De Braganca,Eric B. Hekler,Misha Pavel,Daniel E. Rivera, Pedja Klasnja, Donna Spruijt‐Metz,Benjamin M. Marlin
arXiv (Cornell University) (2022)
引用0浏览0引用
0
0
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