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Bio
Research Expertise and Interests
statistical inference for high dimensional data and interdisciplinary research in neuroscience, remote sensing, and text summarization
My current research focuses on practice, algorithm, and theory of statistical machine learning and causal inference. My group is engaged in interdisciplinary research with scientists from genomics, neuroscience, and precision medicine. In order to augment empirical evidence for decision-making, we are investigating methods/algorithms (and associated statistical inference problems) such as dictionary learning, non-negative matrix factorization (NMF), EM and deep learning (CNNs and LSTMs), and heterogeneous effect estimation in randomized experiments (X-learner). Their recent algorithms include staNMF for unsupervised learning, iterative Random Forests (iRF) and signed iRF (s-iRF) for discovering predictive and stable high-order interactions in supervised learning, contextual decomposition (CD) and aggregated contextual decomposition (ACD) for interpretation of Deep Neural Networks (DNNs).
statistical inference for high dimensional data and interdisciplinary research in neuroscience, remote sensing, and text summarization
My current research focuses on practice, algorithm, and theory of statistical machine learning and causal inference. My group is engaged in interdisciplinary research with scientists from genomics, neuroscience, and precision medicine. In order to augment empirical evidence for decision-making, we are investigating methods/algorithms (and associated statistical inference problems) such as dictionary learning, non-negative matrix factorization (NMF), EM and deep learning (CNNs and LSTMs), and heterogeneous effect estimation in randomized experiments (X-learner). Their recent algorithms include staNMF for unsupervised learning, iterative Random Forests (iRF) and signed iRF (s-iRF) for discovering predictive and stable high-order interactions in supervised learning, contextual decomposition (CD) and aggregated contextual decomposition (ACD) for interpretation of Deep Neural Networks (DNNs).
Research Interests
Papers共 364 篇Author StatisticsCo-AuthorSimilar Experts
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引用量
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合作机构
CoRR (2024): 5019-5073
Cited1Views0EIBibtex
1
0
crossref(2024)
arxiv(2024)
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0
0
medRxiv : the preprint server for health sciences (2024)
Cited10Views0EIBibtex
10
0
arxiv(2024)
Cited0Views0Bibtex
0
0
ICML 2024 (2024)
Cited0Views0EIBibtex
0
0
J. Open Source Softw.no. 96 (2024)
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