基本信息
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职业迁徙
个人简介
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).
研究兴趣
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crossref(2024)
Merle Behr,Karl Kumbier,Aldo Cordova-Palomera,Matthew Aguirre, Omer Ronen, Chengzhong Ye,Euan Ashley,Atul J Butte,Rima Arnaout,Ben Brown,James Priest,Bin Yu
PloS oneno. 4 (2024): e0298906-e0298906
CoRR (2024)
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arxiv(2024)
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Journal of Open Source Softwareno. 95 (2024)
arxiv(2024)
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CoRR (2024)
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