Causality and Increasing Model Reliability — SUCHI SARIA
Causality and Increasing model Reliability: Learning Models that are Safe and Robust to Dataset Shifts
Suchi Saria directs the Machine Learning and Healthcare Lab at Johns Hopkins University and is the founding research director of the Malone Center for Engineering in Healthcare. She is interested in enabling new classes of diagnostic and treatment planning techniques for healthcare—tools that use statistical machine learning techniques to tease out subtle information from "messy" observational datasets and provide reliable inferences for individualizing care decisions.
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The Summer School of Machine Learning at Skoltech (SMILES) is an online one-week intensive course about modern statistical machine learning methods.
It aims at bringing together the Machine Learning community from the CIS, Central Asia, and the Caucasus regions. SMILES presents topics that are at the core of machine learning research, from fundamentals to the state-of-the-art.
Causality and Increasing model Reliability: Learning Models that are Safe and Robust to Dataset Shifts
Suchi Saria directs the Machine Learning and Healthcare Lab at Johns Hopkins University and is the founding research director of the Malone Center for Engineering in Healthcare. She is interested in enabling new classes of diagnostic and treatment planning techniques for healthcare—tools that use statistical machine learning techniques to tease out subtle information from “messy” observational datasets and provide reliable inferences for individualizing care decisions.
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The Summer School of Machine Learning at Skoltech (SMILES) is an online one-week intensive course about modern statistical machine learning methods.
It aims at bringing together the Machine Learning community from the CIS, Central Asia, and the Caucasus regions. SMILES presents topics that are at the core of machine learning research, from fundamentals to the state-of-the-art.
