Weakly Supervised Machine Learning in Medical Sensing: Integrating Expert Knowledge and Programming with Data
Postdoctoral Research Fellow at Stanford University (InfoLab)
Thursday 12 July 2018, 5.30 - 7 pm
Rhodes House, South Parks Road OX1 3RG
One of the most significant roadblocks to using modern machine learning models is collecting hand-labelled training data at the massive scale they require. In real-world settings such as clinical medicine, where domain expertise is needed, and modelling goals change rapidly, hand-labelling training sets are prohibitively slow, expensive, and static. For these reasons, practitioners are increasingly turning to weak supervision techniques wherein noisier, often programmatically-generated labels are used instead. In this talk, we will discuss recent developments in applying various types of weak supervision to problems in medical imaging and diagnostics, and assess future areas wherein the confluence of inadequate control and massive, unlabeled datasets could lead to new discoveries and reductions to an application.
Jared is a current Postdoctoral Research Fellow in Computer Science at Stanford University, where his research focuses on combining heterogeneous data modalities, machine learning, and human domain expertise to inform and improve decisionmaking around such topics as human health, energy & environment, and geopolitical stability. Jared has also worked to bridge the gap between technological development and effective deployment in a variety of contexts including foreign policy at the U.S. Senate Foreign Relations Committee, solar electrification at Offgrid Electric, cybersecurity at the Center for Strategic and International Studies, emerging technology investment at Draper Fisher Jurvetson, nuclear fusion modeling at the Oxford Mathematical Institute, and nonlinear energy harvesting at Duke University. Jared holds a PhD from Stanford University (2017), a B.S. from Duke University, and both an MSc. in Mathematical Modeling and Scientific Computing and an M.B.A. from Oxford, where he studied as a Rhodes Scholar
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