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| Name |
Geman, Donald J. |
| Location
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Johns Hopkins University |
| Primary Field
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Applied Mathematical Sciences |
 Election Citation
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Geman has made innovative and high-impact contributions to stochastic process theory, image processing, machine learning, computer vision, computational molecular medicine, and small-sample learning. His work is at the foundation of widely used methods in machine vision, machine learning, and transcription-based cancer phenotyping. |
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Research Interests
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Geman's main interest is designing methodology and algorithms in the computational sciences, specifically involving statistical learning, modeling and prediction in computer vision and computational medicine. The driving problem in computer vision is to build a machine which produces a rich semantic description of an underlying scene based on image data. His group has focused on a "twenty questions" or "active testing" paradigm in which the order of the questions is determined online, during scene parsing, driven by removing as much uncertainty as possible about the overall scene interpretation given the evidence to date. One example is a sequential Bayesian approach where the prior distribution encodes contextual constraints and evidence is acquired by sequentially and adaptively executing high-level classifiers. In computational medicine, his group is focused on applying statistical learning to large-scale biomolecular data in cancer systems biology and biomarker discovery; the driving problem is to tailor cancer treatment to an individual molecular profile. Their work is motivated by the hypothesis that a key obstacle to clinical applications is that the decision rules that emerge from off-the-shelf machine learning methods are too complex, impeding biological understanding. As a result, his group is attempting to embed phenotype-dependent mechanisms specific to cancer pathogenesis and progression directly into the learning algorithms. |
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