Bioinformatics

这位被提名人拥有开发新型模式识别和机器学习软件的丰富经验，这些软件已被证明在乳腺癌诊断和预测中风后组织预后方面优于主流方法。被提名者提议从事研究，以改进这项工作，并评估其在广泛的医学成像应用中的潜力，例如神经发育成像。被提名者将与哈佛医学院保持第二附属关系，以提供对高质量临床成像数据的持续访问，并促进与医生和神经科学家的合作。

The nominee has extensive experience developing novel pattern recognition and machine learning software that has been shown to outperform mainstream methods in the diagnosis of breast cancer and in predicting tissue outcome after stroke. The nominee proposes to engage in research towards improving this work and towards evaluating its potential in a wide variety of medical imaging applications, such as neurodevelopmental imaging. The nominee will maintain a secondary affiliation with Harvard Medical School to provide ongoing access to high-quality clinical imaging data and to facilitate collaborations with physicians and neuroscientists.