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We are hiring
We are hiring
Summer interns (Machine learning research) working with the team for 12 weeks from May to August, at Plymout Meeting, Pennsylvania.
Summer interns (Machine learning research) working with the team for 12 weeks from May to August, at Plymout Meeting, Pennsylvania.
In summer 2020, we are aiming at carrying on research for the topics below:
- Representation learning of graph data, network science: learning from graphs is the foundation of many projects addressing HCP-patient relations, e.g., the doctor referral network.
- Subsequence mining over patient journey: the subsequence is not necessarily constructed by consecutive services. The machine should learn the important meaningful milestones in patient journey. This topic is important for ML interpretability.
- Semi-supervised learning/ supervised learning with extreme imbalance data: this is crucial for disease detection, particularly rare disease detection. Disease detection essentially is a PU learning task because there is no true negative.
- Causality inference by machine learning: we hope to leverage the development in this area to enhance our model's interpretability.
- Time to event analysis: predict when some event, e.g. treatment initiation, diagnosis of some chronic disease, will happen. Survival analysis could be a good tool here, and can survival analysis benefit from machine learning?
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