Muqun (Rachel) Li

Muqun (Rachel) Li,

Senior Machine Learning Engineer

Rachel Li employs her machine learning and engineering expertise to conceive, build and refine the tools used by our Clinical Trial Transparency (CTT) client services team. These tools help trial sponsors meet their obligations for transparency and privacy.

Rachel’s academic research focused on how machine learning could be used to automate the laborious and complex process of anonymizing unstructured data – such as that found in long-form clinical trial documentation.

With the arrival of EMA Policy 0070, Rachel came to appreciate just how much impact her research could have to address critical privacy concerns in healthcare. Our industry-leading reputation in data anonymization made Privacy Analytics the obvious choice for Rachel when she decided to move to the private sector.

The automation tools that Rachel has developed free our CTT services team from the time-consuming tasks of data detection and classification. The result is more responsive service that delivers higher value to clients. So while Rachel’s role is not client facing, it contributes to the experience of our CTT clients.

Among her achievements, Rachel led a research collaboration with Canada’s National Research Council to advance the anonymization of clinical free text with deep learning-based approaches.

In 2019, she received a Best Data Science Paper Award at the AMIA 2019 Informatics Summit for a paper published jointly with Vanderbilt University researchers. The paper focused on streamlining the anonymization of unstructured data with machine learning and AI.

Attending such industry conferences gives Rachel the rewarding opportunity to engage with our clients and see first-hand the impact of her efforts.

“Those experiences have opened my horizons about just how important and meaningful our work has been from the client’s perspective,” Rachel says. “I often feel quite fortunate to see that my research is being applied to solve a real-world problem.”

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