Get to Know Our Quantitative Sciences Team: The Brains Behind Our Data (Zhongdi Chu)

Verana Health has a team of more than 30 quantitative scientists who work toward the goal of producing high-quality, data-driven insights that can help inform research and improve patient outcomes. 

In this blog series, we invite you to “Get to Know Our Quantitative Sciences Team: The Brains Behind Our Data.” Read more about Zhongdi Chu, Quantitative Sciences senior manager.

Q: How would you explain your role? 

A: I lead our Data Development group within the Quantitative Sciences team. In my role, I work with a cross-functional team to create fit-for-purpose Qdata® modules using natural language processing (NLP), computer vision (CV), and artificial intelligence/machine learning (AI/ML) techniques.   

Q: What attracted you to the Quantitative Sciences field? 

A: I appreciate the ability to explain things with numbers. I find human biology fascinating and I enjoy being able to represent biology mathematically, so that we can make comparisons and draw conclusions. The quantitative sciences field seems like the perfect field to make a human impact and maintain logic and order.  

Q: What attracted you to Verana Health? 

A: Verana Health is really exciting to me because of the access to an ocean of data. There are ample opportunities to explore, experiment, innovate and execute using multiple state-of-the-art techniques. It’s also encouraging to know that we make a positive impact to society by helping patients gain access to the treatments they need, either through supporting clinical trials recruitment, post-marketing label expansion, or other real-world evidence (RWE) studies on safety and efficacy. 

Q: What inspires you to do this work? 

A: The potential we have to further human understanding of certain diseases, as well as to improve patient outcomes, is what inspires me to do this work.

Q: What is the most rewarding part of your job? 

A: The most rewarding part of my job is delivering meaningful results using advanced techniques in a cross-functional setting. For example, we recently launched our Qdata Geographic Atrophy module using a variety of AI/ML techniques. Through Qdata Geographic Atrophy, we are able to understand disease under-diagnosis and under-coding with advanced NLP/CV ML algorithms. We are also able to track real-world geographic atrophy growth rate, the exact endpoint used in clinical trials. 

Q: What advancements do you see in your field in the next 5 years? 

A: In the next 5 years, I see AI/ML being widely adopted and leveraged in clinical settings at the point of care. This will also enable RWE to be more widely leveraged in regulatory decision-making processes.

When she’s not using advanced techniques to create insights, Zhongdi enjoys hiking in her spare time.