Biography

In plain language, Jiali is:

I’m Jiali — someone who finds peace in the quiet clarity of blue: the vastness of the ocean, the openness of the sky. I’m drawn to still places that hold time gently — a quiet morning, an old museum, the steady rhythm of thought. Natural history museums are my favorite; they remind me how brief and beautiful everything is, and how much wonder lives in what we often overlook.

I move through life with a quiet curiosity — always observing, often reflecting. I value sincerity, subtle beauty, and the kind of strength that doesn’t shout. Whether I’m lifting weights, walking alone, or standing before a fossil that’s waited millions of years to be seen, I’m searching for the same thing: something real, something lasting, something quietly alive.

Research Interests

After all these years, she is professionally interested in:

I am driven by the question of how machine learning and artificial intelligence can unlock the full potential of cohort and electronic health record (EHR) data to transform clinical decision-making. My research focuses on modeling patient heterogeneity and disease trajectories to enable precision medicine strategies that deliver individualized, effective care. Through my work analyzing large-scale EHR and cohort datasets, I apply machine learning methods to uncover actionable patterns that support patient-centered interventions and advance the integration of real-world data into clinical practice.

While healthcare generates a wealth of data, critical gaps remain in how these resources are leveraged to improve patient outcomes. Electronic health records (EHR), though increasingly widespread, are still underutilized for predictive modeling, and claims data, while comprehensive in coverage, often lack key clinical details such as laboratory measurements. Each data source has limitations when used in isolation, but by integrating EHR, claims, genomic, and other datasets, we can better detect risk factors, monitor disease progression, and guide timely interventions. A major challenge lies in applying advanced machine learning and artificial intelligence methods to translate complex, heterogeneous data into clinically meaningful insights. My current work focuses on applying and adapting informatics approaches to support precision medicine, with the goal of helping healthcare providers deliver more individualized, data-driven care in real-world clinical settings.

Keywords:

Precision Medicine Big Data Analysis Clinical Informatics Machine Learning Artificial Intelligence Modeling Electronic Health Records (EHR) Translational Research

Looking ahead:

In the years to come, I hope to deepen my work at the intersection of data, medicine, and humanity. My mission is to develop tools that not only analyze health outcomes, but also honor the lives behind the numbers. Through research, collaboration, and quiet persistence, I aim to help shape a future where healthcare is more precise, more equitable, and more deeply connected to the real world we live in.