Kun Bu is a postdoctoral researcher at the University of South Florida studying advanced statistical and AI approaches to extract reliable knowledge from large, complex datasets. In this Postdoc Portrait interview, she shares how she finds new methods for making meaningful connections in datasets to inform better decision making in science and society.
Advancing Data Integrity with Machine Learning
Q | What drew you to statistical analysis of large data sets?
I was drawn to statistics and data science because I have always been fascinated by finding patterns in complex systems. During my early studies in finance, I saw how decisions affecting millions of people were often made under uncertainty. That experience sparked my interest in understanding how data can be used to make more informed and reliable decisions.
As I continued my education, I realized that the same statistical principles could be applied far beyond finance. Whether studying medication safety, disease outcomes, human behavior, or financial markets, researchers face a common challenge: extracting meaningful insights from imperfect and often overwhelming amounts of data. I was especially attracted to the interdisciplinary nature of the field, where mathematics, computing, and domain expertise come together to solve real-world problems.
What keeps me motivated today is the opportunity to develop methods that not only improve prediction but also help explain why patterns emerge, ultimately contributing to scientific discovery and better decision-making across disciplines.
Q | What scientific problem are you trying to solve?
I am working to solve a fundamental challenge of the modern data era: how to extract reliable knowledge from large, complex, and interconnected datasets. Many important problems in healthcare, finance, and public policy generate enormous amounts of information, but traditional statistical methods often struggle to capture the hidden relationships within these data.
My research develops new statistical and AI approaches that can identify meaningful patterns, quantify uncertainty, and support better decision-making. For example, I have worked on methods to detect medication safety risks using healthcare databases. A common theme across my projects is building tools that help researchers move beyond simple prediction toward deeper understanding of underlying systems.
Ultimately, my goal is to make data-driven discoveries more accurate, interpretable, and useful so that scientists, clinicians, policymakers, and industry leaders can make better-informed decisions in an increasingly data-rich world.
Overcoming Misconceptions in AI
Q | What’s one thing you learned from working with large amounts of data that you didn’t expect?
One surprising lesson I learned is that having more data does not automatically lead to better understanding. Early in my research career, I assumed that larger datasets would naturally produce clearer answers. Instead, I found that large datasets often introduce new challenges, including hidden biases, missing information, and complex relationships that can easily lead to misleading conclusions if not carefully analyzed.
I was also surprised by how much uncertainty remains even when sophisticated AI and statistical models are used. The most valuable insights often come not from finding a single “correct” answer, but from understanding the range of possible explanations and quantifying confidence in the results.
This realization changed how I approach research. Rather than focusing solely on building more powerful predictive models, I now place greater emphasis on interpretability, transparency, and uncertainty quantification. In many real-world applications, helping decision-makers understand what we know—and what we do not know—can be just as important as achieving high predictive accuracy.
Q | If your research succeeds, what could it change for science or society?
This work could help make data-driven decision making more reliable, transparent, and accessible across many fields. As organizations collect increasingly large and complex datasets, there is a growing need for methods that not only generate accurate predictions but also provide meaningful explanations and quantify uncertainty. In healthcare, this could lead to earlier detection of medication safety risks and better patient outcomes. More broadly, the statistical and AI methods I develop could help researchers uncover hidden patterns in complex systems and translate data into actionable knowledge. Ultimately, I hope my work contributes to a future where data are used not simply to automate decisions, but to enhance human understanding and support more thoughtful, evidence-based choices in science, industry, and society.
Q | What question are you most excited to answer next?
I am most excited to answer how we can combine AI with statistical reasoning to better understand complex systems rather than simply predict outcomes. AI has achieved remarkable success in pattern recognition, but many models still operate as “black boxes” making it difficult to understand why they make certain predictions or how much confidence we should place in them. I am particularly interested in developing methods that integrate the predictive power of AI with the interpretability and uncertainty quantification of statistics. By doing so, we can create tools that not only identify patterns in large datasets but also reveal the underlying relationships that drive those patterns. Whether in healthcare, finance, or other scientific domains, answering this question could help researchers and decision-makers move from prediction to understanding, enabling more trustworthy, transparent, and actionable use of data in solving real-world problems.
Responses have been edited for length and clarity.
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