Biostatistics in the Age of Artificial Intelligence: Lessons from Yinchao’s Return Home

December 25, 2025

Biostatistics in the Age of Artificial Intelligence: Lessons from Yinchao’s Return Home

Last year, in the final post of the year, I published a personal opinion piece on the future of biostatistics in the era of artificial intelligence. This year, on the occasion of my friend Yinchao leaving the United States and returning to his home country, I would like to share his story in this country and some of the lessons he left me after several years of close friendship.

Personally, I cannot think of anyone more inspiring or wiser—especially given his young age—from whom one can learn so much. Without a doubt, every day I speak with him, I learn something new.

Finally, I will share with some readers of the blog a few predictions about the future of the field that Yinchao himself made during a dinner among friends at an Ethiopian restaurant—predictions that will likely leave no one indifferent. Let us begin.

An Introduction to Yinchao’s Life

Yinchao was trained as a mathematician and later specialized in the field of biostatistics. He was born into a working-class, middle-income family and was one of five siblings. From a very young age, he stood out at school, particularly in subjects such as History and Philosophy. As he often said himself, he was the most passionate and hardworking student: although mathematics came naturally to him, he was never the best in his class.

However, he frequently repeated an idea that deeply shaped him: the future of a country depends on the existence of engineers, and for engineers to exist, it is essential that they achieve an outstanding level in mathematics.

Another interesting aspect of his childhood, beyond his passion for reading and classical literature, was his love of sports. He was particularly fond of table tennis and frisbee—so much so that many days he would skip his homework to practice these sports compulsively with his friends.

Like any idealistic young person full of dreams, and motivated by a health problem affecting a close family member, he knew from a very early age that he wanted to dedicate himself to something that would have a real impact on medicine. At the age of ten, he promised himself that one day he would work in cybernetics and artificial intelligence applied to medicine.

In pursuit of these dreams, and after successfully graduating in Mathematics, Yinchao made what was probably the most important decision of his life: moving to the United States to pursue a master’s degree in Statistics and, later on, a PhD. As he would later tell us, even back in his home country he was already clear about something fundamental: he did not consider himself the most intelligent, and he felt that in his previous education the “genesis” of data analysis methods was often poorly explained—that is, why ideas emerged and how they were actually developed.

He believed that if he wanted to excel, he needed to go where the best were and learn directly from those who had built—or were still building—modern statistical techniques. In his mind, that place was the United States, where many of the leading figures in the field were still active.

For Yinchao, data analysis in medicine is also a much richer domain than statistics alone. It is not enough to apply models: one must understand the essence of biological problems while innovating in the construction of algorithms capable of handling complex structures, high dimensionality, missing data, and different types of measurement error. When properly designed, these tools have the potential to change the lives of millions of patients.

During his master’s and PhD, he worked day and night, eventually graduating with significant contributions to methodological statistics applied to biological data. For example, he developed new methods to identify which variables are truly relevant in biological problems when the observed signal is heavily contaminated by noise. Yet, as he himself often said, what made him most proud was not the mathematical complexity of an algorithm, but the fact that his methods could have a real impact on medicine.

My First Encounter with Yinchao

The day I met Yinchao, he introduced himself in a very kind manner. With a smile, he asked me:
—So, what do you do?

I replied that I was trying to understand the complex structures of functional data that appear in records from wearable devices in the context of digital health.

For about ten minutes, I spoke about the positive impact that developing reliable and effective methods in this setting could have on healthcare. He listened attentively and, with his characteristic honesty, acknowledged something that struck me deeply: those data structures seemed extremely complex to him, and he would not work on that topic because he felt he could not carry out a rigorous analysis of many of the estimators required to build efficient solutions in an empirical setting.

We talked for more than an hour, and from then on we stayed in touch. Over time, we built a solid friendship, discussing science and philosophy—and we still do so today, even at a distance.

The Last Dinner with Yinchao in Boston

At our farewell in Boston, seven friends gathered for dinner at a restaurant in the city. Both during the taxi ride and throughout the meal, we had a particularly enriching exchange of ideas with Yinchao and were able to hear his views on the future of biostatistics. Some of the statements I cherish most—and that invite reflection—are the following:

“Marcos, biostatistics will be the central field of healthcare. Without biostatistics, it is impossible to achieve the dream—perhaps a utopian one—of personalized medicine, or to build sustainable healthcare systems in increasingly aging societies. It is the engine of the new era of healthcare.”

“Scientists should think less about their ego and more about truly efficient solutions that push our fields forward. Especially among younger people, I see little ambition to work on problems that really matter.”

“I believe the United States will become less attractive in the future. In our fields, many of the great masters who live there are retiring or disappearing, and I do not understand how Europe is being left out of the game.”

“We should not be afraid of the future or of artificial intelligence. In the biomedical field, if used properly, it can only bring benefits.”

“Education has a transformative power, but I do not understand how, with technological progress, the level of rigor can keep decreasing. People should be increasingly better prepared. In the future, Marcos, I would like to write a book on empirical processes for physicians, so that they can understand the foundations of data analysis algorithms and truly grasp how automated clinical decisions are made.”

Without a doubt, getting to know Yinchao’s perspectives is deeply inspiring. Hopefully, this story—shared during the Christmas season—will encourage us to reflect and to do better, more meaningful work every day in our respective scientific fields.

Merry Christmas!