Francisco Carrillo: using machine learning to study fluid dynamics

Wednesday, Jul 21, 2021
by Sharon Adarlo

Francisco J. Carrillo, 27, doctoral student


Carrillo is a doctoral student in the chemical and biological engineering department and is expected to earn his degree in the fall. In addition, he is completing the graduate certificate program at the Center for Statistics and Machine Learning (CSML).

Before starting his doctoral studies, he earned a master’s degree in chemical and biological engineering from Princeton in 2018. In 2016, he earned a bachelor’s degree in chemical engineering at the University of Texas in Austin.


Carrillo’s main research focus is on computational fluid dynamics, specifically flow of fluid material through a deformable porous medium such as fine-grained soils and sedimentary rocks.

“The simplest way to explain conceptually what I'm doing is thinking about what happens to a sponge once you press it. If it’s full of air, it is a lot easier to press than if it’s full of water,” said Carrillo. “Or if the sponge is filled with a mix of oil and water? What's going to happen if you squeeze it? Instead of a sponge, think of your lungs or the porous lining of your arteries. Or an aquifer or an oil field. I study situations whenever there is a coupling between different solids and flowing materials. And that’s what I look at in my research.”

When he decided to sign up for the CSML graduate certificate program, Carrillo said he had limited experience in machine learning but wanted to learn the discipline and apply it to his studies on fluid dynamics. Specifically, he wanted to apply machine learning techniques to the phenomenon of clogging such as in traffic jams or pipes getting full with sewage.

He created machine learning tools to look at these different types of clogging scenarios in order to see why they happened and how they happened.

“Machine learning is ideally suited to analyze these situations because clogging is a random process which depends on multiple events and many different variables,” he said.

During his time in the CSML program, he developed and released open-source computational models to simulate multiphase flow in soft porous media, and worked with researchers at ETH Zurich to develop a computational model for simulating biofilm hydrodynamics.

With his work on his doctoral degree about done, Carrillo said he can appreciate how his machine learning studies have enhanced his research work.

“I think it gave me a completely different perspective on how to look at a problem,” he said. “It also gave me the opportunity to learn about how machine learning is applied outside my discipline such as in social media and business analytics.”

After graduation, Carrillo plans on taking a postdoctoral position at Stanford University and continuing in the academic field.

Carrillo has received the Gordon Wu Fellowship, the Mary and Randall Hack ‘69 Award, and the School of Engineering Schowalter Award, among other honors.

Extracurricular Activities:

Carrillo is president of Catholic Organizations in Princeton and Austin, Texas where he has organized weekly events, monthly community service activities, directed a five-day mission trip, and a 350-person Easter pilgrimage through the main streets of Austin, among many other activities.

Carrillo was also a volunteer teacher for Stanford University’s “Code in Place” class, which aimed to teach programing to students throughout the world during the Covid-19 pandemic.

For Fun:

Carrillo enjoys playing with his son Joaquin and reading biographies of historical figures such as George Washington, Ulysses S. Grant, and J.P. Morgan.