Three Johns Hopkins University faculty members have been named 2026 Sloan Research Fellows by the Alfred P. Sloan Foundation. These two-year, $75,000 fellowships are awarded to young scientists in the United States and Canada who have the potential to become leaders in their fields. Mateo Diaz, Yayuan Liu and Soledad Villar, assistant professors in the university’s Whiting School of Engineering, are among the 126 scholars recognized as fellows this year.
With the addition of Diaz, Liu, and Villar, JHU now has 92 Sloan researchers since the award’s inception in 1955. Candidates must be tenured faculty members in the fields of chemistry, computer science, earth system science, economics, mathematics, neuroscience, or physics. However, there is no lifetime tenure. The fellowship is highly prestigious and competitive, with more than 1,000 nominations each year.
About the researcher
Image caption: Mateo Diaz
Mateo DiazHe is an assistant professor in the Department of Applied Mathematics and Statistics and a member of the Data Science Mathematics Institute and the Data Science and AI Institute, where he studies the interplay between optimization, statistics, and geometry, with an emphasis on the design and analysis of practical large-scale algorithms for applications across data science, machine learning, operations research, and signal processing.
His interest in optimization stems from its universal relevance to science and engineering. “Optimization is important for training machine learning models, planning complex logistics, and controlling autonomous systems such as robots and drones,” Diaz said. “However, the inherent non-convexity and non-smoothness of these applications, coupled with their scale, make them difficult to tackle using traditional methods. My goal is to develop efficient algorithms that can overcome these structural difficulties at modern scales.”
Diaz said he is honored to be recognized by the Sloan Foundation. “Given the historical weight and legacy of this award, it is surreal and humbling to be named a Sloan Fellow,” he said. “I am deeply grateful for this fellowship, which will allow my group and I to continue pursuing the fundamental problems that we are most passionate about.”
Image caption: Yayuan Liu
image credit: Will Kirk / Johns Hopkins University
Yayuan Liuassistant professor of chemical and biomolecular engineering with a secondary appointment in materials science, works at the interface of chemical engineering, materials science, and electrochemistry to accelerate the realization of energy and environmental sustainability. The Liu research group designs materials and electrochemical processes and uses advanced characterization to link microscopic phenomena to macroscopic performance. Its research focuses on redox-active carbon capture and electrosynthesis, high-precision electrochemical interfaces for separation, and high-resolution imaging of electrochemical processes.
“I am honored to be selected as a 2026 Sloan Fellow,” said Liu. “This fellowship is very meaningful to me, as it recognizes not only the research that our group has accomplished to date, but also the broader perspective of advances in electrochemical technologies for carbon capture and recovery of important materials. I am especially grateful to my students and collaborators whose creativity and dedication have made this research possible.”
This recognition drives Liu to pursue ambitious research at the forefront of sustainable electrochemical engineering.
“The Sloan Fellowship provides valuable flexibility to pursue high-risk, high-return ideas aimed at making electrochemical processes more scalable, energy efficient, and impactful for addressing climate and resource challenges,” she said.
Image caption: Soledad Villar
Soledad Villar He is an assistant professor of applied mathematics and statistics at the Johns Hopkins Data Science and Mathematics Institute, where his research lies at the intersection of mathematical data science, representation learning, geometric deep learning, and equivariant machine learning. Her research focuses on using tools from algebra and geometry to design machine learning models with desirable mathematical properties, such as incorporating symmetries and physics-inspired constraints directly into their structures.
Although her research is rooted in mathematical theory, Villar says the resulting principles are playing an increasingly important role in shaping how machine learning models are designed and deployed in a wide range of scientific applications.
“AI is built and communicated in mathematical language, and even though it can seem like a black box, we can use mathematics to understand why and how AI works and how it can be improved,” Villar said. “Incorporating mathematical principles into the design of AI models is a natural way to make them more effective and reliable in scientific discovery.”
Her lab’s research investigates how models can generalize across different data sizes and computational scales. This is an area of increasing interest as researchers seek more efficient and adaptive machine learning systems. Villar is particularly excited about the emerging mathematical connection between arbitrary-dimensional machine learning and hyperparameter transfer, two complementary approaches aimed at improving how models are trained and applied.
“Arbitrary-dimensional machine learning is based on the observation that many machine learning models are defined based on a fixed set of parameters, but can be evaluated on inputs of any size or dimension,” she said. “Hyperparameter transfer takes a complementary perspective by identifying how optimization strategies developed for smaller models can be applied to larger models, allowing us to design more efficient and scalable systems.”
Villar recognized the students, postdoctoral fellows, and collaborators as key contributors to the research recognized by the Sloan Research Fellowship. “My students, postdocs, and collaborators are a fundamental part of my research program,” she said. “To be honest, without them I wouldn’t have won this award.”
