Undergraduate Complexity Research

Program Purpose and Value

It has become increasingly vital for students across the sciences to gain computational and mathematical skills: the ability to translate real-world systems into quantitative models, carry out computational experiments, analyze these experiments using statistics, and compare results with real-world data. At SFI, these skills are not an end in themselves — they are tools for developing general theories of how complex systems function, adapt, and evolve.

Researchers need to understand the strengths and limitations of different modeling approaches — differential equations, discrete stochastic processes, agent-based simulations, and more — and be able to choose and defend the right level of abstraction for a question. The goal is not just to fit high-dimensional data, but to uncover generative rules and mechanisms that make sense of that data.

At the same time, theory has to stay accountable to the world. Addressing the most urgent scientific and social challenges of the 21st century requires quantitative methods that stay connected to specific systems — ecosystems, economies, cells, cities. The SFI UCR program is designed to train students in exactly this way of thinking: to build, test, and refine theory in conversation with real problems, while asking ambitious questions about how complex systems work at all scales.

Photo Credit: Doug Merriam	Photo Credit: Scott Wagner	Photo Credit: Kate Joyce

Program Design and Structure

Undergraduate students can expect to spend most hours of a work-week dedicated to their chosen research project. The program contains weekly meetings with program directors and education staff as a way of orienting the program. Students will also participate in weekly tutorials on topics ranging from “Effective and Efficient Reading of Scientific Literature” to “Scientific Communication” to “Being A Well Rounded Researcher.”

Being on campus also leverages the unique benefits of the SFI community. This includes seminars on research at the frontiers of complexity science from resident and external faculty, postdoctoral fellows, and invited researchers. Beyond formal programming, students are part of daily life at SFI — sharing lunch, tea, and informal conversation with researchers, faculty, postdocs, and staff. 

Each student will give two presentations to the SFI community: one “flash talk” outlining their chosen research project and one research presentation during the final week of the summer. 

Faculty

The UCR program co-directors are:

Chris Kempes, professor at the Santa Fe Institute. Chris's work focuses on developing general perspectives of life that can be applied in contexts ranging from modern ecology, to astrobiology, to human institutions. He is one of the creators of the Origins of Life course on ComplexityExplorer.org, which explores the question of how life emerged from an abiotic world, and he co-hosted the "Physics of Life" season of SFI’s Complexity podcast in 2024. Chris was a UCR at SFI in 2005.		Melanie Mitchell, Davis Professor of Complexity at the Santa Fe Institute. Melanie's current research focuses on conceptual abstraction, analogy-making, and visual recognition in artificial intelligence systems. She is the founder of ComplexityExplorer.org and instructor of the Introduction to Complexity course there. Her most recent book is Artificial Intelligence: A Guide for Thinking Humans and she co-hosted the "Nature of Intelligence" season of SFI’s Complexity podcast in 2024.

Mentors

Mentors are selected from a transdisciplinary network of resident SFI faculty and postdocs. In addition to working with their assigned mentors, students will also have opportunities to interact and collaborate with visiting faculty throughout the summer, reflecting a wide range of interests and approaches. 

The process of selecting mentors and projects begins once participants arrive at the Santa Fe Institute, guided by the SFI Education Team and UCR program directors. All candidates are asked to wait until then to make contact with SFI researchers.

Outcomes