Wolfram Summer Research Institute

A project-based program with Stephen Wolfram and the Wolfram team to launch your computational future

We encourage applicants from all backgrounds. Scholarships are available.

Overview

The Wolfram Summer Research Institute is a unique educational and career opportunity for innovators at all stages as well as students from postdoc to undergraduate. Learn the Wolfram approach to science and innovation by developing and completing an individual project under the guidance of Stephen Wolfram and the Wolfram team.

At the core of the program is an original research project. With mentorship from Wolfram experts, participants design, implement and publish work that reflects both their interests and Wolfram’s vision of computation as a universal tool for discovery. Projects often grow into academic publications, conference presentations or technologies with real-world impact.

Graduates join the Wolfram network of innovators and researchers, with opportunities for advanced study, collaborations, internships and continued engagement through programs like the Wolfram Institute. Whether you’re expanding your research, exploring bold ideas or connecting with leaders in computation, the Wolfram Summer Research Institute provides the structure, mentorship and community to help you succeed.

Find the Track That’s Right for You…

Foundational Science

Learn to use the computational paradigm to explore foundational questions across science.

Ruliology & Pure Computation

Study the computational universe of simple programs and their rich behavior.

Applications of Computation

Use the computational paradigm to move forward your chosen area.

Educational Innovation

Bring foundational thinking into the classroom.

Philosophy & Strategy

Apply foundational thinking to an abstract or concrete problem.

Featured Projects

Spatiotemporal Characterization of Protein Molecular Dynamics Trajectory

We use molecular dynamics simulation data of the CXCR7 protein. This protein is a G protein-coupled receptor (GPCRs), it scavenges chemokines and opioids, and recruit β-arrestins, so it is an atypical GPCR. For the protein helices, we extract the spatial coordinates of atoms of alpha carbon (“CA”) and monitor how their positions evolve over time using static and dynamic graphical analysis. In parallel, we compute pairwise contact distances between protein residues and analyze how these distances fluctuate throughout the simulation trajectory.

Ian Asaf Muñoz Granados

Class of 2025

Discrete SU(2) Gauge Theory via Hopf Bundles and Hypergraphs

In this project we expanded previous work related to discrete abelian gauge theories through the group SU(2). We examined the SU(2) Hopf bundle on S⁷ through the Hurwitz group, then conducted several simulations involving Wilson loops in two dimensions for both U(1) and SU(2) and studied their different behaviour on both usual lattices and hypergraphs.

Ioana Milea

Class of 2025

Generalized Self-Contacting Symmetric Fractal Trees in 3D

I identified four kinds of symmetric fractal trees in 3D that are determined by the type of expressions found in their boundary equations; these are trees with number of branches b=4n-1, b=4n, b=4n+1 and b=4n+2 where n takes the integer values from 1 to ∞. Several animations were produced when one walks around the critical boundaries of the parameter space, showing interesting dynamics and topological critical changes for certain angles.

Bernat Espigulé Pons

Class of 2013

Adaptive Evolution of Hypergraph Rewriting Systems

In adaptive evolution, a system is iteratively modified in order to optimize a given fitness function. This biologically inspired technique is commonly used to search for solutions in large, complex spaces. In this project, we investigate the adaptive evolution of hypergraph rewriting systems. Our goal is to discover rewrite rules that evolve a hypergraph for a finite number of iterations and then halt. As an extension, we explore the adaptive evolution of hypergraph multiway systems, aiming to identify rules that produce halting multiway graphs with the maximal possible number of nodes.

Anneline Daggelinckx

Class of 2025

Mining Pockets of Computational Reducibility with AI: Transformer Models of Graph Rewriting

In this work, a transformer neural network was trained on periodic and aperiodic graph rewriting systems to predict the next (or previous) step in their evolution, and to iteratively predict the entire forwards (or backwards) evolution of the system. A simple transformer was able to predict the bidirectional evolution of a periodic case with perfect accuracy, but prediction of an aperiodic case had almost no success. These findings may suggest that artificial intelligences based on current neural networks architectures are generally limited only to tasks also capable by humans, but not tasks completely beyond human capability.

Floe Foxon

Class of 2025

Searching for Holes in Proof Space

This report defines a hole for the proof space arising in multiway system graphs generated by string-substitution rules. A proof is a path from an initial string to a target string (the initial and final string is a theorem) in the graph. By contracting each proof to a single vertex and connecting two proofs when they differ in exactly one step (when they form a single diamond sub-graph), at the end it constructs a proof graph whose connectedness tells the fact of continuously deforming one proof into another. A hole is defined as follows: #Holes = #ConnectedComponents - 1. Adding new symbols or asymmetric rules quickly introduces holes.

Saúl Bernal

Class of 2025

Networks as Manifolds

The aim of this project is to identify the manifolds corresponding to networks that are generated by simple substitution rules from Stephen Wolfram’s physics project. At first glance, some of the networks resemble a cell complex of a known surface. The idea is to provide techniques to substantiate this impression and figure out the geometric structure that might underlie the purely combinatorially given network.