Wolfram High School Summer Research Program
A project-based research opportunity for motivated high-school students to move beyond the cutting edge of computational thinking and artificial intelligence.
The Wolfram High School Summer Research Program is an intensive two-week program designed to advance high-school students' programming and problem-solving skills. Through a curriculum of active-learning activities, hands-on workshops and lectures, students explore the power of modern computation and deep dive into STEM fields while gaining mastery of Wolfram Language, computational thinking and research skills.
Under the guidance of expert mentors, students research and implement solutions to cutting-edge problems selected in collaboration with Stephen Wolfram. Projects are novel contributions to the field and are personalized to the students' interests and skill sets. Each student writes a computational essay and an interactive research paper and publishes their work at the end of the program. Successful projects can be submitted to STEM competitions, turned into academic papers or presented at the Wolfram Technology Conference.
This program was brilliant for research, and I got to meet so many great, qualified people here at Wolfram. From selecting my project topic with Stephen Wolfram to talking with my mentor about technical concepts and my college trajectory, I gained so much knowledge from this program.
This was not only an academically enriching experience but also an introduction to an incredibly bright community of dedicated, driven and kind people. Perhaps even more important than the practical skills gained and excellent work that each student came away with from their projects, this program was an opportunity to connect like-minded and truly passionate students and experts from around the world and develop lasting connections.
I'm so glad I was given this experience. I now know that it's very possible to explore on your own and make your own projects. We were given the opportunity to talk with so many knowledgeable people who answered so many of our questions, no matter how technical or philosophical.
After the summer, successful students enter our ecosystem of education opportunities. This may include doing an advanced project at the Wolfram Emerging Leaders Program, joining our teaching team, connecting with professional mentors or engaging with fundamental physics and metamathematics research at the Wolfram Institute. Particularly successful students are invited to complete internships at Wolfram Research.
We are seeking motivated high-school students interested in solutions-driven research and creating innovative technology. As we are committed to enabling ambitious students, regardless of background or resources, we provide needs-based scholarships and offer a pre-programming workshop for students with limited coding experience.
Generating animated guitar tutorials
My project uses chords or MIDI files as input and creates an animated tutorial video. My implementation finds the pitches that match a chord on the fretboard in a localized area. To account for some commonly used patterns, I use the CAGED system on guitar. This project also includes an implementation of major scale patterns and tab sheet implementation.
Class of 2023
Multiway sequential cellular automata
The study of cellular automata is useful for modeling many evolving systems. In my project, I examine a novel variant of cellular automata that uses sequential updating with multiple sets of rules, resulting in multiway sequential cellular automata. This structure has the potential for modeling many aspects of quantum mechanics, including possibly quantum spin chains. A deeper exploration highlights the impact of specific subsets of multiway circular sequential cellular automata rules on the directionality, behavior, symmetry and causal invariance of the resulting states graphs.
Class of 2022
Generate Super Mario Bros. levels
Super Mario Bros. is one of the top-selling video games of all time and is known for its excellently designed platforming levels, which pioneered the platforming video game genre. My project uses a convolutional neural network to help determine whether an array is a Mario level and to generate them using levels from Super Mario Bros. and its sequel Super Mario Bros.: The Lost Levels.
Class of 2022
Implementing counter machines
The objective of this project was to implement counter machines in Wolfram Language and establish which counter machine was the most unpredictable. I designed a general counter machine function and used this function to demonstrate five types of counter machines. I determined which counter machines were unpredictable and explored complexity by adding more registers.
Class of 2021
Building a graph-based reaction network
When performing syntheses in a lab, it can be challenging to find the shortest path and to avoid exceptions and pitfalls. A computational system for predicting synthesis pathways can help take the burden off of a chemist and can allow for more consistent results. By transcribing common reactions computationally and applying them recursively, a network can be generated to inform decisions in the lab. My project focused on building the groundwork for this in Wolfram Language, providing informative and computationally significant results and paving the way for comprehensive computational synthesis design.
Class of 2021
Automatic Metrical Scansion of Latin Poetry in Dactylic Hexameter
Many significant works of Latin poetry follow the format of dactylic hexameter, meaning that each line is composed of some combination of six metrical feet, each of which is either two long syllables or a long syllable followed by two short syllables. Scansion is the process of identifying the pattern of syllable lengths. Through this project, I use machine learning to scan lines of Latin poetry in dactylic hexameter.
Class of 2019
Colorful Fraud: Exposing Vulnerabilities in Neural Networks
In a day and age where many consider deep learning an off-the-shelf solution to any and all classification/prediction problems, it's important that people examine whether their neural network models are vulnerable to targeted attacks. This project implements a framework for generating adversarial examples: input data crafted to cause the neural network to produce unexpected or targeted incorrect behavior.
Class of 2019