Michael Masenheimer
COMP SCI '27 @ U OF ARIZONA.
MACHINE LEARNING RESEARCH.
COMP SCI AMBASSADOR.
MAKERSPACE TECHNICAL LEAD.
I am an aspiring software engineer studying Computer Science, with recent experience in undergraduate research and client-based software development. I have worked on applications ranging from Spotify clones and airline metadata systems to generative visual art and AI engines. My current focus lies in full-stack engineering and machine learning.
GitHubWhat I Bring to the Table
I have experience in several factions of software development. In school I study Python, Java, MIPS assembly, web dev, discrete math, and linear algebra. Through coursework, projects, and work experience, I’ve learned how to pick up new programming languages, understand logical systems, and solve problems effectively. I like to approach projects with a collaborative and growth-centered mindset.
I also work at the University of Arizona’s student makerspace, where I’ve learned to program Raspberry Pi and Arduino devices. This fall, I plan to lead coding and circuit workshops through the makerspace. I also volunteer as a Computer Science ambassador, which has helped strengthen my communication and collaboration skills. My goal is to make every student feel supported and encouraged in their CS journey.
Technologies
- Java
- C++
- Python
- Unix/Linux
- Docker
- HTML/CSS
Featured Research Project
The Large Hadron Collider (LHC) is the world's largest particle
accelerator, run by a nuclear physics lab near Geneva called CERN. My
research is through the
ATLAS Experiment, a
particle collision detector on the LHC.
I investigate
hardware-based machine learning for the trigger systems on ATLAS,
using convolutional neural networks in C++. Through
high-level synthesis tools like
hls4ml and PyTorch/TensorFlow, I’m exploring
real-time decision-making between AI engines and the collider’s
current digital signal processors.
Tech stack
- C++
- PyTorch
- Docker
- AMD Versal AI Engines
- Vitis IDE
- Git
Project type
Machine Learning and Artificial Intelligence
Timeline
April 2025 - Present
Projects
These projects showcase my journey in computer science through coursework, research, and professional development. Each one includes a visual preview, and full sites are accessible through the links provided. As summer and fall progress, I’ll continue updating this section and my GitHub with new projects.
Spotifly
Spotifly
In this project, I worked in Java to enhance my skills in OOP, particularly polymorphism. Spotifly functions similarly to Spotify on the backend, but without the user interface.
I used encapsulation for security and inheritance to design a local user database within the Java file, incorporating features such as login with backend validation, playlist creation, and song playback.
Spotifly RepositoryMarkov Chain Algorithm
Markov Chain Algorithm
This program generates random text using a Markov algorithm based on an input text file.
It works by constructing a custom hash table mapping word prefixes to a list of words that generally follow the prefix based on the text. The program then uses this table to generate new, random sequences of words by selecting a prefix and appending random suffixes based on the stored relationships.
Markov Chain RepositoryAutomatic Hydroponic Garden
Automatic Hydroponic Garden
During Spring 2025, I interned for the U of A makerspace, CATalyst Studios. Our intern cohort decided to make a hydroponic garden. I used a RaspberryPi and Python to automate the watering process.
I used a Pi5 along with relays, breakout boards, clock modules, lcd screens, and water level sensors to create a UI, displaying messages about the current status of the pump and to control the flow of water.
Read More!Voxel.jp
Voxel.jp
When I first started learning how to code, I discovered a JavaScript library called p5.js, which was designed for generative art. I learned basic geometric algorithms and pseudo JS syntax.
Each small p5 project I posted to an Instagram page called "voxel.jp" among smaller projects/ideas. Some examples include flow fields, oscillating spheres, and slime molds.
Voxel.jpHuffman Tree Decoding Algorithm
Huffman Binary Tree Decoder
This is an algorithm to reconstruct binary trees from preorder and inorder traversals, optomizing data processing through decoding variable-length codes.
I used a recursive-based solution to decode sequences of bits, leveraging depth-first traversal for efficient processing to output the tree's postorder traversal and the decoded sequence.
Huffman RepositoryThis Website!
This Website!
This is the first complete website I've built and hosted. I utilized pure JavaScript, HTML, and CSS to define the UI and interactivity of the site.
It features various portfolio-esque sections, lazy loading, optomized viewport coverage for variable sized screens, and an interactive nav bar (for mobile users).
Portfolio RespositoryGet In Contact
I'm working to build a network of like-minded people who share a passion for software engineering and machine learning. I love discussing projects, goals,and ideas. Please don’t hesitate to reach out, I’d love to connect!