ERC - HAMMER Digital Twin Networking
Creating a digital twin backend Fall 2023 - Fall 2024
- Used Golang for high quality, fast data processing.
- Created an online geometry viewer that communicates with a cloud server and visualizes data present. Click here for a simplified beta!
- Single-handedly discovered and listed cybersecurity concerns, kept a running list so multiple University administrators could be on the same page.
- Implemented many networking features, like routing without a framework (I followed many online tutorials, but made something for our specific use case)
- Created python scripts for automated uploads/downloads/processing.
- Utilized Docker to create containerized applications (containerization helps with performance and storage)
- Used Github actions for automated CI/CD.
- Fun fact: This portfolio you are looking at uses github actions for automated CI/CD too :)
- Implemented secure access through two-factor authentication.
- Created a self-hosting solution using tunneling protocols to connect two universities together.
- Led a team of eight, delegated tasks between two undergrads (including myself), two masters students, two faculty members and two PhD students
- Organized my team in an organization with over 30 members across four Universities.
- Used multiple cloud technologies, such as AWS, Google Cloud, and Microsoft Azure.
- Created multiple benchmarks to compare pricing, security concerns and performance to stakeholders.
Using my self-hosting tunneling solution, latencies ranged from 60ms to 100ms.
Mutli Panel Display VR development
Applications created for Head-mounted displays and Multi-Panel* displays
*Basically, think combining 30 monitors & TVs together, and having them all synced as one display. Like those big Nasa ones.
Summer 2021 - Fall 2024
- Maintained over 500,000$ worth of VR equipment, was on-call to make sure that it worked.
- I handled 100% of the programming, bugtesting, and implementation of over 40 high quality data science applications.
- Frequently used software like CloudCompare for optimization of pointclouds.
- Taught over 10 students how to develop these applications .
- Created documentation and training materials for my lab for over 30 students, many of them without technical backgrounds.
- Presented over 60 times unscripted, with live technical demos. Presented to industry leaders (Rockwell Automation, Meta, Google, University faculty from across America).
Self-taught C#, C++, Unity Engine, Unreal Engine, CloudCompare, Meshlab, MatLab, Python.
Ask about things I had to learn for developing on old, buggy hardware and optimization tricks I used!
Examples of applications developed (Feel free to ask about more!)
- Created interactive 3d graphs that users could
- Used a Downtown Greensboro pointcloud scan to create an immersive tour.
- Used generalized Pointcloud scans for accurate real-time distance tracking in VR.
- Created a realistic human skeletal and digestive system demo with animations.
- Created real time physics simulations of concrete mixing with fluid dynamics.
- Created an educational human anatomy demo with text-to-speech that could dynamically translate languages in real-time.
- Helped develop a farm
High performance computing for AI systems
A small project attempting to fill in performance gaps.
Currently, many AI systems use slower technologies and methods for AI development.
Examples:
Python is seen as the go-to for many data processing steps. This is fine, however many studies have shown that Python is slow compared to other programming languages.
After looking at the literature, many steps of the AI development cycle is spent on extremely expensive computation. What if there's a way to speed up these steps?
Many AI models use reduced-order modelling to speed up computation at the cost of accuracy. This is infeasible in areas that require high accuracy AND speed (autonomous driving vehicles, medical devices, stock trading).
- Began learning rust, a mathematically focused programming language focused on performance and memory safety.
- Came up with an ambitious plan to develop an image processing library that's able to implement something similar to YOLO for Python in Rust.
- Identified a need for fast AI models to be used on weaker hardware (portable systems, such as self-driving cars).
Status:
Shelved during Fall 2024. This was due to the complexity of the process involved.
As of Winter/Spring 2025, I have to learn a LOT about low-level design. My progress has been a little slow, but I'd love to speak further.
