MAAV: Object Detection
9/2025 - Present Tech Stack: Python, YOLO, Blender
I created an object detection algorithm that was ablet to detect objects from images.
To prepare for our upcoming drone competition, our drones need to be able to detect specific obstacles specified by the competition
within images taken mid-flight. To do this, our team decided to use pre-trained models, specifically a YOLO v11n model
that we custom-trained on our own images to better fit our needs. We chose YOLO due to their built-in ability to segment
objects by using bounding boxes. To boost our training efficiency, we took real-world images, but also created a virtual 3D
environment that simulated the competition airfield and generated thousands of Blender images that allowed us to effectively train
our model year-round. As of today, our model has a precision of around 75% and we are still training it to hopefully incrase performance.
7/2025 - 11/2025 Tech Stack: C++, Swift, Blender, Python
I created a C++ program that could find an efficient solution to the Rubik's Cube with a record time of 250 ms.
I utilized A* search and a custom heuristic function to search through different states of the Rubik's Cube, represented as nodes in a graph to find the solution.
In the end, I deployed the program as an webapp on macOS systems using Swift. In order to visualize the solution produced by the app, I also created a virtual 3D environment for a Rubik's Cube
using Blender and brought the cube to life using Blender's built-in Python API to program the cube to take in user
input and perform the necessary moves.
MAAV: Drone Image Mapping
3/2025 - 4/2025 Tech Stack: Python, OpenCV
I created an image stitching algorithm using Python and OpenCV.
By capturing many consecutive images of an airfield, I applied classic Computer Vision
techniques such as SIFT features and homography transformations to correctly orient each image.
Once all of the images were properly oriented in alignment with each other, I was able to iteratively
stitch the photos together on a unified canvas, allowing us to produce the proper stitched image.
Technische Universitat Berlin: Self-Balancing Segway
5/2024 - 8/2024 Tech Stack: MATLAB, Arduino
I spent 6 weeks building a robotic self-balancing segway using MATLAB and Arduino.
I Implemented predictive controls in Simulink and applied Kalman Filtering in order to iteratively update position.
This allowed our segway to re-orient itself given different parameters, such as the tilt angle of the body. By implenting
these features into our custom controller, the segway was able to stay upright for up to arond 5-10 seconds.
Michigan Data Science Team: Neural Networks from Scratch
1/2024 - 4/2024 Tech Stack: PyTorch, Python, pandas
I gained hands-on experience by creating a neural network from scratch, integrating Machine Learning concepts from
scratch such as backpropogation and feedforward algorithms. The entire model was coded using Python and pytorch, with
features like backpropogation being implemented using standard Python Numpy Math and matrix multiplication.
The model was trained on network data in order to detect network intrusion
and was able to correctly detect network intrusion with an accuraccy of up to 95%.
Michigan Data Science Team: Webscraping Project
9/2023 - 1/2024 Tech Stack: Selenium, Python, Matplotlib
I worked on a ecommerce webscraping project. The aim of this project was to scrape popular online shopping websites,
such as Amazon, in order to find the average rating and prices for certain objects. We were able to visualize these
metrics in a standard Python plot using Matplotlib and key in on items that were seemingly overpriced for their rating.
This allowed the user, to gague the price range that they should be targeting for the best valued product according to the search query.