Ahmad Mustapha

Aspiring Tech Lead

My Story

Professional Me

Hello! I am Ahmad Mustapha an ML/DL expert, I've led diverse projects like detecting vehicles in African reserves, analyzing traffic in Dubuque City, predicting illnesses from X-ray images, and generating humorous images. Proficient with Scikit-learn, Pandas, NumPy, R, and Python, I manage various data types seamlessly. Skilled in model development and deployment using Python Flask and Dash, complemented by strong Java development. Well-versed in project management tools (Jenkins, Jira) and practiced in Agile methodologies. Committed to staying current with emerging technologies through continuous research and ideation.

Tech Stack

Featured Projects

Vehicle Detection In The Wild

A freelancing project to detect vehicles in the wild by processing aerial imagery taken from drones. Different computer vision models where used. The final model was based on the YOLO model. Data annotation was done using OpenCV CVAT Pytorch was used for modeling.

Sickness Xray Based Detection

Used several convolutional neural networks CNNS model to predict the sickness of patients. Tried Alexnet, Resnet, Densnet, and several custome models. The work opened a research oppurtinuty to explore how such models not only predict sickness but also detects the patients protected charecteristecs like sex, age, and race. We found that although networks can predict such charactersitics but they doesn't baise on them while predicting sickness.

Github

Text Segmentation Baseline

N-gram based machine learning text segmentation applied over islamic hadith to predict the split between sanad and maten parts. Islamic ahadith are historical narrations that describe the acts and statements of Prophet Mohammad (PBUH), his household, and his Companions. Formulated a new entropy based segmentation technique. Used Python, Requests, BeautifulSoup.

Github

Dubuque City Traffic Monitoring

Developed a full data application that routinely featchs data from the clients API and dump them processed in database. Different machine learning algorithems (LSTMS, SVMs, Knns, Kmeans, ...) are then executed and the resulting models are finally served using Flask. A unifiying dashboard was developed to monitor the data, visualize statistics anad ML summaries.

Unsupervised Deep Learning

Developed a research framework to understand the representation quality of several Unsupervised Deep Learning Models. We are talking about models that learns without lables using clustering, entropy, rotation, masking to generate from-data groundtruth. The research also tackled how to optimize them from efficancy perpective without trading off performance. The research was acumulated to serve as my master dissertion.

Github

Point In Polyhedron

A CUDA implementation of the Point In Polyhedron (PIP) problem. PIP is an important problem that have a number of applications in different domains such as computer graphics. In this project. I combine and re-implement two research papers trying to parallelize the procedure on GPU using CUDA C.

Github

Gesture Wars

An overnight project that controles a simple PyGame game using hand gestures. The project user-details Google's Mediapipe handmarker to read live hand gestures from the camera and then fires PyGame events according to the hand shape. We have three gestures: thumb-up straight, thumb-up left, thumb-up right, and thumb-down shoot.

Github
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