Data Scientist
Education
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M.S., Business Analytics |
Emory University (May 2024) |
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M.S., Information Technology |
Carnegie Mellon University (May 2023) |
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B.S., Software Engineering |
Adventist University of Central Africa (November 2021) |
Work Experience
Machine Learning Engineer Intern @ The Rwanda Space Agency (Summer 2022)
- Conducted exploratory data analysis on geospatial images to monitor and quantify deforestation and forest
degradation in remote areas of Rwanda.
- Optimized sequence deep learning models (RNN, LSTM, GRU) for image segmentation to achieve higher
accuracy and precision on geospatial images.
- Collaborated with team members to develop comprehensive reports and engaging presentations to effectively
communicate project findings and insights to stakeholders and team members.
Cofounder & Data Lead @ Extra Technologies Ltd (June 2018 - July 2021)
- Developed and led the implementation of a cooperative management platform that increased efficiency in
daily operations for over 16 cooperatives with 5000 members, resulting in a 15% reduction in overhead costs.
- Created analytics dashboards using PowerBi to track and analyze member performance for Tea Cooperatives.
Projects
Frame-Level Speech Recognition using Multilayer Perceptron
code
Overview
Developed an advanced frame-level speech recognition system using a Multilayer Perceptron (MLP).
This project focused on transforming raw Mel Frequency Cepstral Coefficients (MFCCs) into precise phonetic transcriptions at the frame level.
Key Responsibilities
- Engineered an MLP model to learn feature representations and establish a non-linear classification boundary for speech recognition.
- Employed cross-entropy loss to minimize the discrepancy between the predicted outputs and the target phoneme class labels.
- Utilized gradient descent algorithms for neural network parameter optimization, aimed at reducing the overall cost function and enhancing model precision.
Achievements
- Successfully developed a neural network capable of distinguishing between diverse phonetic sounds in speech.
- Achieved a high model accuracy with a score of 0.87693, demonstrating advanced proficiency in speech processing and machine learning techniques.
Face Classification & Verification using Convolutional Neural Networks
code
Overview
- Implemented a ResNet architectures for face verification, focusing on constructing effective convolutional neural networks and generating discriminative, generalizable feature representations.
- Implemented a face classifier to extract and analyze facial features (like skin tone, hair color, nose size) from images, converting them into fixed-length feature vectors or face embeddings.
- Designed a verification system to compute the similarity between feature vectors of two images, determining if they represent the same person.
Key Achievements
- Successfully trained a model that can distinguish facial features with high accuracy, thereby enhancing the performance of face verification tasks.
- Developed and optimized a model capable of effectively comparing feature vectors to generate a similarity score, crucial for accurate identity verification.