Ariane Shimirwa
Graduate Teaching Assistant at Carnegie Mellon University
Information Technology at Carnegie Mellon University Africa
Rwanda
Hi, I'm Ariane Shimirwa!
Graduate Teaching Assistant at Carnegie Mellon University
A passionate Data Scientist and Applied Machine Learning Engineer with experience in performing data analysis and creating machine learning models using Python. I am eager to apply my skills and experience in Data Science, Machine Learning, AI, GIS, and Earth Observation to solve complex challenges and contribute to Africa's sustainable development.
Experience
Carnegie Mellon University
Graduate Teaching Assistant
January 2024 - Present
- Support students in understanding class materials
- Prepare recitations and assignments, along with solutions for assignments
- Hold office hours to answer the student's questions and help them with assignments
- Grade homework assignments and assign grades
- Work hand in hand with the professor to enhance student experience in the course
Courses: Planning for Digital Transformation & Project in AI for Healthcare Courses details: https://www.africa.engineering.cmu.edu/academics/courses/04-801-S3.html & https://www.africa.engineering.cmu.edu/academics/courses/04-801-T4.html
Graduate Teaching Assistant
August 2023 - December 2023
- Supported the instructor with class preparation and delivery
- Responded to student concerns or queries relating to the class materials
- Prepared recitations and assignments, along with solutions for assignments
- Held office hours to answer the student's questions and help them with assignments
- Graded homework assignments and assigned grades
Course: Data, Inference, and Applied Machine Learning Course details: https://www.africa.engineering.cmu.edu/academics/courses/18-785.html
Nithio
Data Science Intern
May 2023 - December 2023
- Performed monthly data analysis for various operators, encompassing data modeling, predictive analytics, and dashboard updates, ensuring timely and accurate data representation
- Played a pivotal role in the development of a robust, generalized prediction model. This model was designed for high adaptability and performance, capable of assimilating and learning from diverse data sets across different operators
GIZ Rwanda– Digital Transformation Centre Kigali
AI HUB Associate Consultant
May 2023 - September 2023
- Provided expert consultation on the implementation of Artificial Intelligence (AI) within governmental projects to enhance performance and deliver superior results
- Facilitated training sessions, workshops, and events to educate stakeholders on the advantageous use of machine-learning techniques applied to Earth Observation data and started the Machine Learning for Earth Observation Network in Rwanda
- Offered crucial support for the public transport optimization phase of the Smart Mobility project in Rwanda, utilizing data analysis/AI to improve efficiency and streamline operations
University of Rwanda, Directorate of Teaching and Learning Enhancement
Data Analyst Intern
December 2019 - February 2021
- Ensured 100% accuracy of data collection and data reporting
- Performed statistical analysis using R and Python programming ensuring the accuracy of the analysis and delivery of a high-quality report
- Conducted weekly data analysis sprints which contributed to decision-making and reporting to the head office
Education
Information Technology
Carnegie Mellon University Africa
Graduated in 2024
Certificates & Badges
No certificates or badges added
Projects
Data Analyst
This was my practicum(final year) project. This study was al about analyzing the impact of internet shutdowns on economic activities in various countries, using nighttime light data collected from NASA as a measure. Key indicators such as SOL, GDP, and FDI were analyzed using statistical analysis and predictive modeling. GIS tools were also used in the analysis.
This research project addressed the global public health challenge posed by non-communicable diseases (NCDs), particularly in low- and middle-income countries. It proposes an AI-based mobile platform that utilizes clinical data and machine learning algorithms such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), to predict risks of NCDs like cardiovascular diseases and type 2 diabetes. Additionally, the mobile application aids users with high-risk assessments in scheduling professional medical consultations.
This was a class research project that aimed to develop a model for classifying water quality for human consumption using machine learning and AI techniques with the exploration of both supervised and unsupervised learning algorithms.
Languages
English
Professional
French
Intermediate
Skills
Python
Data Analysis
Research
GIS Application
Developing Machine Learning Algorithms
AI / ML Application
GitHub
Ubuntu Linux
Working with Big Data Tools
SQL Database
AWS (Amazon Web Services)
Teamwork
Communication
Critical thinking
Problem Solving
R Language
Azure
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