Felix Nshuti
Teaching Assistant at Carnegie Mellon University
Kigali, Kigali City, Rwanda
Summary
🤩
Rockstar🎓
Top SchoolFelix Nshuti is an ECE-trained machine learning engineer and teaching assistant at Carnegie Mellon with six years of experience building ML systems that bridge low-level systems engineering, compilers, and computer architecture. He has contributed to open-source projects like ivy (model-conversion tooling) and sktime (time-series ML), where he implemented GRU classifiers, fixed edge-case bugs, and refactored core gradient utilities—work that combines practical engineering with attention to reproducibility and documentation. A former GSoC mentor and DeepLearning.AI tutor, he pairs hands-on coding and community leadership, having led a team developing a NumPy frontend and supported contributors daily. Based in Kigali, he brings a researcher’s rigor and systems-level perspective to production-focused ML tooling, with a knack for clarifying complex APIs and improving maintainability.
5 years of coding experience
1 year of employment as a software developer
Bachelor of Technology - BTech Computer Engineering, Bachelor of Technology - BTech Computer Engineering at Pandit Deendayal Energy UniversityAdvanced Diploma (Dropout) Biomedical Technology/Technician, Advanced Diploma (Dropout) Biomedical Technology/Technician at Rwanda Polytechnic, IPRC KigaliMaster of Science - MS Electrical and Computer Engineering, Master of Science - MS Electrical and Computer Engineering at Carnegie Mellon University
Github Skills (16)
scikit10
forecasting10
machine-learning10
time-series10
forecast10
gru10
python10
data-science10
classification10
scikit-learn10
numpy9
documentation9
debug9
pandas9
pytorch8
Programming languages (6)
Github contributions (5)
sktime/sktime
Feb 2024 - Mar 2025
A unified framework for machine learning with time series
Role in this project:
Data Scientist
Data ScientistContributions:72 reviews, 23 PRs, 1 push in 1 year
Contributions summary:Felix primarily contributed to the sktime repository by fixing bugs and implementing changes to the time series classification and forecasting models. Their work included resolving issues related to the `ForecastX` and `HolidayFeatures` components, and ensuring compatibility for edge cases. They also added a new implementation for GRU-based classifiers and adapted `darts` regression models. The user has demonstrated a focus on enhancing and maintaining the core functionality of the library, especially in the area of time series analysis.
forecastingtime-series-analysistime-series-regressiondata-sciencedeep-learning
ivy-llc/ivy
Jul 2022 - Jan 2023
Convert Machine Learning Code Between Frameworks
Role in this project:Back-end Developer
Back-end DeveloperContributions:702 reviews, 235 commits, 851 PRs in 6 months
Contributions summary:Felix primarily focused on reformatting existing code and fixing documentation typos. They were involved in reformatting the adam_step function within the gradient module. Additionally, they corrected documentation typos related to gradients and data type. They also worked on refactoring methods such as `sinh`, `cos`, and `is_variable`.
pythontensorflowframework-learningtemplatedata-science
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