Nadia Parsa
Pamplona
Summary
Master's in computer engineering with academic experience in Explainable Artificial Intelligence (XAI) and deep learning. Knowledge in machine learning, fuzzy logic, and frameworks such as TensorFlow, PyTorch, and Scikit-learn. Skilled in NLP, computer vision, and data processing. Strong technical and analytical abilities applied in projects, with the capacity to clearly communicate results.
Overview
4
4
years of professional experience
6
6
years of post-secondary education
1
1
Certification
Work history
Master's Curricular Research and Innovation
Public University of Navarra (UPNA)
09.2024 - 02.2025
- Researched attribution methods for interpreting deep neural networks using fuzzy rule-based classifiers. Implemented and evaluated advanced techniques in Python, utilizing PyTorch, TensorFlow, Scikit-learn, LIME, SHAP, and Captum.
Quality Assurance Intern
To Next (IT company)
04.2021 - 07.2021
- Executed system tests and flagged coding issues to improve development outcomes. Created prototypes and liaised with clients to align tech features with business needs.
Education
Master's - Computer Engineering
Public University of Navarra (UPNA)
09.2022 - 02.2025
Bachelor's - Computer Science
Kabul University
04.2016 - 12.2019
Skills
- Programming & Frameworks: Python, MySQL, TensorFlow, PyTorch, Scikit-learn, Keras, LIME, SHAP, Captum
- Machine Learning Techniques: Supervised learning, unsupervised learning, deep learning, Explainable AI (XAI), Natural Language Processing (NLP), Computer Vision
- Data Management & Processing: Pandas, NumPy, data cleaning, feature engineering, visualization (Matplotlib, Seaborn)
- Tools & Platforms: Git, Linux, Jupyter, PyCharm
- Research & Communication: Experimental design, literature review, academic writing, technical presentations
Certification
- Machine Learning Specialization – Andrew Ng, DeepLearning.AI & Stanford Online | Coursera | 2025
- Deep Learning Specialization – Andrew Ng, DeepLearning.AI | Coursera | 2025
- Explainable AI (XAI) Specialization - Duke University | Coursera | 2025
IDIOMS
Spanish | B2
English | C1
Persian | Native
Projects
Master Thesis: “Implementing And Evaluating Fuzzy Rule-Based Explainer Systems for Deep Neural Networks: From Local Explainability to Global Understanding”, Implemented a fuzzy if–then rule system for classification tasks by leveraging important features identified from deep neural network decisions. Designed the system to mimic DNN performance while providing both local and global explainability and conducted analysis of its limitations. Technologies: Python, PyTorch, TensorFlow, Scikit-learn, SHAP, and Captum, Bachelor Thesis: “Persian News Article Summarization Using Deep Learning”, Developed an automatic summarization system for Persian news articles using an RNN-based sequence-to-sequence model with attention. Applied NLP techniques including tokenization and word embeddings, and trained the model to generate concise, coherent summaries while preserving key information. Evaluated performance using standard metrics. Technologies: Python, TensorFlow, NLTK, HAZM
References
References available upon request.
SOFT SKILLS
- Analytical and problem-solving ability
- Effective collaboration in multidisciplinary teams
- Clear communication and results presentation
- Flexibility and willingness for continuous learning
Timeline
Master's Curricular Research and Innovation
Public University of Navarra (UPNA)
09.2024 - 02.2025
Master's - Computer Engineering
Public University of Navarra (UPNA)
09.2022 - 02.2025
Quality Assurance Intern
To Next (IT company)
04.2021 - 07.2021
Bachelor's - Computer Science
Kabul University
04.2016 - 12.2019
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