Overview
Conducted research under a scientific initiation grant focusing on explainable facial
recognition solutions.
This internship was part of my Master's thesis research and contributed to advancing the
field of interpretable AI in computer vision.
Key Responsibilities
- Developed and implemented explainable AI algorithms for facial recognition systems
- Conducted extensive literature review on state-of-the-art explainable AI techniques
- Collaborated with research team on novel approaches to facial recognition
transparency
- Prepared and submitted research findings to an international conference
- Documented research methodology and results for academic publication
Key Achievements
- Published Research: Successfully published a scientific article to
ICCV (International Conference on Computer Vision)
- Technical Innovation: Contributed to advancing transparency in AI
facial recognition systems with the introduction of two innovative explainable AI
solutions for facial recognition: MaskSiamese and ProtoSiamese
- Academic Recognition: Research work recognized with a final
dissertation grade of 19/20
Technologies & Tools Used
Python
PyTorch
TensorFlow
NumPy
Research Impact
The research conducted during this internship has contributed to the broader
understanding of
explainable AI in facial recognition systems. The published work at ICCV represents a
significant
contribution to the field and demonstrates the potential for creating more transparent
and
trustworthy AI systems in computer vision applications.
Skills Developed
- Advanced machine learning and deep learning techniques
- Explainable AI and interpretable machine learning
- Computer vision and image processing
- Research methodology and academic writing
- Scientific paper preparation and publication
- Collaboration in research environments
- Critical analysis of state-of-the-art literature