🔭 What I work on & explore:
- Building end-to-end data and ML systems and understanding how they behave in real-world settings
- Looking at what happens to models when data is noisy, incomplete, or shifts over time
- Studying failures and robustness, especially in security-related ML systems
- Exploring how representations and embeddings change across different data modalities
- Using generative models to improve data diversity and test model robustness
- Thinking beyond accuracy and focusing on reliability and real-world behavior
🧪 Experience highlights:
- Mitacs Globalink Research Intern (Canada): worked on robustness and generalization in phishing detection systems under evolving attacks
- Assistant Data Engineer: built Python and SQL pipelines, handle data validation and transformations, generate automated Excel reports, and experiment with AI-assisted reporting
- Undergraduate thesis: explored how visual information can condition music generation in a multimodal setting
🧭 How I like to work (before starting any task):
- I first try to clearly understand the problem and the data involved
- I prefer building simple, modular systems to see how decisions affect the full pipeline
- I iterate through small experiments, learning from failures and refining the approach
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