🎓 B.Sc. in Computer Science · M.Sc. Student in Computer Engineering
I build and evaluate machine learning systems. Training a model is the easy part; the harder question is when and why a given architecture fits the problem and the data.
Recent work includes speech and audio classification, comparative studies of CNN and attention-based models, and applied deep learning experiments. I report results with cross-validation, confusion matrices and significance tests rather than a single best number.
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Deep learning CNN, attention-based and sequence models for audio and vision |
Rigorous evaluation Cross-validation, confusion matrices, significance testing |
Engineering Moving experiments from notebook to reproducible pipelines |
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Core
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Applied
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I want to build systems that are accurate and also understood, where each architectural decision is backed by a clear evaluation. In practice that means rigorous comparisons, reporting what didn't work alongside what did, and steadily improving both my ML intuition and my engineering habits.
I'm open to ML engineering and research roles. The fastest way to reach me is email or LinkedIn.
