Skills
My technical background connects data analysis, machine learning, NLP research, rigorous evaluation, and research engineering for applied AI systems.
Technical Background
My technical background spans data analysis, machine learning, NLP, and research engineering. My previous experience as a data analyst provided a strong foundation in Python, SQL, R, statistical modelling, machine learning, and experimental design, together with practical experience working with real-world data and evaluating analytical results.
During my MSc in Language Technology, I extended this foundation into NLP research, working with PyTorch and Hugging Face on transformer-based models, machine translation, large language models, information retrieval, and model adaptation methods such as LoRA and Mixture-of-Experts.
My research places a strong emphasis on rigorous evaluation. I have experience with statistical testing, bootstrap confidence intervals, ablation studies, error analysis, and human evaluation, using these methods to understand model behaviour and assess the reliability of experimental findings.
More recently, my work has moved toward data-centric research, including corpus analysis, data filtering, and training-data attribution with Shapley values. From an engineering perspective, I use Git, Linux, Docker, and HPC/GPU environments to support reproducible research and large-scale experimentation.
Data Analysis & Visualization
Statistical analysis, exploratory data work, and visualization for real-world datasets, experimental results, and decision support.
AI & Machine Learning
Deep learning, Transformer adaptation, fine-tuning workflows, and model evaluation for research and applied AI systems.
NLP & Language Technology
Multilingual NLP, machine translation, retrieval, and language-technology systems for specialized domains.
Responsible AI & Evaluation
Bias evaluation, human validation, statistical testing, and model behavior analysis for social and linguistic AI risks.
Data-Centric ML
Dataset diagnostics, filtering, attribution, and quality control for understanding how training data shapes model behavior.
ML Engineering
Research engineering skills for building reproducible model-training pipelines, experiments, demos, and deployments.
Applied AI Systems
User-facing AI tools, dashboards, and interactive prototypes that turn model outputs into usable workflows.