Felipe González-pizarro is an NLP Data Scientist with 11 years of experience and dual MSc degrees, most recently completing a focused MS in Computer Science at UBC specializing in NLP and multimodal machine learning. He builds and evaluates textual and multimodal topic modeling algorithms and has applied large vision-language models to detect harmful content, producing datasets and peer-reviewed publications from institutions like Max Planck. Felipe has moved research into practice—implementing hallucination detection, prompt engineering and LLM fine-tuning in industry roles—and currently contributes NLP expertise at Ontopical after a stint at Rashi. He also has a strong human-centered angle from his HCI and info-vis work (e.g., TopicVisExplorer) and years of teaching experience across machine learning, NLP and algorithms. Comfortable straddling research and productization, he brings a track record of releasing resources (a 92K-image dataset) that accelerate safer multimodal content analysis.
11 years of coding experience
5 years of employment as a software developer
Master of Science - MS Computer Science, Master of Science - MS Computer Science at The University of British Columbia
Exchange Experience Master Computer Science and Engineering, Exchange Experience Master Computer Science and Engineering at Politecnico di Milano
Master of Science - MS Computer Science, Master of Science - MS Computer Science at Universidad Tecnica Federico Santa Maria
web-based interactive visualizations of LDA-generated topics to sup-port human interpretation of multi-corpora comparison
Contributions:2 PRs, 177 pushes, 1 branch in 1 year 4 months
corporaldasupcomparisonweb-based
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