Research Bio
Fernando Pérez's research focuses on creating tools for modern computational research and data science across domain disciplines, with an emphasis on high-level languages, interactive and literate computing, and reproducible research. Through tools like IPython and Project Jupyter, he builds foundational blocks that enable scientists to tackle all stages of computational research (from exploration through publication) with a coherent approach, thus improving scientific productivity, collaboration and reproducibility.
Research Expertise and Interest
data science, Educational Data Science, scientific computing
In the News
Berkeley Launches Agile Metabolic Health and Open Platforms Initiative
New UC Berkeley Center Will Apply Data Science to Solving Environmental Challenges
Teaching
Individual Study Leading to Higher Degrees [STAT 299]
Supervised Research: Interdisciplinary Studies [UGIS 192E]
Reproducible and Collaborative Statistical Data Science [STAT 159]
Reproducible and Collaborative Statistical Data Science [STAT 259]
Directed Study for Graduate Students [STAT 298]
Individual Study Leading to Higher Degrees [STAT 299]
Supervised Research: Interdisciplinary Studies [UGIS 192E]
Individual Study Leading to Higher Degrees [STAT 299]
Individual Study Leading to Higher Degrees [STAT 299]