Evan Muzzall
Head of Software and Services for Data Science (SSDS)
Visit SSDS at: https://ssds.stanford.edu/
I help others become empowered through data to solve challenging problems.
I can help with:
- R/RStudio/Tidyverse, Python/Jupyter Notebooks, Bash, Git/GitHub, SQLite, Microsoft Excel, Qualtrics, Google Suite
- Data preparation: text, image, quantitative, machine/deep learning, data imputation
- Data visualization: ggplot2, geospatial mapping, matplotlib, seaborn, plotly, altair, geopandas, gnuplot
- Machine learning: caret, SuperLearner, H2O, scikit-learn, tensorflow, pytorch, keras, regression, classification, tree-based methods, confusion matrix derivations, cross-validation
- Deep learning: quantitative, text, image, MLP, GAN, RNN, CNN, LSTM, transfer learning
- Text: mining, classification, word embeddings, topic modeling (assisted/anchored/weighted/neural), sentiment analysis, semantic structure/analysis, large language models
- Unsupervised methods/dimension reduction: PCA, MCA, CCA, tSNE, UMAP, clustering
- API access, social network data, network analysis, webscraping
- Survey design and analysis, item response theory
- Reproducible research and machine learning pipelines
- Categorical data analysis
- Time series, forecasting
- Containers, Docker
- Music theory
- Bloomberg Terminal
Currently working on:
- Ecological effects of chlorinated water, disease, and conflict in Northwest Syria
- Health, agriculture, and armed conflict in Syria and Yemen
- Machine learning to better understand perioperative depression
- Post disaster environmental concern on Mauritius
- User experience research of text data mining platforms
Evan Muzzall
Head of Software and Services for Data Science (SSDS)
Visit SSDS at: https://ssds.stanford.edu/
I help others become empowered through data to solve challenging problems.
I can help with:
- R/RStudio/Tidyverse, Python/Jupyter Notebooks, Bash, Git/GitHub, SQLite, Microsoft Excel, Qualtrics, Google Suite
- Data preparation: text, image, quantitative, machine/deep learning, data imputation
- Data visualization: ggplot2, geospatial mapping, matplotlib, seaborn, plotly, altair, geopandas, gnuplot
- Machine learning: caret, SuperLearner, H2O, scikit-learn, tensorflow, pytorch, keras, regression, classification, tree-based methods, confusion matrix derivations, cross-validation
- Deep learning: quantitative, text, image, MLP, GAN, RNN, CNN, LSTM, transfer learning
- Text: mining, classification, word embeddings, topic modeling (assisted/anchored/weighted/neural), sentiment analysis, semantic structure/analysis, large language models
- Unsupervised methods/dimension reduction: PCA, MCA, CCA, tSNE, UMAP, clustering
- API access, social network data, network analysis, webscraping
- Survey design and analysis, item response theory
- Reproducible research and machine learning pipelines
- Categorical data analysis
- Time series, forecasting
- Containers, Docker
- Music theory
- Bloomberg Terminal
Currently working on:
- Ecological effects of chlorinated water, disease, and conflict in Northwest Syria
- Health, agriculture, and armed conflict in Syria and Yemen
- Machine learning to better understand perioperative depression
- Post disaster environmental concern on Mauritius
- User experience research of text data mining platforms
Education
- PhD, Anthropology, Southern Illinois University Carbondale
- MA, Anthropology, Wichita State University
- BS, Anthropology, Michigan State University
Selected publications
Click here to view peer-reviewed publications
Non peer-reviewed:
Weng A and Muzzall E. 2022. Working with messy time series data in Python Jupyter Book
Muzzall E and Weng A. 2022. Text Analysis and Machine Learning Jupyter Book
Muzzall E. 2017. Ensemble machine learning for sex prediction of a worldwide craniometric dataset
More about me
I am an instructor, researcher, and data scientist with 15 years’ experience across learning design and pedagogy, instruction, cross-functional collaboration for data science projects, project management, consultation, and mentorship. At Stanford, I lead a team that upskills and reskills students, faculty, and staff in group and personal settings (online and offline) to help them be successful in their computational research projects through workshops, consultations, specialized trainings, curriculum development, mentoring, and coaching using a variety of pedagogical and methodological resources that emphasize critical thinking and data skills.
Additionally, I cultivate successful cross-functional collaborations across campus as well as with external stakeholders to leverage data science and artificial intelligence to optimize workflows and processes for research teams. My personal research interests span machine learning applications to bioarchaeology and international conflict, teaching pedagogy, deep learning, and computational text analysis/natural language processing.
I earned my PhD in 2015 from the Southern Illinois University Carbondale Department of Anthropology under the guidance of Izumi Shimada and Robert Corruccini. Before joining Stanford, I was the Instructional Services Lead at the UC Berkeley D-Lab.
Visit my GitHub so see Google Scholar pages to view my work, workshop materials for R, Python, Bash, and SQL along with various projects, data, guitar tabulature, and other odds and ends. Support the Textile Makerspace!