Evan Muzzall

Head of Software and Services for Data Science (SSDS)

Evan Muzzall

https://ssds.stanford.edu/

I am a social data scientist and digital humanist with experience in pedagogy and curriculum development, instruction, instructor coaching, consultation, mentoring and professional development, and collaborative interdisciplinary research projects. I support the computational research pursuits of students, librarians, faculty, and staff using a variety of pedagogical and methodological resources that emphasize critical thinking and data skills.

My research interests span functional biomechanics, mortuary archaeology, microbiology, teaching computational text analysis in digital humanities contexts, machine learning applications to bioarchaeology and international conflict, the science of science, deep learning, and natural language processing/computational text analysis.

Currently working on:  

  • Ecological effects of chlorinated water, disease, and conflict in Northwest Syria
  • Machine learning to better understand perioperative depression
  • Attitudes about envronmental catastrophes in the Mascarenes
  • Health, agriculture, and armed conflict in Syria and Yemen
  • Postoperative complications in young female diabetics

I can help with:

  • R/RStudio/Tidyverse, Python/Jupyter Notebooks, Bash, Git/GitHub, 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 (lm, glm, penalized, step, spline, hinge), 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
  • Unsupervised methods/dimension reduction: PCA, MCA, CCA, tSNE, UMAP, clustering
  • API access, social network data, webscraping
  • Categorical data analysis
  • Time series, forecasting
  • Survey design and analysis
  • Bloomberg Terminal

Evan Muzzall

Head of Software and Services for Data Science (SSDS)

https://ssds.stanford.edu/

I am a social data scientist and digital humanist with experience in pedagogy and curriculum development, instruction, instructor coaching, consultation, mentoring and professional development, and collaborative interdisciplinary research projects. I support the computational research pursuits of students, librarians, faculty, and staff using a variety of pedagogical and methodological resources that emphasize critical thinking and data skills.

My research interests span functional biomechanics, mortuary archaeology, microbiology, teaching computational text analysis in digital humanities contexts, machine learning applications to bioarchaeology and international conflict, the science of science, deep learning, and natural language processing/computational text analysis.

Currently working on:  

  • Ecological effects of chlorinated water, disease, and conflict in Northwest Syria
  • Machine learning to better understand perioperative depression
  • Attitudes about envronmental catastrophes in the Mascarenes
  • Health, agriculture, and armed conflict in Syria and Yemen
  • Postoperative complications in young female diabetics

I can help with:

  • R/RStudio/Tidyverse, Python/Jupyter Notebooks, Bash, Git/GitHub, 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 (lm, glm, penalized, step, spline, hinge), 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
  • Unsupervised methods/dimension reduction: PCA, MCA, CCA, tSNE, UMAP, clustering
  • API access, social network data, webscraping
  • Categorical data analysis
  • Time series, forecasting
  • Survey design and analysis
  • Bloomberg Terminal

Education 

  • PhD, Anthropology, Southern Illinois University Carbondale
  • MA, Anthropology, Wichita State University
  • BS, Anthropology, Michigan State University

Selected publications 

Peer-reviewed:

von Vacano C, Ruiz M, Starowicz R, Olojo S, Moreno Luna AY, Muzzall E, Mendoza-Denton R, Harding DJ. 2022. Critical faculty and peer instructor development: Core components for building inclusive STEM programs in higher education. Frontiers in Psychology 13:754233. doi:10.3389/fpsyg.2022.754233.

Muzzall E, Perlman B, Rubenstein LS, Haar RJ. 2021. Overview of attacks against civilian infrastructure during the Syrian civil war, 2012-2018. BMJ Global Health 6:e006384. http://dx.doi.org/10.1136/bmjgh-2021-006384.

Muzzall E. 2021. A novel ensemble machine learning approach for bioarchaeological sex prediction. Technologies: Big Data in Biology, Physical Sciences and Engineering  9, 23. https://doi.org/10.3390/technologies9020023

von Vacano C, Muzzall E, Anderson AG, Reeve J, van Neunen T. 2020. Building STEAM for DH and electronic literature: An educational approach to nurturing the STEAM mindset in higher education. Electronic Book Review [Frame]works for the Creative Digital Humanities. https://doi.org/10.7273/y68f-7313.

Muzzall E, Coppa A. 2019. Temporal and spatial biological kinship variation at Campovalano and Alfedena in Iron Age Central Italy. In Bioarchaeology of Frontiers and Borderlands; Tica, C., Martin, D.L., Eds.; University Press of Florida: Gainesville, FL, USA; pp. 107-132. https://doi.org/10.2307/j.ctvx0720b.11.

Roy-Chowdhury M, Muzzall E, Baumgardner DJ, Kennell BC, Esterbrook AC, Shurley JF, Scalarone GM. 2019. Potential clinical utility of ERC-2 yeast phase lysate antigen for antibody detection in dogs with blastomycosis. Medical Mycology 57, pp. 893-896. https://doi.org/10.1093/mmy/myy137.

Muzzall E, Campbell RM, Campbell M, Corruccini RS. 2014. Dahlberg Award Winner: The effects of dietary toughness on occlusopalatal variation in savanna baboons. Dental Anthropology Journal 27: pp. 8-15. https://doi.org/10.26575/daj.v27i1-2.39.


Non peer-reviewed:

Muzzall E. 2022. Text Analysis and Machine Learning Jupyter Book. https://eastbayev.github.io/SSDS-TAML/intro.html.

Muzzall E. 2017. Ensemble machine learning for sex prediction of a worldwide craniometric dataset. https://github.com/EastBayEv/Ensemble-machine-learning-for-sex-prediction-of-a-worldwide-craniometric-dataset.

More about me 

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.

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