معرفی
Dr. Rachel Wilkerson is a Lecturer in the Data Science program at Baylor University's School of Engineering and Computer Science. She holds a PhD in Statistics from the University of Warwick (2020), an MSc in Complexity Science from the University of Warwick (2013), and a BA in University Scholars from Baylor University (2011). Her academic and professional work centers on causal inference, graphical models, and human-centric data systems.
- PhD, Statistics, University of Warwick (2020)
- MSc, Complexity Science, University of Warwick (2013)
- BA, University Scholars, Baylor University (2011)
Her research lies at the intersection of storytelling, expert knowledge, and probabilistic modeling. She specializes in staged trees and Bayesian networks, using expert elicitation to build models in data-scarce environments. Her work supports decision-making in civic, legal, and non-profit contexts, emphasizing transparency and fairness in algorithmic systems.
The trends in her research—though no formal articles are listed—focus on translating qualitative narratives into formal statistical models, particularly in complex, real-world systems. Her work bridges data science with ethics, art, and social impact, often involving small or messy datasets where traditional methods fall short.
- Volunteer, DataKind (Data Dives, scoping team)
- Participant, Statisticians for Society (RSS program)
- Trainer, Data Science for Social Good (causal inference workshops)
- Member, R-Ladies community
- Consultant, Tesserwell, LLC – human-centric data design
She has led training initiatives for non-profits on data maturity and has consulted on algorithmic fairness, legal risk modeling, and customized causal inference. Her advisory and grant activities are community-focused, emphasizing collaboration with civic organizations and social impact projects.
She is affiliated with Tesserwell, LLC, a consultancy she runs that develops software for probabilistic modeling, algorithmic auditing, and legal risk visualization. The team focuses on creating tools that make expert knowledge actionable in statistical frameworks, particularly in high-stakes domains.
