Dr. Aisha Williams

About

Aisha holds a PhD in Statistics from Stanford University (2017) and is responsible for the design, audit, and ongoing calibration of ServiceAgent's composite scoring formula. Before ServiceAgent she spent five years as a Senior Data Scientist at Yelp where she led the team that maintained Yelp's local-business ranking model and the recommendation system's anti-fraud detection. Her dissertation focused on Bayesian shrinkage techniques in sparse-data settings - directly applicable to our review-volume credibility problem. Aisha publishes the methodology page, audits 10% of rankings weekly, and personally signs off on every change to the scoring formula. If a page has a Dataset schema or a published formula, Aisha reviewed it.

Credentials

• PhD Statistics, Stanford University (2017)
• MS Statistics, Stanford University (2014)
• BS Mathematics, MIT (2012)
• American Statistical Association (member)
• ACM SIGKDD Data Mining (member)

Areas of expertise

• Composite ranking algorithm design
• Bayesian shrinkage in sparse-data settings
• Review-fraud detection
• Anti-spam machine-learning
• Methodology transparency + reproducibility

Background

• Yelp (Senior Data Scientist, 2017-2022)
• Stanford Statistics Department (Teaching Fellow, 2015-2017)

Selected publications

• Journal of the American Statistical Association (3 peer-reviewed papers)
• ACM SIGKDD 2019 (Bayesian methods for sparse-review ranking)
• ServiceAgent Methodology Page (author + audit lead)