Experience

Kebotix

• United States
• Chemical Manufacturing
• 1 - 100 Employee

• Lead Machine Learning Scientist

  • Aug 2022 - Present

• Machine Learning Scientist

  • Nov 2018 - Aug 2022



Insight Data Science

• United States
• Higher Education
• 1 - 100 Employee

• Data Science Fellow

  • Sep 2018 - Nov 2018

  - Produced automated analytics using Facebook Prophet for forecasting food waste to help clients of food donation startup Copia reduce their waste by up to ~ 80%. - Offered a range of forecasts aggregated weekly, monthly, and quarterly to improve predictability and match potential decision timescales for businesses. - Integrated forecasts from Python and Pandas into Periscope data visualization dashboards for use by Copia as a core component of their data analysis product. - Produced automated analytics using Facebook Prophet for forecasting food waste to help clients of food donation startup Copia reduce their waste by up to ~ 80%. - Offered a range of forecasts aggregated weekly, monthly, and quarterly to improve predictability and match potential decision timescales for businesses. - Integrated forecasts from Python and Pandas into Periscope data visualization dashboards for use by Copia as a core component of their data analysis product.



Massachusetts Institute of Technology

• United States
• Higher Education
• 700 & Above Employee

• Graduate Student Researcher

  • Aug 2012 - Aug 2018

  - Designed extrapolation methods for eliminating the bias inherent in the smaller training set sizes all classical cross-validation methods utilize, improving validation accuracy in an online learning context. - Built hierarchical cross-validation methods to select between the growing variety of cross-validation methods (new and classical) in a data-dependent fashion. - Discovered difficulties with generalization under adaptivity, producing new limits on when obfuscation (e.g. adding noise) can preserve holdout set accuracy despite repeated, adaptive use. - Proved that the ubiquitous Beta distribution is tightly concentrated about its mean. Show less