Gregory Faletto – Page 3 – Data Scientist, Statistician

My Math Review Notes

Jul 3, 2019

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by

gfaletto

in Technical Methodology (Math-Heavy)

I recently took the first-year screening exam for Ph.D. students in the statistics group in the Department of Data Sciences and Operations at Marshall. Since I started applying to grad school, I’ve been writing up review notes to help me…
Our Entry in the OCRUG Hackathon 2019

May 19, 2019

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by

gfaletto

in Technical Methodology (Math-Heavy)

I was a part of Team Save the WoRld along with Faizan Haque, Javier Orraca, Sam Park, and Shruhi Desai in the OCRUG Hackathon 2019 held at UC Irvine on May 18th and 19th. (In fact, I am writing this…
Presentation on Multi-Task Learning

Apr 1, 2019

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by

gfaletto

in Technical Methodology (Math-Heavy)

Today I gave an in-class presentation at USC on two papers in multi-task learning (or multivariate regression–linear regression when the response is a vector rather than one number). You could simply train a separate model for each response, but when…
The McCarthy/Fader/Hardie Model for Customer Retention

Nov 20, 2018

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by

gfaletto

in Technical Methodology (Math-Heavy)

In another post, I described how I fit a model to predict how well Buffer—a digital subscription-based firm that publicly releases much of its financial data—retains its customers. I used a methodology developed by Daniel McCarthy, Peter Fader, and Bruce Hardie (paper…
Which Customers Stick Around? Predicting Customer Retention

Nov 20, 2018

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by

gfaletto

in Business

One of the most critical priorities for any business is retaining their customers for as long as possible. You want to keep your customers happy so they come back to spend more. Data science can help businesses keep customers longer.…

About the author

Hi, I’m Greg Faletto. I’m a data scientist working on getting key metrics from messy real-world data and using causal inference to measure the impact (lift) of new features and product launches. Previously I worked on measuring the impact of linear and streaming television ad campaigns at VideoAmp, developed a methodology for A/B testing at Google, worked on machine learning at ZipRecruiter and Live Nation, and taught “Data Analysis for Decision Making” at the University of Southern California Marshall School of Business. I completed my Ph.D. in Statistics at the University of Southern California, where I wrote my dissertation on machine learning and causal inference. At USC I developed new machine learning methods for causal inference, estimating probabilities of rare events, and identifying the most important variables for predicting an outcome and wrote open-source software. My research has been published at the International Conference on Machine Learning and in the Proceedings of the National Academy of Sciences.

Blog

My Math Review Notes

Our Entry in the OCRUG Hackathon 2019

Presentation on Multi-Task Learning

The McCarthy/Fader/Hardie Model for Customer Retention

Which Customers Stick Around? Predicting Customer Retention