Adele Cutler, researcher, statistician and recently retired professor at Utah State University, is special to LoopMe. At LoopMe where we use artificial intelligence (AI) to improve advertising results, we honor mathematicians that made a material impact in the world by naming the conference rooms in our NYC and London offices after them. Adele shares that honor with Katherine Johnson, John Von Neuman, Grace Hopper, Geoffrey Hinton, Ada Lovelace, Bertrand Russell, Alan Turing, Thomas Bayes, and Mary Jackson.
Adele is a pioneer in computer science, and of all of the statisticians that we have celebrated at LoopMe, she is among the ones that are still contributing in this field. Due to her research and the development of Random Forest, a learning algorithm that helps with predicting outcomes (what she is most known for). Coincidentally, her research contributed to the core part of our AI solution that powers advertising campaigns for the world’s largest brands.
She joined us recently for an interview, with Leonard Newnham, LoopMe’s Chief Data Scientist, PHD, Machine Learning. In the interview she breaks down what led her to this field, how she views AI now, and what advice she would give the next generation of women in the field of math and science. 50% of our data science team are women, so it’s especially important to our employees to hear from leading women scientists and mathematicians, like Adele.
Research and Expertise
Prior to developing Random Forest with her colleague Lou Briemann, she focused on mixture models and optimization. Mixture models are cluster analysis (dividing up people in groups of similar people). She transitioned from doing statistician based work to doing work related to machine learning. Adele shared that she enjoys applied mathematics because it showed her that real world problems can be solved with machine learning.
Random Forest and how it relates to machine learning
Random Forest is a commonly used machine learning algorithm that combines multiple decision trees to get a single result. Decision trees start with a simple question like what car should I buy? From that one question the computer can come up with several other questions in order to make predictions. This is quite useful when thinking about advertising and finding the right audience for a brand. Naturally decision trees have their limitations, however, when multiple decisions are made in a random forest their predictions tend to be more accurate.
Many statisticians use this algorithm because it requires very little adjustment, it deals with a wide variety of data types, it can handle missing values, and can be used to predict how much someone will buy and/or whether they will buy or not.
Legacy
Adele is a brilliant woman who has helped improve how we help our clients using our own machine learning. She is an advocate of creating opportunities for more young women to join the computer science field as well. She believes that “you have to push yourself to learn new things, especially in this field because it is moving so quickly.”
Our heartfelt gratitude went out to Adele for this enlightening discussion – we appreciated her contributions and time to speak with our team.
