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Cynthia Rudin: Interpretable Machine Learning for All
Data in, decisions out: New faculty member Cynthia Rudin designs algorithms to help professionals turn raw information into informed choices
Cynthia Rudin has joined the faculty of the Department of Electrical and Computer Engineering in Duke University’s Pratt School of Engineering with a dual appointment in the Department of Computer Science. A rising star in the swiftly growing field of machine learning, Rudin focuses on writing algorithms that are easily interpretable by human experts for practical tasks like predicting energy grid reliability or diagnosing sleep apnea.
In machine learning, researchers teach computers how to learn from data, because while humans can’t easily find patterns in large datasets, a computer can. Machine learning algorithms can often make surprisingly accurate predictions. How exactly the machine learning algorithm arrives at those predictions, however, is not always clear.
“A lot of machine learning takes place with huge datasets where the inner workings of the codes are a ‘black box’ that nobody can see into,” said Rudin, who joins Duke from the faculty at the Massachusetts Institute of Technology and runs the Prediction Analysis Lab at Duke. “But I work on problems where the models clearly show how they produce their conclusions. This makes it easier for people to actually use the results to make decisions. ”
One type of machine learning model that Rudin particularly likes is called a Falling Rule List. Invented by Prediction Analysis Lab member Fulton Wang and also under development by PhD student Chaofan Chen, the model looks for high risk categories of medical patients first, then medium risk categories, and then low risk categories.
A Falling Rule List could use data from electronic health records to predict how likely individuals are to be readmitted to a hospital after they were released. The top category might contain patients who have serious problems and do not follow their doctor’s instructions, listing their chances of being readmitted as 92 percent. The bottom category might have the most compliant, healthiest patients at 10 percent.
With many tiers in between where a patient could fall, a physician might easily determine why the algorithm listed a patient at a particular level of risk.
“These types of machine learning models are perfect for healthcare situations because doctors need to be able to explain their decisions and recommendations to their patients,” said Rudin. “And to do that, they have to be able to interpret the results themselves.”
Interpretability, however, sometimes come at a cost—either a loss of accuracy or the need for a large amount of computational power. Rudin’s goal, then, is to determine just how many constraints the model can withstand without losing too much accuracy and still being able to fully optimize the code to avoid taking until the end of the universe to complete the computation.
It’s a goal that Rudin has achieved time and again, having already won an NSF CAREER award, the INFORMS Innovative Applications in Analytics Award, and an Adobe Digital Marketing Research Award, and having been named one of the 12 most impressive professors at MIT by Business Insider in 2015. In the last year, her team has worked on predicting criminal recidivism, diagnosing sleep apnea, and creating an app to improve recovery from prostatectomy surgery, using different flavors of interpretable machine learning and statistical algorithms.
And she’s having no trouble finding collaborators in her new position at Duke—already, she has started projects with faculty across campus. Her new collaborators are part of what drew Rudin to Duke.
“The people here are fantastic, and as an academic, good people are always the major draw,” said Rudin. “Duke has a huge amount of expertise in my area of research. It may be a small university, but it is incredibly strong—Duke really is a superpower in this area.”