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Making machine learning accessible to all

UteAnalytics allows a client to clean their data and conduct exploratory data analysis in various ways.

“Many call this the age of information,” said Rajive Ganguli, the Malcolm McKinnon Professor of Mining Engineering at the University of Utah. “It is perhaps more accurate to call it the age of data since not everyone has the ability to truly gain from all the data they collect. Many are either lost in the data or misled by it. Yet, the promise of being informed by data remains.”

Ganguli, who is also the College of Mines and Earth Science’s associate dean, is launching UteAnalytics, a free analytics software that makes artificial intelligence (AI) or machine learning (ML) accessible to all.

Founder of the ai.sys group at the U, Ganguli said that as long as a client knows their data, they can use UteAnalytics to understand better the problems they are trying to solve. The research group’s mission is to seek insight from data, model systems and to develop computational tools for education and research.

At various points in time, Ganguli has developed ML tools that his students could use in class. Years ago, it occurred to him that more could benefit from ML if only his workflow and tools were more user-friendly. Graduate student Lewis Oduro brought his vision to tuition by leveraging the numerous public domain ML tools available to programmers and converting them into Windows-based software.

“The tool is problem agnostic,” Ganguli said. “Hence it can have a broad group of users. I have used it for a variety of projects I am involved in, including mining, atmospheric sciences/air quality and COVID/hospital admissions.”

Two men stand in commencement attire.

PHOTO CREDIT: Rajive Ganguli

Lewis Oduro (right) and Rajive Ganguli (left).

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He reports that tens of subject matter experts (SMEs) who are non-coders have already subscribed to receive the software in advance of its formal release. “Many are professionals across a broad spectrum of fields from social science to business,” along with scientists and engineers.

Understanding your domain

Designed to empower the domain expert, UteAnalytics allows a client to clean their data and conduct exploratory data analysis in various ways. The software also allows users to estimate the effect of each input on the output, as well as develop models in advance of predicting on a new dataset.

Daniel Mendoza, who holds faculty appointments in the Department of Atmospheric Sciences and elsewhere at the U, is an early adopter of the software. Through his work with air quality monitors on UTA trains and electric buses in Salt Lake Valley, he and his team have successfully collected more than eight years of data for particulate matter and ozone levels, and recently, for nitrogen oxides.

“When we look at neighborhood-specific data we can drill in and really see some social justice impacts,” Mendoza reported last year. Today, he is “using UteAnalytics to quickly and efficiently analyze the temperature data that we’ll be collecting in real-time from our mobile and stationary sensors. UA gives researchers the power to look at data in a very streamlined way without endless hours of coding. The included tools facilitate a thorough interpretation of data and save time without compromising reliability.”

The difference that data—assisted by UteAnalytics tools — make in Mendoza’s work on air quality is most recently seen in the Urban Heat Watch campaign, involving citizen scientists who are helping collect data along the streets of Salt Lake Valley. As one of the top three urban heat islands in the nation, the Salt Lake City metropolitan area features a groundbreaking monitoring program—nowhere else the world does an initiative exist at the density and scale than in Utah’s capital city and environs.

An auspicious launch

UteAnalytics is just the latest deliverable for Ganguli, who has led approximately $13 million in projects as primary investigator. He is currently involved in several projects in five different countries — U.S., Denmark/Greenland, Mongolia, Saudi Arabia and Mexico — on topics ranging from ML to training.

Meanwhile, graduate student Lewis Oduro, who defended his thesis this past spring, has since taken a job near Phoenix, Arizona as a mining engineer at Freeport-McMoRan, a leading international mining company. A native of Ghana, Oduro said of his mentor, “He gave me the chance to work under him and provided me with the kind of relationship only evident between a father and a son.”

Under Ganguli’s tutelage and support, Oduro was the principal player in building UteAnalytics as desktop software used for data analytics and building predictive ML models.

“I will forever be indebted to him and to the entire faculty at the University of Utah’s Mining Engineering Department,” the young scientist said on his LinkedIn page.