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It can be difficult for scientists, engineers, and anyone who does research or works in industry to extract reliable conclusions from large data sets. That’s why Jonathan Huggins, assistant professor in the Department of Mathematics & Statistics and in the Faculty of of Computing & Data Sciences, has devoted his research to finding data analysis tools that are computationally efficient. In other words, some methods aren’t guaranteed to incorporate all the available data and produce as close to a perfect result as you could want, and Huggins wants to change that.“You’d really like to have methods that you can trust, you know are going to work, not just because they’ve worked in the past, but because you have some more kind of theoretical guarantees,” Huggins says.
There are two applications Huggins is entrenched in now. One deals with cancer genomes and creating a tumor by analyzing the data from the mutational processes—everything that can combine to give you cancer. From there, the task is to try to accurately reflect these processes to determine the cause of the cancer. A second application is ecological forecasting: using data from the worldwide carbon cycle—how much carbon do trees and plants release, how much carbon is in the air, for example—and factoring local impacts like climate. The ultimate goal is to predict how much certain systems will be affected by climate change.“So I have a method and I want it to be computationally efficient. I also want it to be statistically efficient, using all available information from the data that I have, so I’m not sort of wasting the data that I have available to me,” Huggins explains. “What are the trade-offs there, how do I extract everything that I can to be as efficient as possible?” Learn More