Jundong Li's Research Aims to Improve Machines’ Recommendations and Predictions Based on Cause and Effectmkw3a@virginia.edu
Children attend pre-school and kindergarten to develop the skills they need to thrive. They practice sharing and making friends. They learn to use words and numbers. They gain confidence through movement and self-control. Teachers have an array of techniques to help children develop these skills.
But how should they help students who struggle? There’s no shortage of opinions from educators, parents and policy-makers about effective ways to teach children, based on observations of what happens in classrooms and children’s behavior in and out of school. These observations may be missing something important, however - factors that are unseen, unrecognized or unreported but are powerful influences on a student’s performance.
Jundong Li, University of Virginia assistant professor of electrical and computer engineering, computer science and data science, is conducting research that could help teachers and administrators more accurately determine which learning methods are best for their youngest pupils.
Li has earned a prestigious National Science Foundation CAREER award to better understand cause and effect in human decision-making in the era of big data. Li will use his $600,000 five-year award to develop a suite of sophisticated algorithms and mathematical models, informed by human experience and intuition, to find cause-and-effect relationships in a huge amount of data. His work has the potential for broad applications in public health and medicine in addition to education.
The CAREER program, one of the NSF’s most prestigious awards for early-career faculty, recognizes the recipient’s potential for leadership in research and education. Li’s award recognizes his expertise in data mining, machine learning, and artificial intelligence, which are part of a research strength area for the Charles L. Brown Department of Electrical and Computer Engineering within UVA’s School of Engineering and Applied Science.
“The basic problem here is that machine learning and data mining alone are often insufficient to make decisions for humans,” Li said. “Typically, given a large amount of data, machine learning models can find correlations and then use those correlations to make inferences and predict outcomes.”
Because machines cannot really understand human needs, expectations and behaviors, their predictions and recommendations may be based on spurious correlations.
“We all know that correlation does not necessarily imply causation,” Li said. “In order to make a decision, typically we need to have a better understanding of what is cause and what is outcome. We want to find causal relations between variables at play.” This means creating what Li calls a causal inference model, which quantifies the strength of cause-and-effect relationships between different variables and uses the strongest to make a decision.
Nowadays, research to make machine learning algorithms and models better at reasoning is largely data-driven, Li said. “For my CAREER award project, I want to incorporate prior human knowledge into these algorithms, to give the model the benefit of human wisdom as it processes data and interprets decision-making scenarios.”