Exploring the frontiers of data science

Satellite technology is transforming the field of geography, says Xiao Huang. "It's kind of like being an astronaut in that satellites give you a view of Earth from space."

As a high-tech geographer, Xiao Huang uses remove sensing and AI for insights into how to design more equitable cities, improve management of natural resources, lessen the impact of natural and human-caused disasters, and improve public health policies.

"I love geography and computer technology," says Huang, assistant professor in Emory's Department of Environmental Sciences. "I want to use my knowledge of these fields to help humanity, especially socially disadvantaged communities."

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Developing a new approach to control a dangerous urban mosquito in Ethiopia

New AI tool supports best practices to prevent spread of dangerous C. diff infections

"At Emory, I look forward to continuing this line of work and exploring innovative ways AI can help improve patient care," says Shengpu Tang, who recently joined the university as assistant professor of computer science.

Decision-making forms the core of hospital patient care, involving an array of clinicians whose duties span diagnosis, treatment and resource allocation. The complexity of these interrelated decisions makes it challenging for physicians, nurses and other caretakers to connect all the dots in real time. 

Shengpu Tang, assistant professor of computer science at Emory University, is developing AI tools to identify, validate and transmit key data needed to most effectively support healthcare workers in decision-making processes. 

 

“The end goal is to improve patient care and patient outcomes,” Tang says. 

JAMA Open Network published the results of Tang’s latest collaborative project: the first AI guidance deployed in a hospital setting aimed at guiding best practices to prevent the spread of dangerous infections of Clostridioides difficile. 

Analysis by the researchers found that the new AI-guided protocol significantly reduced antibiotic prescriptions at Michigan Medicine — a factor that increases infection risk for vulnerable patients — with 10% to 15% fewer days on antimicrobials. Importantly, reducing days on antimicrobials did not increase the length of stay, readmission rate or mortality among patients. The already low incidence of Clostridioides difficle trended downwards during the study, but that reduction did not reach statistical significance.

Read the full story here.

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Children as young as five can navigate a 'Tiny Town'

A playground scene in Tiny Town. (Dilks lab)

Many behavioral studies suggest that using landmarks to navigate through large-scale spaces — known as map-based navigation — is not established until around age 12. 

A neuroscience study at Emory University counters that assumption. Through experiments combining brain scans and a virtual environment the researchers dubbed Tiny Town, they showed that five-year-olds have the brain system that supports map-based navigation. 

The journal Proceedings of the National Academy of Sciences published the finding, the first neural evidence that this cognitive ability is in place in such young children. 

“While large-scale navigation abilities certainly continue to develop throughout childhood, our findings show that the underlying neural system is established remarkably early,” says Yaelan Jung, first author of the study and a postdoctoral fellow in Emory’s Department of Psychology. 

“Rather than taking a decade or more, map-based navigation is underway in half that time,” adds Daniel Dilks, associate professor of psychology and senior author of the study. “Five-year-olds have the brain system enabling them to find their way around a tiny, virtual town. They not only know that the ice cream store in the mountain region is different than the ice cream store in the lake region, they know how to navigate the streets to get to each of them.”

Read the full story here.

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Developing a new approach to control a dangerous, invasive mosquito in Ethiopia

Edilawit Mesfine, left, and Edel Seifu, both from Jigjiga University, collect data and larvae from a construction site. (Photo by Kim Awbrey)

Emory University received $2.8 million in funding from the Gates Foundation to support its work to develop and test a high-tech, low-cost method to control an invasive mosquito that poses a growing threat of urban malaria in Africa. The three-year project is focused on three cities in Ethiopia: Jigjiga, Semera and Logiya. 

The project’s novel approach to combating malaria combines on-the-ground knowledge of human and mosquito behaviors with detailed environmental imagery from drones and NASA satellites. Machine learning techniques will be applied to the data to develop a model — powered by artificial intelligence — for targeted public health interventions. 

The aim is to efficiently control populations of the invasive Anopheles stephensi mosquito by first, identifying water sources that are most likely to harbor the larvae during the dry season. And secondly, by sharing maps of these precise targets with local public health authorities — via a mobile phone app — to guide their larvae-eradication efforts in the most efficient and effective manner. 

The strategy is based on research on the ecology of stephensi in Jigjiga led by Gonzalo Vazquez-Prokopec, Emory professor of environmental sciences and co-principal investigator for the grant. “It sounds counterintuitive to focus mosquito-control efforts on the dry season,” Vazquez-Prokopec says. “Our research, however, shows that the dry season offers a perfect window of opportunity to cost-effectively control these mosquitoes.” 

Vazquez-Prokopec is an expert on the disease ecology of pathogens spread by vectors, such as mosquitoes. His research considers environmental factors as well as the interactions of mosquitoes, the pathogens they carry, and people. 

Read the full story here.

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New AI tool set to speed quest for advanced superconductors

Xu Chen, an Emory PhD student of theoretical chemistry, is first author of the paper. He says the team was inspired by the image-recognition training used for self-driving cars to create a powerful machine-learning framework.

Using artificial intelligence shortens the time to identify complex quantum phases in materials from months to minutes, finds a new study published in Newton. The breakthrough could significantly speed up research into quantum materials, particularly low-dimensional superconductors. 

The study was led by theorists at Emory University and experimentalists at Yale University. Senior authors include Fang Liu and Yao Wang, assistant professors in Emory’s Department of Chemistry, and Yu He, assistant professor in Yale’s Department of Applied Physics. 

The team applied machine-learning techniques to detect clear spectral signals that indicate phase transitions in quantum materials — systems where electrons are strongly entangled. These materials are notoriously difficult to model with traditional physics because of their unpredictable fluctuations. 

“Our method gives a fast and accurate snapshot of a very complex phase transition, at virtually no cost,” says Xu Chen, the study’s first author and an Emory PhD student in chemistry. “We hope this can dramatically speed up discoveries in the field of superconductivity.” 

One of the challenges in applying machine learning to quantum materials is the lack of sufficient high-quality experimental data needed to train models. To overcome this, the researchers used high-throughput simulations to generate large amounts of data. They then combined these simulation results with just a small amount of experimental data to create a powerful and efficient machine-learning framework.

Read more about the discovery.

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