Team develops technique to segment carbon nanotube forests in images

Mizzou Engineering researchers are another step closer to controlling the properties of carbon nanotubes growing in mass quantities.

Mizzou Engineer lends protein prediction expertise to climate change studies at Danforth Plant Science Center

An inter-institutional research team is using the power of computational analysis to pinpoint which plant genes confer resilience against rising temperatures that threaten global food supplies in the coming decades. Mizzou Engineering Professor Jianlin “Jack” Cheng — one of the first scientists in the world to use deep learning, a powerful artificial intelligence technique, to predict protein structures — adds a unique perspective to the work. Since 2018, he’s been collaborating with Dr. Ru Zhang, a plant scientist at the Danforth Plant Science Center in St. Louis, to leverage computational tools in the study of plant genes.

Engineer uses advanced deep learning to predict where proteins will localize within cells

A Mizzou Engineer is developing computational tools that can be used to predict where proteins will localize within a cell. Using highly advanced deep learning, the resource could help researchers better understand how proteins function or, if positioned incorrectly within a cell, misfire and cause problems.

Research Team Training AI to Better Detect Small Objects Shang working with Missouri

Artificial Intelligence (AI) is good at recognizing a single bird in an image. Where it falls short is when it tries to identify hundreds of tiny birds in an aerial photo. Professor Yi Shang and his research team have been working for three years to see where AI can improve its vision when it comes to small objects.

Protein Prediction Challenge Makes History – and Mizzou Engineers Rank in Top 10

Mizzou Engineering students took on tech giants at a worldwide competition last month and came home in the top 10 for devising a way to accurately predict protein structures. And in subcategories, Mizzou teams ranked in the top 3.

Mizzou Engineers Help Develop Smarter Way to Diagnose Malaria

Mizzou Engineers are part of a global team developing a smarter way to combat malaria—a leading cause of death in the world. Now, they’ve outlined a two-step approach that will use deep learning to more accurately and efficiently diagnose the disease.

MU Students Develop Tool to Fight Fakery

Imagine watching a video from a presidential candidate on your favorite political blog, only to find out later that the candidate not only disavows the video but claims she never made the video in the first place. These so-called “deepfake videos,” which use fabricated photos and audio, are among the biggest challenges facing the news industry.

Bringing deep learning to materials science: MU team reaches breakthrough

Graphene is a single layer of carbon atoms that has a wide array of potential uses, particularly as a candidate material for use in electronic devices, such as LED screens, touch panels, smart phones and solar cells. Graphene’s electrical and optical properties can be significantly altered for better usage. Discovering how these atoms tune to create these properties is one of the most pressing questions in materials science.