News

Viruses like SARS-CoV-2 don’t respect boundaries, moving between species and continents and leaving destruction as they go. Beating the next pathogen with pandemic potential means getting good at crossing borders ourselves — between fields of study, between research universities, and between scientists and the wider community.

An $18 million grant announced by the U.S. National Science Foundation (NSF) will put that goal within reach. The award brings together five universities and more than 20 researchers, academics, and public health experts to establish the Virginia Tech-led Center for Community Empowering Pandemic Prediction and Prevention from Atoms to Societies (COMPASS).

Among those on board are Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering and director of the Sanghani Center for Artificial Intelligence and Data Analytics, and Anuj Karpatne, associate professor of computer science and core faculty at the center. Read full story here.

A new initiative between the Center for Advanced Innovation in Agriculture (CAIA) and the Sanghani Center for Artificial Intelligence and Data Analytics has launched a Graduate Research Assistantship Program that provides scholarships for exceptional Ph.D. students conducting research that generates agricultural solutions enabled by artificial intelligence and data analytics. 

The initiative builds upon a consistent collaboration between the two centers that began with the inauguration of CAIA in 2021. Faculty from the two centers have been working on joint research projects that includes a National Science Foundation-sponsored Convergence Accelerator project and some faculty are members of both centers.

Sangwoo Kim in the Department of Biological Systems Engineering and Runing Yang in the Bradley Department of Electrical and Computer Engineering are the first Ph.D. students to receive the Joint Graduate Scholarship.

Read full story here.

Anuj Karpatne, associate professor in the Department of Computer Science in the College of Engineering has won a five-year, $595,738 National Science Foundation Faculty Early Career Development Program CAREER award to explore a unified approach for accelerating scientific discovery using scientific knowledge and data. Karpatne is also a core faculty member at the Sanghani Center for AI and Data Analytics. Read the full story here.

Researchers in three different disciplines at Virginia Tech are partnering in a $15 million grant from the National Science Foundation (NSF) to establish an institute in the new field of “imageomics,” aimed at creating a new frontier of biological information using vast stores of existing image data, such as publicly funded digital collections from national centers, field stations, museums, and individual laboratories. 

The goal of the institute is to characterize and discover patterns or biological traits of organisms from images and gain insights into how function follows form in all areas of biology. It will expand public understanding of the rules of life on Earth and how life evolves.

Imageomics is one of five Harnessing the Data Revolution institutes receiving support from the NSF.  

Anuj Karpatne, assistant professor in the Department of Computer Science and faculty at the Sanghani Center for Artificial Intelligence and Data Analytics, is serving as one of four co-investigators for the multi-university project led by the Ohio State University. Leanna House, associate professor in the Department of Statistics and faculty at the Sanghani Center, and Josef Uyeda, assistant professor in the Department of Biological Sciences, are designated senior personnel. All three researchers are part of the executive leadership team of the institute and investigators on Virginia Tech’s $1.4 million portion of the grant. Click here to read more about these scientists will apply their expertise to the project.

With advances in deep learning, machines are now able to “predict” a variety of aspects about life, including the way people interact on online platforms or the way they behave in physical environments. This is especially true in computer vision applications where there is a growing body of work on predicting the future behavior of moving objects such as vehicles and pedestrians. 

“However, while machine-learning methods are now able to match — and sometimes even beat — human experts in mainstream vision applications, there are still some gaps in the ability of machine-learning methods to predict the motion of ‘shape-shifting’ objects that are constantly adapting their appearance in relation to their environment,” said Anuj Karpatne, assistant professor of computer science and faculty at the Sanghani Center for Artificial Intelligence and Data Analytics. Click here to read how Karpatne and his team will tackle this challenge in their National Science Foundation-sponsored research.