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.
Anuj Karpatne, DAC faculty member and assistant professor of computer science
Anuj Karpatne, an assistant professor of computer science and a Discovery Analytics Center faculty member, has been named the 2019 Intelligent Systems and Geoscience (IS-GEO) inaugural Fellow.
The announcement was made by Suzanne Pierce, a research scientist at the Texas Advanced Computing Center (TACC) and principal investigator for the IS-GEO Research Coordination Network, during the American Association for the Advancement of Science (AAAS) conference in Washington, D.C., today.
“I am pleased to announce the IS-GEO Fellows Program,” Pierce said. “The program is designed to support researchers as they commit to in-depth projects to accelerate discoveries. Dr. Karpatne was selected because of his expertise in scientific theory{or physics}-guided machine learning. Throughout his fellowship year, he will evaluate applied machine learning approaches to Earth datasets for the energy industry.”
“We are at a crossroads of data-driven discovery in a number of scientific disciplines, such as earth sciences, that are witnessing a deluge of data and increased acceptance of data-driven, AI methodologies. However, to fully capitalize the promise of AI in accelerating scientific discovery, what is a needed is a paradigm shift that goes beyond current standards of ‘black-box’ AI research and embraces a deep synergy between scientific theories and AI, termed as theory-guided machine learning,” Karpatne said.
“Through the IS-GEO Fellows Program, I aim to expand the horizons of theory-guided machine learning, build new collaborations in the IS-GEO community, and solve impactful problems in the energy industry by using physics and data,” he said.
IS-GEO Fellow awardees are selected from the active membership of the IS-GEO community and receive an honorarium to explore new research areas with direct domain and real-world applications. Selected Fellows are encouraged to combine theoretical and scientific knowledge with applications to data problems from earth and environmental topics.
The AAAS Annual Meeting is the most widely reported global science gathering and the premier event to network with future collaborators across disciplines.