News

Twelve highly accomplished Virginia Tech faculty experts in computer science and computer engineering have formally affiliated with the Virginia Tech Innovation Campus in Alexandria. This first cohort of Innovation Campus faculty will play a vital role in shaping the new campus by helping to establish key research themes, enhancing the project-based curriculum, and developing the campus governance structure. Among these 12 experts are Naren Ramakrishnan, Thomas L. Phillips Professor of Engineering and director of the Sanghani Center for Artificial Intelligence and Data Analytics and center faculty Chang-Tien Lu, professor of computer science and director of the computer science program, Northern Virginia. Read more here.

Would you like a virtual assistant that could go through chunks of large reports with pages upon pages of tables and raw numeric data and summarize it all in a short paragraph? 

This is what Mandar Sharma is trying to accomplish with his Ph.D. research in the area of natural language generation.

“The progress of artificial intelligence depends heavily upon our ability to communicate with machines and natural language is the crux of human communication,” Sharma said. The paper, “T3: Domain-Agnostic Neural Time-series Narration,” which he presented at the 2021 IEEE International Conference for Data Mining, generates succinct narratives that describe large time-series datasets.

“With a dataset of time-series and narrative pairs, a promising direction for future exploration lies in learning direct mappings from numbers to text, extending beyond just time-series,” said Sharma, who is advised by Naren Ramakrishnan.

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A 2021 Computing Innovation (CI) Fellow, postdoc Rebecca Faust, has been working with Chris North, professor in computer science and associate director at the Sanghani Center for Artificial Intelligence and Data Analytics, since January.

They are exploring how to create explanations of the effects of semantic interactions on a deep learning model through the analysis of perturbations and differences in the model after interactions. 

“Through these explanations, we hope to demonstrate how models adjust when people inject prior knowledge into them through semantic interaction and validate whether the updated model adequately captures this prior knowledge,” said Faust, who earned her Ph.D. in computer science from the University of Arizona in December 2021. 

Faust will also help lead Department of Defense (DoD)-funded projects on interactive analytics, funded through the Center for Space, High-Performance, and Resilient Computing (SHREC).

“Dr. North was at the top of my list,” she said. “Together, we crafted an application, including a research proposal, a fellowship plan, and a mentorship plan, and submitted it to the program.” 

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Virginia Tech and Amazon are partnering to advance research and innovation in artificial intelligence and machine learning. The Amazon – Virginia Tech Initiative for Efficient and Robust Machine Learning will include machine learning-focused research projects, doctoral student fellowships, community outreach, and an establishment of a shared advisory board.

“This partnership affirms the value of our connection to Amazon as we scale up project-based learning and research programs in artificial intelligence and machine learning,” said Virginia Tech President Tim Sands. “Building Virginia Tech’s strength and expertise in these fields will support critical technological advancements and our commitment to fuel workforce development in the commonwealth.” 

“We are delighted to collaborate with Virginia Tech in launching this new initiative which brings together the top talent in our two organizations in a joint mission to achieve ground-breaking advances in robust machine learning,” said Prem Natarajan, vice president of Alexa AI – Natural Understanding at Amazon. “The proximity of this initiative to Amazon’s HQ2 will catalyze research efforts that leverage the depth of talent in the Northern Virginia area to address some of the most pressing challenges in AI.”  To learn more about the initiative which will be housed in the College of Engineering and led by Sanghani Center for Artificial Intelligence and Data Analytics researchers, read 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.

A Virginia Tech team from the Sanghani Center for Artificial Intelligence and Data Analytics is one of 10 finalists chosen to compete in the Alexa Prize SimBot Challenge. The challenge focuses on advancing the development of next-generation virtual assistants that continuously learn and gain the ability to perform common sense reasoning to help humans complete real-world tasks.

“The SimBot should be able to understand the intention of a task as well as any instructions or feedback it receives from a user and interpret the environment to correctly predict what action is needed to complete it,” said Lifu Huang, assistant professor of computer science and faculty at the Sanghani Center.  Click here to read more about how the team will tackle this challenge.

Focused on using virtual/augmented reality for day-to-day productivity tasks, Kylie Davidson is investigating how immersive technologies can be used during sensemaking. 

“The goal is to add computational analytics to our software prototype to assist the user in real-time while they complete a sensemaking task,” she said.

After graduating with a bachelor’s degree in computer science from James Madison University, Davidson chose a Ph.D. program at Virginia Tech where she could conduct cutting-edge computer science research with real-world impact.”

“At the Sanghani Center,” she said, “I get to work with a community of researchers who are solving real-world problems every day.”  

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Because artificial intelligence benefits from training on large datasets, trying to implement AI within the U.S. manufacturing industry poses a critical problem, according to Ismini Lourentzou, assistant professor in the Department of Computer Science and faculty at the Sanghani Center for Artificial Intelligence and Data Analytics. “Manufacturers not only tend to be slow and repetitive with data collection efforts, but they typically keep their data secret and partnerships are rare,” she said.

Lourentzou was recently awarded an EArly-concept Grant for Exploratory Research (EAGER) from the National Science Foundation for a project, Cost-sensitive Federated AI for Smart Manufacturing Data-Sharing, to develop a manufacturing service infrastructure that would encourage U.S. manufacturers to accelerate the use of AI in smart manufacturing and exchange data with trusted partners.  

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As he was looking for Ph.D. programs in computer science, Mohannad Elhamod happened upon the Science-Guided Machine Learning lab headed by Anuj Karpatne, an assistant professor and faculty at the Sanghani Center. “I was very excited about the work he was doing and after attending a Graduate Preview Weekend where I was delighted by the diversity of academic and social activities in the Department of Computer Science, I was pretty much convinced that Virginia Tech was where I should be.”

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Jie Bu, a Ph.D. student in computer science, has been interested in machine learning since he was an undergraduate in communications engineering at Harbin Institute of Technology, China. There he was introduced to the Random Forests (a machine learning model) and genetic algorithms which, Bu said, still hold great fascination for him.

In his current research at the Sanghani Center, Bu uses machine learning for physical applications. 

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