Virginia Tech and Amazon establish machine learning research partnership

January 21, 2020 – Students and faculty of the Data Analytics Center work together at the Virginia Tech Research Center – Arlington. (Photo by Erin Williams/Virginia Tech)

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.

Scientists partner on multi-university grant to establish a field of ‘imageomics’

The Imageomics Institute will create a new field of study that uses images of living organisms to understand biological life processes.

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.

Virginia Tech team selected as finalist in Alexa Prize SimBot Challenge to advance next-generation virtual assistants

One of 10 finalists in the Alexa Prize SimBot Challenge, Virginia Tech’s team members meet regularly for updates on their specific work and overall progress on the project. The winner will be announced in 2023. Photo by Andrew Cybak for Virginia Tech.

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.

Sanghani Center Student Spotlight: Kylie Davidson

Graphic is from the paper “Sensemaking Strategies with Immersive Space to Think” 

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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Ismini Lourentzou awarded NSF grant to develop infrastructure for more effective AI in U.S. manufacturing industry

Ismini Lourentzou

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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Sanghani Center Student Spotlight: Mohannad Elhamod

Graphic is from the paper “Hierarchy-guided Neural Networks for Species Classification”

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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Sanghani Center Student Spotlight: Jie Bu

Graphic is from the paper “Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)” 

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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Congratulations to Sanghani Center 2021 Summer and Fall Graduates

Virginia Tech’s Fall Commencement ceremony for the Graduate School is now underway (livestream here) and seven students from the Sanghani Center are among those receiving degrees. 

“This has been a tough year and they successfully navigated obstacles caused by the COVID19 pandemic to achieve their academic goals and we are very proud of them,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the Department of Computer Science at Virginia Tech and director of the Sanghani Center for Artificial Intelligence and Data Analytics

Following is a list of Sanghani Center 2021 summer and fall graduates:

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Sanghani Center Student Spotlight: Shailik Sarkar

Graphic is from the paper “Deep diffusion-based forecasting of COVID-19 via incorporating network-level mobility information”

Growing up in a family that included a doctor and public sector employees, Ph.D. student Shailik Sarkar said it became increasingly evident to him that social, behavioral, and economic factors often influence the physical and mental health patterns of an individual or a group of people.

That realization shaped his own decision to focus his research in computer science on exploring how data mining and artificial intelligence can be used to tackle community healthcare problems. 

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Virginia Tech researchers garner NSF grant to connect AI with urban planning to improve decision making and service delivery

Tom Sanchez (left) and Chris North (right)

Tom Sanchez, professor of urban affairs and planning, and Chris North, professor of computer science and associate director of the Sanghani Center for Artificial Intelligence and Data Analytics, have been awarded a planning grant from the National Science Foundation’s Smart and Connected Communities program. Click here to read about how they will combine their expertise to use cities’ data collection and algorithm deployment to develop creative solutions to urban planning processes that have previously relied on traditional, analog approaches.