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.

Sanghani Center Student Spotlight: Yi Zeng

Graphic is from the paper “‘Rethinking the Backdoor Attacks’ Triggers: A Frequency Perspective’”

At the International Conference on Computer Vision (ICCV 2021) earlier this month, Yi Zeng, a Ph.D. student in electrical and computer engineering, gave a poster presentation on “Rethinking the Backdoor Attacks’ Triggers: A Frequency Perspective.”

Among the paper’s collaborators is his advisor Ruoxi Jia. Zeng was a master’s degree student at the University of California San Diego when he became aware of Ruoxi (at the University of California Berkeley at the time) and her achievements in trustworthy machine learning. 

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Researchers receive grant to predict the mechanics of living cells

(From left) Anuj Karpatne, Department of Computer Science and Sanghani Center for Artificial Intelligence and Data Analytics; Amrinder Nain and Sohan Kale, both in the Department of Mechanical Engineering, meet in the STEP Lab. Photo by Peter Means for Virginia Tech.

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.