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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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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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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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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.

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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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.

Having the opportunity to apply state-of-the-art machine learning models to bioinformatics problems as an undergraduate motivated M. Maruf to take a deep dive into machine learning and deep learning as a Ph.D. student in computer science at Virginia Tech which he chose because of its exemplary research and top-notch facilities. 

“Dr. Anuj Karpatne’s unique view towards solving real-world problems fascinated me to explore more knowledge-infused machine learning,” Maruf said of his advisor at the Sanghani Center.

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With privacy a growing concern, Si Chen, a Ph.D. student in the Bradley Department of Electrical and Computer Engineering is using machine learning to study potential attacks and defenses against machine learning models. 

She was attracted to this area of research because it is important and practical in real-world settings.

“For example,” said Chen, “if a company trains a medical diagnosis model on a training set containing sensitive information, an attacker may be able to infer the training set’s knowledge even if he or she only has access to the model. Our job is to research better attack algorithms that can aid development of defense techniques.”

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