News

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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Working toward a Ph.D. in computer science, Muntasir Wahed is delving into self-supervised learning, adversarial training, and out-of-distribution detection.

“Suppose we train a machine learning classifier to help medical diagnosis of a disease X given an X-ray,” Wahed said. “We collect a large dataset of X-rays for both positive and negative samples of the disease X. However, after we deploy the classifier in real life, it encounters confusing X-rays that have features not seen in any of the X-rays in the training samples. In such cases, it would be unreliable to classify the samples as positives or negatives. Instead, we would like to have a mechanism to recognize that these samples are so far unseen, or in other words, out-of-distribution.”

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Conferences have been a big part of Arka Daw’s life as a Ph.D. student this past academic year.

Daw presented “Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling” in proceedings at the 2020 SIAM International Conference on Data Mining (SDM), and “PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics” in proceedings at the 2021 ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD).

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With restrictions to working in physical office space still in effect, graduate students at the Sanghani Center are working remotely this summer for companies, labs, and programs from coast to coast. Students are not only gaining real-world experience from internships and other opportunities but, in many cases, they are also able to advance their own research interests.

Following is a list of Sanghani Center students and the work they are doing:

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Virginia Tech’s virtual university commencement will livestream tonight, Friday, May 14, at 6:15 p.m., and degrees will be conferred at this time.

“We are extremely proud of our graduates who achieved their goals despite more than a year of a pandemic that upended much of their lives,” 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. “When everything went virtual, they continued to attend classes, meet with their advisors, conduct research, present papers at conferences, and work at internships — all testament to their perseverance and a good barometer of their future success .”

Following is a list of Sanghani Center graduates:

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A multidisciplinary faculty team has garnered the Virginia Tech 2021 Alumni Award for Outreach Excellence for developing and administering the Urban Computing (UrbComp) program that trains graduate students in the latest methods of analyzing massive datasets to study key issues facing urban populations while emphasizing ethical and societal issues for practicing responsible data science.

The award, announced today by the university, accompanied by a $2,000 monetary award, is funded through the university’s Alumni Association and managed and administered by the Commission on Outreach and International Affairs.

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In 2016, Ping Wang followed her advisor, Chandan Reddy, from Wayne State University, where she received a master’s degree in computer science, to Virginia Tech and the Sanghani Center.

Her area of interest is healthcare systems, which are undergoing many changes in the era of big data.

“Advances in artificial intelligence and digitization in healthcare have enabled healthcare providers to effectively sift through tremendous amounts of medical information,” said Wang. “My first research project in this direction was about survival analysis and my advisor Dr. Reddy and other group members provided many useful suggestions and help at the initial stage. After further investigation, I found that there are still many unique challenges in the healthcare domain. I hope to leverage my expertise in data mining and machine learning to solve real-world challenges and advance healthcare applications.”

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