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Interpolation of sparse high-dimensional data

Automated Feature-Topic Pairing: Aligning Semantic and Embedding Spaces in Spatial Representation Learning

Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks

Spatio-Temporal Event Forecasting Using Incremental Multi-Source Feature Learning

SWITCHES: Searchable Web Interface for Topologies of CHEmical Switches

Sanghani Center Student Spotlight: M. Maruf

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 […]

Aneesh Jain

Aneesh Jain is a master’s degree student in the Department of Computer Science. His advisor is Chandan Reddy. Jain’s work is in the domain of deep learning for software engineering, researching models for tasks like code translation, code summarization, and code generation.

Yi Zeng

Yi Zeng is a Ph.D. student in the Bradley Department of Electrical and Computer Engineering. His advisor is Ruoxi Jia. In his research, Zeng is particularly interested in machine learning’s general robustness and security. Currently, he is looking into general robust inference for deep learning models against adversarial and backdoor attacks.

Sanghani Center Student Spotlight: Si Chen

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 […]

Sanghani Center Student Spotlight: Muntasir Wahed

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 […]