(From left) Pedro Soto, postdoctoral associate, Department of Mathematics; Wenjie Xiong, assistant professor, Department of Computer and Electrical Engineering; Muhammad Gulzar, assistant professor, Department of Computer Science; Xuan Wang, assistant professor, Department of Computer Science; Ruoxi Jia, assistant professor, Department of Electrical and Computer Engineering; Dawei Zhou, assistant professor, Department of Computer Science; and Bo Ji, associate professor, Department of Computer Science. Virginia Tech photo
Two student Amazon Fellows and five faculty-led projects supported by the Amazon-Virginia Tech Initiative for Efficient and Robust Machine Learning for the 2024-25 academic year were named at a retreat held on the Blacksburg campus.
Fellowships are awarded to Virginia Tech doctoral students recognized for their scholarly achievements and potential for future accomplishments. They must be enrolled in their second, third, or fourth year and interested in and currently pursuing educational and research experiences in AI-focused fields. In addition to receiving funding for their work, the fellowship includes an opportunity to interview for an Amazon internship intended to provide them with a greater understanding of industry and use-inspired research.
The initiative’s faculty awards support machine learning sponsored research that works toward revolutionizing the way the world uses and understands this field of modern technology.
Dawei Zhou. Photo by Peter Means for Virginia Tech.
While not a perfect system, human reasoning still outshines artificial intelligence (AI) in a number of critical areas. One Virginia Tech researcher wants to help change that.
JinYi Yoon (from left) and Adithya Kulkarni, postdoctoral fellows in the Department of Computer Science. Photo by Tonia Moxley for Virginia Tech.
The Department of Computer Science recently added its first two Presidential Postdoctoral Fellows, JinYi Yoon and Adithya Kulkarni.
They join the ranks of more than 200 postdoctoral scholars working across every Virginia Tech college and institute to advance the pursuit of knowledge and develop into the next generation of experts in their fields.
In 2022, to support the capacity of postdoctoral fellows to initiate innovative and exciting projects, the university established the Office of Postdoctoral Affairs to serve this important community.
Kulkarni is advised by Dawei Zhou and Lifu Huang, both core faculty at the Sanghani Center for Artificial Intelligence and Data Analytics. As a postdoctoral fellow, he will work on combining the power of graph learning and large language models (LLMs) to develop approaches that enable explainability, interpretability, replicability, and, thus, the general robustness of LLMs. He will also help mentor graduate students and teach introductory computer science courses at Virginia Tech.
Insider threats to cybersecurity can occur when an actor with authorized access to an organization’s network conducts malicious activities that may release the organization’s critical information that further results in severe consequences such as financial loss, system crashes, and national security challenges.
“These threats are on the rise and according to a recent cyber security survey, 27 percent of cybercrime incidents involved insiders,” said Dawei Zhou, an assistant professor in the Department of Computer Science; director of the VirginiaTech Learning on Graphs (VLOG) Lab and core faculty at the Sanghani Center for Artificial Intelligence and Data Analytics.
One of Zhou’s projects, “Combating Insider Threat: Identification, Monitoring, and Data Augmentation,” targets the challenging problem of how to combat insider threats. He recently received a 2023-2024 Cisco Faculty Research Award that will help support this research.
Zhou said his project uses multiple dynamic and heterogeneous data sources that include internal system logs, employee networks, and email exchange networks.
“Distinctly from other types of terror attacks, insider threats exhibit several unique challenges like rarity, non-separability, label scarcity, dynamicity, and heterogeneity, making it extremely difficult to catch them in time for a successful counter-attack,” said Zhou.
He explains: Rarity means that the absolute number of such insiders is extremely small, especially compared with the total number of employees in a large organization or company; non-separability means that the insiders are very good at camouflaging themselves to make them indistinguishable from normal ones and thus able bypass the detection system; label scarcity means that the annotation process of insiders is labor-extensive and time-consuming; dynamicity refers to the time-evolving nature of the raw input data sources as well as the behaviors of insiders; and heterogeneity refers to the heterogeneous data coming from various sources and in various formats.
“Although different insiders are often conscious and good at camouflaging themselves, they might share some common traits if examined under the proper lens” he said.
With this in mind, the project will try to combat insider threat via an interactive learning mechanism, building new theories and algorithms for the following learning tasks:
Insider Identification: characterize the descriptive and essential properties of insiders and detect groups of insiders – such as traitors, masqueraders, and unintentional perpetrators — with common traits.
Insider Monitoring: track the evolution of insider behaviors over time and provide a visual system for analysis, annotation, and diagnosis.
Data Augmentation; sanitize input data by completing missing data and cleaning noisy data and generate synthetic insiders to alleviate the label scarcity issue.
Computer science Ph.D. students Shuaicheng Zhang and Haohui Wang, who are advised by Zhou, will be working with him on the project. A third student, Weije Guan, will be joining the team in the Fall semester.
“We hope that the innovative approach we are taking will result in a better understanding of how to counterattack these threats and ultimately decrease the number of cybercrimes,” Zhou said.
