The Sanghani Center is home to high-profile research, garnering recognition within and beyond the data analytics community.
Our talented team has been recognized with many competitive research awards and featured in major news and media outlets such as the Wall Street Journal, Newsweek, the Boston Globe and the Chronicle of Higher Education.
Ismini Lourentzou joined Virginia Tech’s “Curious Conversations” to chat about artificial intelligence (AI) and machine learning related to personal assistants, as well as her student team’s recent experience with the Alexa Prize TaskBot Challenge 2.
Ahuja defended his dissertation this past summer and is currently an applied scientist at DocuSign in Seattle, Washington. His advisor was Edward Fox.
The organization’s annual award supports student efforts to transform the genre of the dissertation through the use of innovative research data management techniques and software to create multimedia Electronic Theses and Dissertations (ETDs). It includes a cash award and travel scholarship funds to attend a future ETD Symposium.
Following is an excerpt from the email Ahuja received from the chair of the NDLTD Awards Committee notifying him of this honor:
“Your thesis, “Analyzing and Navigating Electronic Theses and Dissertations,” provides a technical framework to expand the access to the content of millions of published theses, like yours, which are constrained in their usability and usefulness by the portable document format. Current digital libraries are institutional repositories with the objective being content archiving, they often lack end-user services needed to make this valuable data useful for the scholarly community. To effectively utilize such data to address the information needs of users, digital libraries should support various end-user services such as document search and browsing, document recommendation, as well as services to make navigation of long PDF documents easier and accessible. Your research and dissertation directly addresses these concerns in creative and beneficial ways.”
The Roanoke Valley was no exception to the heat, with news reports naming 2023 as the region’s second-hottest summer. But the rising temperatures were particularly stifling for some neighborhoods in Roanoke — those impacted by harmful urban planning practices.
Virginia Tech has received funding from the National Science Foundation for a collaborative research project that brings machine learning and data science research to the domain of Stable Isotope Ratio Analysis (SIRA) to improve discovery and traceability of illicitly-sourced timber products. Illegal timber trade (ITT) is the most profitable natural-resource crime, valued at 50-152 billion U.S. dollars per year.
“To enforce timber regulations and international frameworks, there is a need for accurate, cost-effective, and high-throughput tools that can be used to identify and trace illegally sourced timber products,” Ramakrishnan said.
The team brings together data scientists, analytical chemists, geospatial and remote sensing scientists, practitioners, international trade and supply chain specialists, and field experts who conduct reference sample expeditions to bring novel data science approaches to analyzing a range of geospatial and remotely sensed datasets.
Patrick Butler, senior research associate, and Brian Mayer, research associate at the Sanghani Center will be part of the Virginia Tech team.
Key foci of this project include machine learning methods for SIRA analytics; location determination from isotopic ratios; and active sampling strategies to close the loop. Foundational machine learning contributions in science-guided machine learning, contrastive and generative learning paradigms, and active sampling algorithms will support not only the specific domain of SIRA but other adjacent domains in environmental conservation, agricultural forecasting, and smart farm modeling.
“For example, what welearn from our research could be directly applicable to tracing many other illicitly-sourced products and product inputs, including forest risk commodities such as cocoa, soy, and beef,” said L. Monika Moskal, professor at the University of Washington.
The study will have broad and far-reaching impacts on American security and prosperity, as well.
“Many key U.S. adversaries rely on illegal logging to finance their activities,” said Jade Saunders, executive director at World Forest ID. “Detecting and curbing such activities will moderate sources of regional instability and threats to U.S. interests.”
The project will lead to improving geospatial prediction accuracy of product origin and will enable a cost-benefit analysis to minimize future data collection costs and optimize prediction gain. Finally, this project will also positively affect U.S. economic competitiveness by reducing competition with illicit actors and moderating risks to international trade, Ramakrishnan said.
As technology continues to revolutionize industries and alter the nature of everyday life, the future of work can seem unclear. Virginia Tech’s Institute for Creativity, Arts, and Technology (ICAT) is bringing together a wide breadth of expertise to discuss this topic during the Human-Centered Future of Work Symposium.
Virginia Tech’s new Data and Decision Sciences Building has opened its doors to students, faculty, staff, and industry professionals ready to tackle some of the world’s most pressing data challenges. Completed in the summer, the 120,000-gross-square-foot facility houses multiple colleges including the Pamplin College of Business, College of Engineering, and College of Science.
Several faculty from the Department of Computer Science have offices located in the building, along with labs and classrooms that allow students to experience and interact with the latest computational technologies. The new visualization lab features a high-resolution power wall with multi-touch functionality. Coupled with SAGE3 software developed by researchers in the Sanghani Center for Artificial Intelligence and Data Analytics under a $5 million dollar National Science Foundation grant, the high-resolution screen enables the display and organization of large amounts of media, data analytics, and visualizations. Read full story here.
Metro systems are vital to many people’s daily lives, but they face safety or reliability challenges, such as criminal activities or infrastructure disruptions. Real-time threat detection and analysis are crucial to ensure their safety and reliability.
Syuan-Ying (Justin) Wu, a master’s degree student in computer science whose research focuses on social media analytics and software development, is currently part of a research team that is working with the Washington Metropolitan Area Transit Authority (WMATA) to address these issues.
With fellow students at the Sanghani Center and his advisor, Chang-Tien Lu, Wu has been instrumental in developing the MetroScope real-time threat/event detection system that can automatically analyze event development; prioritize events based on urgency; send emergency notifications via emails; provide efficient content retrieval; and self-maintain the system.
“This is a great improvement over many existing systems that can detect the event but cannot analyze it or prioritize it,” Wu said. “And our system offers other advantages like not having to continuously monitor system notifications.”
Wu, who earned a bachelor’s degree in applied mathematics at Fu Jen Catholic University in Taiwan, said this research collaboration with a metropolitan metro system is a good example of what led him to pursue his master’s degree at Virginia Tech and the Sanghani Center. “The exceptional computer science program and distinguished professors have offered me the opportunity to find ways of applying cutting-edge technology to tackle a real-world problem,” he said. “It has been the perfect environment to achieve my goals.”
Projected to graduate this fall, Wu hopes to secure a position as a software engineer.
Ming Jin, an assistant professor in electrical and computer engineering and core faculty at the Sanghani Center has received a National Science Foundation grant to revolutionize the design of learning-enabled, safety-critical systems, with a special focus on power systems.
The grant was awarded under the Safe Learning-Enabled Systems (SLES), a partnership between the NSF, Open Philanthropy, and Good Ventures.
Jin will collaborate with Javad Lavaei, professor in Industrial Engineering and Operations Research at the University of California Berkeley.
The project introduces antifragility, a concept that goes beyond robustness which can be compared to a sturdy structure that remains unyielding in a storm but does not grow or adapt from the experience; or resilience which is like a rubber band: when stretched, it can recover by going back into its original shape.
“We are not merely designing systems to withstand challenges of rare and unpredictable events, but to flourish because of them,” Jin said.
The task of preserving end-to-end safety of the power system will be crucial, Jin said, though it is complex amidst distributional shifts, driven by the growing complexity and unpredictability of the environment.
The project will addresses safety challenges through three interconnected research thrusts. The first thrust targets the creation of proactive, antifragile systems that anticipate and adapt to changes, using advanced techniques such as meta-safe learning and offline reinforcement learning. The second thrust bolsters system antifragility through multi-agent systems, encouraging exploration, cooperation, and distributed control to ensure resilience and safety, even under significant disturbances. The third thrust is devoted to validation and stress testing, employing multi-objective adversarial learning and real-world case studies to better handle rare or unexpected events.
“Our algorithms are more than just learners; they’re evolvers. By turning continual threats into avenues for enhancement, we are redefining what safety in power systems looks like,” he said.
The Amazon–Virginia Tech Initiative for Efficient and Robust Machine Learning will support two Amazon Fellows and five innovative research projects led by Virginia Tech faculty in the 2023-24 academic year that further the initiative’s mission of advancing innovation in machine learning.
An open call for fellowship nominations and faculty projects went out across the Virginia Tech campuses. An advisory committee of Virginia Tech faculty and Amazon researchers selected two Amazon Fellows from 27 nominations — more than double what was received last year — and five faculty projects from 17 submitted proposals. Read full story here.
Faculty are considering how AI models such as ChatGPT can customize learning by producing dynamic case studies or offering instant feedback or follow-up questions. Many are making AI the subject of assignments. They’re asking students to analyze and identify weaknesses in arguments produced by ChatGPT, for instance, or to edit an AI-produced essay with “track changes” on.
That kind of critical thinking about generative AI is vital, said Ismini Lourentzou, assistant professor of computer science in the College of Engineering and core faculty at the Sanghani Center for Artificial Intelligence and Data Analytics. “It’s our responsibility as educators to teach students how to use these tools responsibly, and then understand the limitations of these tools.” Read the full story here.