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.
The Innovation Campus Academic Building One (new home to the Sanghani Center for Artificial Intelligence and Data Analytics) set to open early next year, has cutting-edge features and is being built with technology befitting of a university committed to expanding the workforce in the areas of computer science and computer engineering. Read full story here.
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.
Insufficient logical reasoning capability in AI can be a disadvantage when trying to tackle complex problems like diagnosing and treating rare diseases and detecting and disrupting financial fraud, said Dawei Zhou, assistant professor in the Department of Computer Science and a core faculty member at the Sanghani Center for Artificial Intelligence and Data Analytics.
Developing AI that can function more like human intelligence and learn from complicated real-world situations is the focus of his recently-announced National Science Foundation Faculty Early Career Development (CAREER) award.
Read full story here.
When Lance Collins was named vice president and executive director of the Virginia Tech Innovation Campus in August 2020, he knew that in addition to tailoring the campus approach to the demands of the greater Washington, D.C., metro area, he needed to let the strengths of the faculty lead the way.
“Our goal is to build a vibrant community perfectly positioned to connect talented students and researchers with Northern Virginia’s growing tech ecosystem,” said Collins. “When it comes to identifying forward-looking, impactful research areas, faculty are the best guides.”
The 11 Virginia Tech faculty members from the College of Engineering who joined the Innovation Campus in August 2022, along with seven new faculty hires, bring with them expertise and add to some of the university’s top research strengths — the focus areas of Artificial Intelligence and Machine Learning, Wireless, and Quantum. In addition, Innovation Campus leadership sought input from industry leaders on the Campus Advisory Board, resulting in a fourth area of focus, Intelligent Interfaces.
Artificial Intelligence/Machine Learning presents some of the most talked-about and pressing opportunities and challenges in our modern world. Through the Sanghani Center for Artificial Intelligence and Data Analytics, professor of computer science and Director Naren Ramakrishnan and his team are working toward solutions in intelligence analysis, sustainability, and electronic medical records.
Artificial Intelligence/Machine Learning presents some of the most talked-about and pressing opportunities and challenges in our modern world. Through the Sanghani Center for Artificial Intelligence and Data Analytics, professor of computer science and Director Naren Ramakrishnan and his team are working toward solutions in intelligence analysis, sustainability, and electronic medical records.
Read the full story here.
For Sanghani Center students interning at tech companies, corporations, and research labs from coast to coast, the benefits are threefold: they gain on the job work experience; they contribute to solving real-world problems; and what they learn enhances their own research interests.
Summer 2024 finds them at places as varied at GE Healthcare, Amazon, National Renewable Energy Laboratory, the Washington Post, Citigroup, Honeywell, Google, and Honda, to name only a few.
“As evidenced by this year’s internship opportunities, businesses in all fields are relying more and more on data and AI to improve efficiency and gain insights and our students are well prepared to help them accomplish their goals in that regard,” said Naren Ramakrishnan, Sanghani Center director.
Following is a list of Sanghani Center interns – where they are working and what they are doing:
Eman Abdelrahman, a Ph.D. student in computer science, is a graduate student intern at Lawrence Livermore National Lab in Livermore, California. She is working on enhancing autonomous Graphical User Interface (GUI) intelligent agents leveraging large multimodal models. Her advisor is Ismini Lourentzou.
Sareh Ahmadi, a Ph.D. student in computer science, is an intern at Blue Triangle Technologies, Inc., Mechanicsville, Virginia. She is using a large language model (LLM) as she works on building and prompt engineering of an artificial intelligence (AI) agent and chatbot in the company’s portal for customers to get insights from their data. Her advisor is Edward Fox.
Ahmed Aredah, simultaneously pursuing a Ph.D. in civil engineering and a master’s degree in computer science, is an infrastructure software development intern at Moffatt & Nichol in the FlexTerm Simulation Division in Norfolk, Virginia. He is working on simulating ports operations, full stack/DevOps development, and data analysis. His advisors are Hesham Rakha and Hoda Eldardiry.
Vasanth Reddy Baddam, a Ph.D. student in computer science, is a research scientist intern at the Honda Research Institute in Detroit, Michigan. He is currently developing frameworks and algorithms to enable robots to navigate through human spaces more effectively and to make the robots more socially compliant. His advisor is Hoda Eldardiry.
Rayan Bouhal, an undergraduate student in computer science, is a software engineering intern at Honeywell HCE in Richmond, Virginia. He is working on the Niagara Framework. His advisor is Hoda Eldardiry.
Si Chen, a Ph.D. student in electrical and computer engineering, is a research engineer intern on the trust layer team at Salesforce EinsteinGPT in San Francisco, California. She is working on content moderation for large language models. Her advisor is Ruoxi Jia.
Yusuf Dalva, a Ph.D. student in computer science, is a research scientist/engineer intern at Adobe Research, Creative Media Lab (CML) in Seattle, Washington. He is working on harmonized, multi-layer image generation. His advisor is Pinar Yanardag.
Pradyumna Upendra Dasu, a master’s degree student in computer science, is a machine learning intern at Blue Triangle Technologies, Inc. in Mechanicsville, Virginia. He is contributing to the building of a machine learning pipeline for yielding actionable insights from web performance data. His advisor is Edward Fox.
Tanner Fredieu, a Ph.D. student in electrical and computer engineering, is a software systems engineer intern in the Mission Systems Engineering Division at NASA’s Jet Propulsion Laboratory in Pasadena, California, where he is leading the design, development, and deployment of deep learning-based computer vision systems for satellites operating in low-Earth orbit. Fredieu’s internship, which began last summer, was extended throughout the 2023-2024 academic year and will end in September. His advisor is Lynn Abbott.
Alvi Md Ishmam, a Ph.D. student in computer science, is an artificial intelligence/machine learning Ph.D. intern at GE Healthcare in San Ramon, California. He is working to develop vision language model on 2d/3d bio medical images. His advisor is Chris Thomas.
Fausto German Jimenez, a Ph.D. student in computer science, is a data science and analytics intern on the TV+ Revenue and Subscription team at Apple in Culver City, California. He is applying advanced data science techniques to drive engagement and subscriptions to the Apple TV+ service. His advisor is Chris North.
Feiyang Kang, a Ph.D. student in electrical and computer engineering, is a research intern at NVIDIA Research, in Santa Clara, California. He is on the augmented video (AV) perception team, researching data-centric problems in perception for autonomous vehicles. His advisor is Ruoxi Jia.
Vanshaj Khattar, a Ph.D. student in electrical engineering, is a graduate research intern at the National Renewable Energy Laboratory (NREL) in Golden, Colorado. He is working on the problem of critical load restoration during power blackouts caused by extreme events. More specifically, he is using reinforcement learning techniques to address uncertainties in load demands and network topology during the critical load restoration process, an approach aimed at improving the resilience of power systems in the face of unforeseen disruptions. His advisor is Ming Jin.
Sha Li, a Ph.D. student in computer science, is an engineering and business operations intern at the Washington Post in Washington, D.C. She is on the Personalization and Artificial Intelligence team, working on open-domain question answering tools. Her advisor is Naren Ramakrishnan.
Harish Babu Manogaran, a master’s degree student in computer engineering, is a perception intern at a stealth startup in Palo Alto, California. He is working on computer vision for augmented reality (AR)/virtual reality (VR) applications. His advisor is Anuj Karpatne.
Faizan Manzoor, a master’s degree student in electrical and computer engineering, is a Power System Engineering Division intern at Mitsubishi Electric Power Products Inc in Warrendale, Pennsylvania. His advisor is Ming Jin.
Makanjuola Ogunleye, a Ph.D. student in computer science, is a data science intern in the Artificial Intelligence Division at Intuit Inc., Mountain View, California. He is working on improving the experiences of Intuit Assist (one of Intuit’s most powerful generative AI products) for customers, using large language models and retrieval augmented generation. His advisor is Ismini Lourentzou.
Shailik Sarkar, a Ph.D. student in computer science, is an engineering and business operations intern on the Personalization and Artificial Intelligence team at the Washington Post in Washington, D.C. He is exploring innovative applications of artificial intelligence and natural language processing in news consumption. His advisor is Chang Tien-Lu.
Medha Sawhney, a Ph.D. student in computer science, is a deep learning automation intern at NVIDIA, working with the Deep Learning Focus Group, in Santa Clara, California. She is responsible for developing computer vision and reinforcement learning solutions to automate graphics processing unit (GPU) validation. Her advisor is Anuj Karpatne.
Gurkirat Singh, a master’s degree student in computer science, is a sales and trading summer analyst at Citigroup’s Markets Division in Houston, Texas. He is working on the Electrical Reliability Council of Texas (ERCOT) Power Trading desk where he is developing quantitative tools and strategies to improve trade research and execution. His advisor is Hoda Eldardiry.
Anushka Sivakumar, a master’s degree student in computer science, is a software development engineer intern at Berkley Alternative Markets Tech, W.R. Berkley Corporation in Virginia. She is a backend engineer supporting the development of innovative applications in the commercial insurance industry. Her advisor is Chris Thomas.
Wenjia Song, a Ph.D. student in computer science, is a software engineer intern at Google Cloud in Sunnyvale, California. She is developing unsupervised approaches for Gmail spam detection. Her advisor Danfeng (Daphne) Yao.
Afrina Tabbasum, a Ph.D. student in computer science, is an applied science intern at Amazon in Bellevue, Washington. She is working on satellite imagery with the Geospatial Science team. Her co-advisors are Hoda Eldardiry and Ismini Lourentzou.
Zain ul Abdeen, a Ph.D. student in electrical engineering, is a graduate research intern at the National Renewable Energy Laboratory (NREL), in Golden, Colorado. He is working on machine learning based intrusion detection and mitigation strategies to improve smart grid resilience. His advisor is Ming Jin.
A teenager sends some explicit pictures requested by a new online love interest, only to get a blackmail threat from someone in another country.
A tween goes to meet a teen girl who bought her jewelry online, but she’s greeted by a man she doesn’t know.
From the threat of “sextortion” to cybergrooming, children and teens face a growing range of online crimes, and three Virginia Tech researchers are working to make the digital world safer for them.
Under the auspices of a National Science Foundation (NSF) grant, Jin-Hee Cho and collaborators Lifu Huang and Sang Won Lee in the Virginia Tech Department of Computer Science want to develop a technology-assisted education program to prevent online sexual abuse of children and teens.
Huang, assistant professor of computer science and core faculty at the Sanghani Center for Artificial Intelligence and Data Analytics, will work on various aspects of the AI that powers the bots.
“I have a very strong interest and a strong belief in using AI techniques to solve social problems,” Huang said.
He will leverage his previous research in conversational AI, which uses Large Language Models to make the educational bots believable as humans interacting with one another.
Read full story here.
Attendees at two major conferences this summer will be hearing about Haohui Wang’s research.
Wang, a Ph.D. student advised by Dawei Zhou, will be traveling to Europe to present her collaborative work, “EvoluNet: Advancing Dynamic Non-IID Transfer Learning on Graphs,” at the 2024 International Conference on Machine Learning (ICML) in Vienna, Austria, in July; and ““Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization” at the 2024 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining in Barcelona, Spain, in August.
She will also present the ICML paper remotely at a Deloitte AI journal club, a group within Deloitte that discusses state-of-the-art AI techniques.
“As a Sanghani Center student, I have had the excellent opportunity of working with leading researchers in the field of artificial intelligence,” said Wang. “The center’s interdisciplinary environment fosters collaboration with experts across various related areas, enhancing the real-world application of my research.”
Her research focuses on advancing machine learning techniques, particularly in the areas of transfer learning and long-tail learning, proposing a framework to improve model performance on both head and tail classes. Both papers she is presenting at the summer conferences consider the challenges related to data, especially in real-world scenarios where labeled data is scarce.
“This makes our work applicable to various real-world scenarios, such as financial fraud detection. In financial transaction networks, while most transactions – such as credit card payments, and wire transfers – are normal, the rare occurrences of fraudulent transactions which include money laundering, synthetic identity transactions, are crucial to detect. We achieve improved detection by leveraging the models we have developed,” said Wang.
Her interest in this research area began during her undergraduate studies, particularly through coursework focused on finding the internal relationship and mechanism between daily things.
“I found it enjoyable to leverage the power of data science to solve real-world problems. This interest deepened during my master’s studies, leading me to focus my research efforts in machine learning ever since,” she said.
Wang earned a bachelor of science degree from Shandong University and a master of science degree from Zhejiang University, both in China.
Projected to graduate in May 2027, Wang said she will explore any opportunities in academia or industry that provide an opportunity to continue her research.
Anuj Karpatne’s project “Lake-GPT: Building a Foundation Model for Aquatic Sciences” is one of the first 35 to be supported with computational time through the National Artificial Intelligence Research Resource (NAIRR) Pilot program, marking a significant milestone in connecting U.S. researchers and educators to computational, data, and training resources needed to advance artificial intelligence (AI).
The NAIRR Pilot awards – a joint effort led by the National Science Foundation in collaboration with other U.S. federal agencies – are a result of President Joe Biden’s landmark Executive Order on the Safe, Secure and Trustworthy Development and Use of AI and provide researchers and students access to key AI resources and data.
Karpatne, associate professor in the Department of Computer Science and core faculty at the Sanghani Center for Artificial Intelligence and Data Analytics, was among 10 award recipients invited to speak at a White House event hosted by the Office of Science and Technology Policy on Opportunities at the AI Research Frontier on May 6 announcing the launch of the NAIRR Pilot program. Karpatne also was one of two recipients invited to give longer talks on their NAIRR projects at the AI Expo for National Competitiveness in Washington, D.C., hosted by the Special Competitive Studies Project.
A second group of NAIRR Pilot awards, announced in late May, include Debswapna Bhattacharya, associate professor of computer science, and Xuan Wang, assistant professor of computer science and core faculty at the Sanghani Center.
Read full story here.
Virginia researchers are working to ensure people feel safer and that their privacy is more protected on computer networks and other devices through a new inclusive cybersecurity program funded by the Commonwealth Cyber Initiative (CCI).
CCI awarded 11 projects as part of its 2024 Addressing Inclusion and Accessibility in Cybersecurity Program.
Three faculty at the Sanghani Center for Artificial Intelligence and Data Analytics were among the recipients: Chang-Tien Lu, Abhijit Sarkar, and Lynn Abbott.
Read full story here.
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.
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The Society for Industrial and Applied Mathematics (SIAM) has named Layne Watson, a professor in three departments at Virginia Tech – computer science, mathematics, and aerospace and ocean engineering – and core faculty at the Sanghani Center for Artificial Intelligence and Data Analytics, part of the 26-member 2024 class of SIAM fellows.
SIAM is an international community of more than 1,400 individual members and nearly 500 academic, manufacturing, research and development, service and consulting organizations, government, and military are institutional members. Members are nominated to the fellowship program in recognition of their outstanding research and service to the community. Through their various contributions, SIAM fellows form a crucial group of individuals helping to advance the fields of applied mathematics, computational science, and data science.
A SIAM member since 1976, Watson is being recognized particularly for pioneering the theoretical development, algorithm design, software implementation, and application of homotopy methods. Homotopy theory is part of topology, a branch of theoretical mathematics.
Read full story here.