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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.

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. 

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. 

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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.

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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.

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“Sticks and stones may break my bones,” the old adage goes. “But words will never hurt me.” 

Tell that to Eugenia Rho, assistant professor in the Department of Computer Science, and she will show you extensive data that prove otherwise.

Her Society + AI & Language Lab has shown that

• Police language is an accurate predictor of violent interactions with Black motorists.
• Broadcast media bias and social media echo chambers have put American democracy at risk.

Now, Rho’s research team in the College of Engineering has turned to another question: what effects did social media rhetoric have on COVID-19 infection and death rates across the United States, and what can policymakers and public health officials learn from that?

“A lot of studies just describe what’s happening online. Often they do not show a direct link with offline behaviors,” Rho said. “But there is a tangible way to connect online behavior with offline decision making.”

Rho is also an affiliate faculty member at the Sanghani Center for Artificial Intelligence and Data Analytics.

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Virginia Tech’s week of commencement ceremonies is underway! The Graduate School Commencement ceremony was held Wednesday, May 8; the main ceremony is being held today, Friday, May 10; and the Washington, D.C. area ceremony will be held on Sunday, May 12.  

“Graduation is always a bittersweet time for faculty as we applaud our students’ accomplishments,” 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. “We very proud of all of them but saying good-bye is not so easy and we are always happy when they stay in touch – as many of them do — to let us know where their research is leading them.”

The following Sanghani Center students are among those who are receiving degrees:

Ph.D. Graduates

Abdulaziz Alhamadani, advised by Chang-Tien Lu, has earned a Ph.D. in computer science. His research interests mainly focus on developing efficient and applicable methods of training machine learning models for real-world applications such as pandemic prediction, drug overdose crises, and crisis management in various industries. His additional research areas include natural language processing, such as text classification and building large corpora for low-resource languages, machine learning ethics, and event detection.The title of his dissertation is “Integrated Predictive Modeling and Analytics for Crisis Management.” Alhamadani has joined the Computer Science Department at Florida Polytechnic University as an assistant professor.

Lulwah AlKulaib, advised by Chang-Tien Lu, has earned a Ph.D. in computer science. Her research focuses on social media analysis, machine learning, and natural language processing with a special interest in Arabic. The title of her dissertation is “Analyzing Networks with Hypergraphs: Detection, Classification and Prediction.” AlKulaib has joined Kuwait University as an assistant professor in computer science.

Hongjie Chen, advised by Hoda Eldardiry, has earned a Ph.D. in computer science. His research lies in the areas of graph neural networks, time-series analysis, and recommendation systems. The title of his dissertation is “Graph-based Time-series Forecasting in Deep Learning.”

Jiaying Gong, advised by Hoda Eldardiry, has earned a Ph.D. in computer science. Her research focuses on multisource machine learning and natural language processing. The title of her thesis is “Few-Shot and Zero-Shot Learning for Information Extraction.” Gong will join coreAI, eBay, in New York City, as an applied researcher.

Jianfeng He, advised by Chang-Tien Lu, has earned a Ph.D. in computer science. His research focus on computer vision centers on guided image editing. He also focuses on natural language processing, studying uncertainty analysis in its applications (e.g., text classification, few-shot learning, named entity recognition, and text summarization). The title of his dissertation is “Uncertainty Estimation in Natural Language Processing,” for which he received the Department of Computer Science Achievement Award for Best Ph.D. Research. In June, He will join Amazon as an applied scientist in Seattle, Washington. 

Ola Karajeh, advised by Edward Fox, earned a Ph.D. in computer science. Her research interests are graph machine learning, natural language processing, Twitter analysis, and public health. The title of her dissertation is “Improving Text Classification Using Graph-based Methods.”

Andreea Sistrunk, advised by Naren Ramakrishnan, has earned a Ph.D. in computer science. Her research interest is in human-computing Interaction with all forms that data science takes in information processing and its impact on society. She is equally interested in education and methods leveraging the state of the art in pedagogy and andragogy in computer science, advanced math, and engineering. The title of her dissertation is “Designing Human-Centered Collaborative Systems for School Redistricting.” Sistrunk will continue her work with the Geospatial Research Laboratory in the Washington, D.C. area, as a physical research scientist.

Master of Science Degree Graduates

Cho-Ting (Amanda) Lee, advised by Naren Ramakrishnan, has earned a master’s degree in computer science. Her research interests are in data mining and machine learning, with a focus on trade data analytics and anomaly detection. The title of her thesis is “Can an LLM find its way around a spreadsheet?”

Jiayue Lin, advised by Chris North, has earned a master’s degree in computer science. His research focus is on visual analytics and artificial intelligence, with a particular interest in refining deep learning-based image projections using semantic interaction methods. The title of his thesis is “ImageSI: Semantic Interaction for Deep Learning Image Projections.” 

Daniel Palamarchuk, advised by Chris North, has earned a master’s degree in computer science. His research focuses on visualizing temporal text, document, and topic data using pre-transformer embedding methods. The title of his thesis is “Temporal Topic Embeddings with a Compass.” Palamarchuk will work as a programming teacher in the Northern Virginia area and plans on pursuing a Ph.D. 

Ramaraja Ramanujan, advised by Edward Fox, has earned a master’s degree in computer science. His research focuses on data analysis of geospatial and administrative data, conducting statistical verifications and simulations, and visual analytics. The title of his thesis is “Improving Rainfall Index Insurance: Evaluating Effects of Fine-Scale Data and Interactive Tools in the PRF-RI Program.” Ramanujan will join Microsoft in Redmond, Washington, as a software engineer. 

Chia-Wei Tang, advised by Chris Thomas, has earned a master’s degree in computer science. His research focuses on the development of misinformation detection utilizing multimodal reasoning. The title of his thesis is “M3D: MultiModal MultiDocument Fine-Grained Inconsistency Detection.” Tang is joining Juniper Networks in Sunnyvale, California, as a software engineer. 

Lemara Williams, advised by Chris North, has earned a master’s degree in computer science. Her research centers around visualizing changes in projections over time. The title of her thesis is “TimeLink: Visualizing Diachronic Word Embeddings and Topics.” Williams will continue her studies and is beginning a computer science Ph.D. program in the Fall at the Washington University in St. Louis. 

Xiaona Zhou, advised by Ismini Lourentzou, has earned a master’s degree in computer science. Her research focuses on the applications of data science and machine learning. The title of her master’s thesis is “Hierarchical Bayesian Dataset Selection.” Zhou will be pursuing a Ph.D. in computer science at the University of Illinois Urbana-Champaign.

Layne Watson, professor of computer science in the College of Engineering at Virginia Tech, has been conferred the title of professor emeritus by the Virginia Tech Board of Visitors.

Watson is also a core faculty member at the Sanghani Center for Artificial Intelligence and Data Analytics.

The emeritus title may be conferred on retired faculty members who are specially recommended to the board by Virginia Tech President Tim Sands in recognition of exemplary service to the university. Nominated individuals who are approved by the board receive a copy of the resolution and a certificate of appreciation.

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