Virginia Tech’s weeklong (May 13-17) commencement ceremonies in various locations are celebrating the Class of 2026 and their families.

In all, 1,879 students are receiving graduate degrees including 265 doctoral recipients hooded at the Blacksburg commencement and an additional 12 in the greater Washington, D.C., area.

“We are very proud of all our graduates at the Sanghani Center,” said Naren Ramakrishnan, University Distinguished Professor of Computer Science at Virginia Tech, and director of the Sanghani Center for Artificial Intelligence and Data Analytics. “They have worked on interesting and impactful problems during their time here and made significant contributions to our research community. We wish them continued success as they leave Virginia Tech to chart new paths.”

The following are among Sanghani Center’s Ph.D. graduates:

Sara Alsalamah, advised by Chang-Tien Lu, earned a Ph.D. in Computer Science. Her research focuses on the VHealth Suite, a secure and intelligent patient-centered framework for virtual hospital ecosystems that integrates medical imaging, clinical data, operational intelligence, and interactive AI with embedded security and access control. Her dissertation is titled ``VHealth Suite: A Unified, Secure, and Intelligent Patient-Centered Framework for Legacy System Integration in Virtual Hospital Ecosystems.'' Alsalamah will join Al-Imam University in Riyadh, Saudi Arabia, as an assistant professor.

Fanglan Chen, advised by Chang-Tien Lu, earned a Ph.D. in computer science. She also earned two graduate certificates: one in Data Analytics and one in Urban Computing through the National Science Foundation-sponsored UrbComp program. Her research interests span graph learning, urban computing, and spatiotemporal data mining, with a focus on developing data-driven approaches to model multi-scale urban systems and support reliable, actionable urban decision-making. The title of her dissertation is “Graph Learning for Urban Computing: Techniques and Applications.”

Xiaohan Ding, advised by Eugenia Rho, earned a Ph.D.in computer science. His research focuses on exploring the intersection of computational social science, computational linguistics, and natural language processing to enhance human-AI collaboration. Specifically, he investigates the influence of media language on social discourse and the development of large language model (LLM)  tools to facilitate constructive online interactions. The title of his dissertation is "Large-Scale Online Conversations About Public Health: Predicting Real-World Outcomes." Ding is joining Georgia Tech in Atlanta, Georgia, as a postdoc researcher. 

Eslam Hussein, advised by Chris Thomas, earned a Ph.D. in computer science. His research focuses on understanding digital misinformation, including how algorithmic systems shape misinformation exposure, how narratives influence the spread of health misinformation, and how multimodal machine learning and graph-based reasoning could be used to detect deceptively edited videos. The title of his dissertation is “Understanding Digital Misinformation Across Platforms and Modalities.”

Alvi Md Ishmam, advised by Chris Thomas, earned a Ph.D. in computer science. His primary research interest lies in trustworthy multimodal large language models. The title of his dissertation is “Towards Adversarial Robustness in the Era of Multimodal Large Language Models: Exploring Attack and Defense Strategies.” Ishmam is joining Argonne National Lab in Lemont, Illinois, as a postdoc research scientist. 

Shravya Kanchi, advised by Danfeng (Daphne) Yao, earned a Ph.D. in computer science. Her research focuses on leveraging generative artificial intelligence for cybersecurity, including synthetic data generation for security machine learning tasks, safe fine-tuning of large language models, and multi-agent frameworks for automated vulnerability detection and testing. The title of her dissertation is "Generative AI for Hardening Security: From Training Data Augmentation, Language Alignment, to Code Vulnerability Demonstration."

Minqian Liu, advised by Lifu Huang, earned a Ph.D. in computer science. His primary research interests lie in natural language processing and multimodal learning, more specifically in the evaluation and post-training of large generative models. The title of his dissertation is “Holistic and Generalizable Evaluation of Generative Models." Liu is joining Microsoft in Redmond, Washington, as a senior applied scientist.

Reza Mazloom, co-advised by Lenwood Heath at the Sanghani Center and Boris A. Vinatzer in the School of Plant and Environmental Sciences, earned a Ph.D. in computer science. His research focuses on data-driven prokaryotic genome organization and identification, using Life Identification Numbers (LINs) to cluster genomes, compare them with taxonomy, and support rapid strain-level query assignment. The title of his dissertation is “Data-Driven Prokaryotic Genome Identification: LIN Assignment, Taxonomy Correspondence, and Deployment.”

Makanjuola Ogunleye, advised by Ismini Lourentzou, earned a Ph.D. in computer science. His research focuses on developing collaborative artificial intelligence (AI) agents that are trustworthy and can learn, reason, and communicate reliably across language, vision, and embodied environments. He investigated how agents can use grounded communication signals for vision-language learning, reduce hallucinations through inference-time grounding in 3D environments, and collaborate more efficiently through latent residual belief sharing in multi-agent systems. The title of his dissertation is “Towards Trustworthy Collaborative Agents.”

Shailik Sarkar, advised by Chang-Tien Lu, earned a Ph.D. in computer science. His research focuses on spatial data mining and application of machine learning in health analytics. The title of his dissertation is “Explainable AI for Social Good: Applications in Mental Health, Public Health Risk, and Environmental Traceability.” Sarkar has joined Florida Polytechnic University in Lakeland, Florida, as assistant professor in the Department of Data Science and Business Analytics.

Longfeng Wu, advised by Dawei Zhou, earned a Ph.D. in computer science. Her research focuses on trustworthy and generative recommender systems with large language models, aiming to bridge theoretical innovation and real-world impact. The title of her dissertation is “Towards the Next Generation of Recommendation Systems: Exploration, Trustworthiness, and Context-Aware Generation.” Wu will join Amazon in Sunnyvale, California, as an applied scientist.

Raquib Bin Yousuf, advised by Naren Ramakrishnan, earned a Ph.D. in computer science. His research develops large language model (LLM) systems for complex analytical reasoning. His work explores how LLMs can organize evidence across documents, retain relational structure over long contexts, retrieve more reliable context using metadata, and support human-guided analysis of structured data. The title of his dissertation is “Improving LLM Reasoning and Retrieval for Structured and Complex Information Spaces.”

Jingyi Zhang, advised by Lenwood Heath, earned a Ph.D. in computer science. Her research focuses on computational biology, graph theory, and bioinformatics, with an emphasis on developing machine learning frameworks for gene regulatory network inference to understand stress responses in desiccation-tolerant plants. The title of her dissertation is "Unraveling Gene Regulation in Sea Urchins and Resurrection Plants through Integrative Network-Based Approaches."

Shuaicheng Zhang, advised by Dawei Zhou, earned a Ph.D. in computer science. His research focuses on open-world graph learning and foundation-model reasoning over structured knowledge, with an emphasis on agentic artificial intelligence (AI) systems, memory-augmented large language models (LLMs), graph-grounded reasoning, multimodal foundation models, and scalable real-world AI deployments. The title of his dissertation is “Towards Open World Graph Learning and Applications.” Zhang is a recipient of the KDD 2025 Best Paper Award in the Benchmark and Dataset Track. He is joining LinkedIn in Sunnyvale/Mountain View, California, as an AI researcher working on industrial-scale agentic AI systems.

The following are included in Sanghani Center’s master’s degree graduates:

Najibul Haque Sarker, advised by Chris Thomas, earned a master's degree in computer science. His research interests lie in building post-training techniques to increase safety and reasoning capabilities in large-scale unimodal and multimodal generative models. The title of his thesis is "Immunizing Models Against Harmful Long-Horizon Fine-tuning."

Gaurav Srivastava, advised by Xuan Wang, earned a master's degree in computer science. His research focuses on building efficient agentic artificial intelligence (AI) systems with small language models, especially making compact models reason well, use tools, plan steps, and complete tasks autonomously without relying on massive compute. His work lies at the intersection of natural language processing, efficient large language model (LLM) reasoning, model efficiency, LLM evaluation, and agentic systems. The title of his thesis is “Enabling Small Language Models as Efficient and Capable Agents.” Srivastava is joining Dell Technologies in Austin, Texas, as a research engineer 2. 

Aafiya Shamshad Hussain, advised by Chris Thomas, earned a master's degree in computer science. Her research interests are adversarial attacks, multimodal machine learning, large language model (LLM) reasoning, and evaluation. She has studied model vulnerabilities to adversarial input perturbations and their effects on downstream reasoning and has collaborated on research exploring reasoning and agentic capabilities of LLMs. The title of her thesis is "SoundBreak: Studying vulnerabilities exposed by Audio Adversarial Attacks on Trimodal Models.” Hussain is joining Robot Toolworx in Rutherford, New Jersey, as a machine learning engineer.

Mehmet Koruturk, advised by Ming Jin, earned a master’s degree in electrical engineering. His research focuses on reliable, resilient, and sustainable artificial intelligence (AI)-driven energy systems, with particular emphasis on reinforcement learning, power systems optimization, distributed energy resources, and intelligent energy management under uncertainty. He has worked on developing benchmarking and evaluation frameworks for reinforcement learning applications in sustainable energy systems and has explored resilient control and decision-making methods for next-generation renewable and smart grid technologies. The title of his thesis is “Reinforcement Learning Benchmarking for Sustainable Energy Systems: Perturbation Robustness, Safety Constraints, and Multi-Agent Coordination.” Koruturk will contribute to national energy initiatives as a renewable energy specialist with the Turkish Ministry of Energy and Natural Resources, while pursuing a Ph.D. in the field of sustainable and intelligent energy systems. 

Eunice Son, advised by Naren Ramakrishnan, earned a master's degree in computer science. Her interests lie in applying artificial intelligence (AI) and machine learning to data-driven problems in finance, economics, and socio-technical systems with experience in time-series modeling, predictive analytics, and algorithmic bias. The title of her thesis is “A Schema-aware Harness for Tabular Reasoning with Language Models.”