Twelve ASSET Center Ph.D. Students Named 2026 AWS Fellows

Through the ASSET (AI-Enabled Systems: Safe, Explainable and Trustworthy) Center, 12 exceptional doctoral students advancing the frontiers of trustworthy artificial intelligence will receive $840,000 in funding for research and mentoring support as 2026 Amazon Web Services (AWS) ASSET Fellows. Supported by a long-standing relationship between the ASSET Center and AWS, the fellowship continues to empower emerging leaders whose research addresses the technical, ethical and societal challenges of modern AI systems.

Building on the success of the 2025 AWS ASSET Fellows, whose work spanned areas such as robust machine learning, interpretability and AI safety, the program has further strengthened the ASSET Center’s mission to develop AI systems that are reliable, transparent and aligned with human values. The 2026 cohort reflects both the depth and diversity of research underway across Penn Engineering, with Fellows working at the intersection of machine learning, systems theory and real-world applications.

“We are grateful to AWS AI for their continued support,” says Rajeev Alur, Zisman Family Professor in Computer and Information Science (CIS) and Founding Director of the ASSET Center. “Since the inception of the AWS ASSET Fellows program three years ago, AI technology has rapidly progressed, and its applications have become more pervasive. This has created new challenges in ensuring the trustworthiness of emerging architectures such as Multimodal GenAI systems and Agentic AI systems. This year’s class of Fellows has been selected to explore these new directions, and I eagerly look forward to the results of their research.”

2026 AWS ASSET Fellows

The ASSET Center is proud to recognize the following students as 2026 AWS Fellows:

Ryan Chan, doctoral student in Electrical and Systems Engineering (ESE), is advised by René Vidal, Rachleff University Professor in ESE and in Radiology. Chan’s research focuses on trustworthy and agentic AI, with an emphasis on explainable-by-design methods for visual classification and natural language processing. He recently developed conformal information pursuit, a framework that uses conformal inference to quantify uncertainty in large language model reasoning tasks.

Seewon Choi, doctoral student in Computer and Information Science (CIS), is advised by Rajeev Alur, Zisman Family Professor in CIS. Choi is developing scalable methods for composing symbolic programs with neural networks in neurosymbolic learning frameworks, with applications in robust and interpretable clinical decision-making.

Cassandra (Casey) Goldberg, doctoral student in CIS, is advised by Eric Wong, Assistant Professor in CIS. Goldberg investigates how concepts are represented within foundation models, aiming to provide mechanistic interpretations that reveal when semantic properties are active and improve the transparency of model decision-making.

Mayank Keoliya, doctoral student in CIS, is advised by Mayur Naik, Misra Family Professor in CIS. Keoliya’s work focuses on trustworthy AI for healthcare, developing scalable neural models that leverage multimodal electronic health record data for disease risk prediction and personalized treatment strategies.

Shayan Kiyani, doctoral student in ESE, is advised by Hamed Hassani, Associate Professor in ESE, and George Pappas, UPS Foundation Professor of Transportation in ESE. Kiyani studies the foundations of trust in generative AI, including methods to quantify uncertainty and frameworks for decision-making under uncertainty.

Jiuyao Lu, doctoral student in Statistics (Wharton), is advised by Michael Kearns, National Center Professor of Management & Technology in CIS, and Aaron Roth, Henry Salvatori Professor of Computer & Cognitive Science in CIS. Lu’s research explores safe prediction methods in sequential decision-making settings, focusing on guarantees such as worst-case regret and incentive compatibility.

Josh Ludan, doctoral student in CIS, is advised by Mark Yatskar, Assistant Professor in CIS, and Chris Callison-Burch, Professor in CIS. Ludan works on multimodal learning systems that integrate molecular data and scientific text, with an emphasis on improving interpretability and guiding pretraining through structured knowledge.

Marcus Min, doctoral student in CIS, is advised by Osbert Bastani, Associate Professor in CIS. Min focuses on AI for code, developing benchmarks to evaluate the capabilities and limitations of AI agents in complex coding and theorem-proving tasks.

Ramya Ramalingam, doctoral student in CIS, is advised by Osbert Bastani and Aaron Roth. Ramalingam studies uncertainty quantification in machine learning pipelines to improve the reliability and trustworthiness of automated decision systems.

Yao Tang, doctoral student in CIS, is advised by Jiatao Gu, Assistant Professor in CIS. Tang develops multimodal agentic AI systems capable of reasoning, planning and interacting with complex environments such as coding platforms and web interfaces.

Honam Wong, doctoral student in CIS, is advised by Surbhi Goel, Magerman Term Assistant Professor in CIS, and Enric Boix-Adsera, Assistant Professor in Statistics and Data Science at Wharton. Wong is working on theoretically grounded approaches to distill capabilities from large models into smaller, more efficient systems that can selectively defer to larger models when needed.

Zixuan Yi, doctoral student in CIS, is advised by Zachary Ives, Adani President’s Distinguished Professor in CIS, and Ryan Marcus, Assistant Professor in CIS. Yi investigates semantic operators that combine relational algebra with large language models to enable reliable, explainable and efficient data integration and question answering over the open web.

Advancing Trustworthy AI

The AWS Fellows program continues to play a vital role in fostering innovative research that addresses core challenges in AI trustworthiness, including uncertainty quantification, interpretability, robustness and responsible deployment. By supporting students across disciplines and application domains, from healthcare to scientific discovery to programming systems, the program reflects the ASSET Center’s commitment to advancing AI that can be trusted in high-stakes, real-world settings.

Follow these student research projects and more news from the ASSET Center by visiting their website.