Dr. Li "Harry" Zhang 张力

Assistant Professor

Dr. Zhang focuses on Natural Language Processing and Artificial Intelligence, with interests in planning and reasoning using Large Language Models.

Cassie Huang

PhD Student

Ceyhun Efe Kayan

PhD Student

Ceyhun focuses on applying mechanistic interpretability techniques to uncover reasoning patterns, provide robustness and improve planning and formalization performance of LLMs.

Brian Tomasette

PhD Student

Brian is researching how neuro-symbolic AI systems can be applied in the marketing and advertising industry. Prior to this Brian spent 24+ years at AOL, Google, Meta, and Amazon along with several startups building ad targeting, ranking, and campaign management systems. Applying LLMs and large neural networks to both the human (or agent) campaign management context and the ad decisioning system is his key research area.

Prabhu Prakash Kagitha

MS Student

Rikhil Amonkar

UG Student

Stuti Mohan

UG Student

Renxiang Wang

UG Intern

Renxiang is studying how to turn LLMs into modular, interpretable systems, where each component handles a specific reasoning skill. The goal is to design multi-agent pipelines that improve accuracy, robustness, and transparency in complex tasks.

Kevin Nguyen

MS Intern

Jianing Yin

MS Intern

Jianing is an MS student in Computer Science at the University of Pennsylvania. Her research focuses on building capable and socially aware AI agents, spanning LLM evaluation, multi-agent reasoning, and human-AI interaction. She is broadly interested in advancing AI systems toward robust, autonomous agents that generalize across complex real-world tasks.

Jiayi Zhang

MS Intern

Jiayi is a graduate student in the Khoury College of Computer Sciences at Northeastern University. Her research focuses on building AI systems that achieve bidirectional alignment with humans and can co-improve over time. She is particularly interested in understanding and explaining complex model behaviors in human-interpretable ways and building human-like LLM-based user simulators to bridge RL training with real-world interaction.

Bo Sun

UG Intern

Yewon Lee

MS Student

Chimezie Maduno

MS Intern

Chimezie is studying how to turn LLMs into modular, interpretable systems, where each component handles a specific reasoning skill. The goal is to design multi-agent pipelines that improve accuracy, robustness, and transparency in complex tasks.

Wenhao Hu

Intern

Muyu He

Intern → Collinear AI

Muyu He is working at collinear.ai as a research scientist focusing on post training and mechanistic interpretability. His research focus is primarily on efficient training paradigms and understanding attention-related latent space manifold transformations.

Liancheng Gong

Intern → PhD student at University of Maryland

Yuan Yuan

Intern, MS student at University of Pennsylvania

Yuan Yuan is a MS student in Computer and Information Science and Data Science at the University of Pennsylvania. He has experience in both NLP and multimodality, with research interests in reasoning, personalization, and LLM agents.