麻豆村

Leena Mathur, a doctoral student in the (LTI), received a two-year fellowship to advance socially intelligent AI systems that can work alongside people in real-world environments. Her research develops algorithms that help robots infer human intent from multimodal cues, such as speech and gesture, enabling them to act proactively in support of human health and well-being.

Yufei Wang, a one-year fellow in the (RI), is addressing one of robotics鈥� most difficult challenges: transferring policies trained in simulation into real-world environments. His research develops scalable methods for sim-to-real learning, including generative simulation pipelines that automate data collection and allow robots to generalize across diverse objects and tasks.

Patrick Callaghan, who received a two-year fellowship in the RI, studies how robots learn from human teachers. His work applies the second-order theory of mind 鈥� the ability to reason about what others believe about themselves 鈥� to improve robot-teacher communication, making learning more intuitive and effective.

Feiyu Gavin Zhu, beginning a one-year fellowship in the RI, investigates how household robots can adapt to individual preferences with limited data. Drawing on cognitive architectures and program synthesis, his work seeks to create modular, teachable policies that allow non-experts to personalize robots quickly and effectively.

Pranjal Aggarwal, supported with a one-year fellowship in the LTI, is developing computer-use agents that can autonomously interact with digital environments, from software development platforms to scientific tools. His vision focuses on creating self-improving agents that not only solve problems but generate and refine new ones, building toward open-ended AI systems with real-world economic impact.

Emmy Liu, a two-year fellow in the LTI, studies how reinforcement learning can improve reasoning in large language models. Her work seeks to enable AI systems to generate and refine scientific ideas or creative writing with more principled, scalable feedback, extending methods like reinforcement learning from human feedback into open-ended domains.

Shiyi Zoe Du, awarded a two-year fellowship in the Ray and Stephanie Lane Computational Biology Department(opens in new window), is designing failure-adaptive laboratory intelligence 鈥� agentic AI systems that treat experimental setbacks as opportunities for discovery. Her research explores next-generation AI systems that integrate science, technology and medicine into a self-updating platform driving biomedical innovation and more.

Yizhou Zhao, beginning a one-year fellowship in the in the College of Engineering, explores how digital twins 鈥� high-fidelity virtual replicas of the physical world 鈥� can be automatically generated from videos. His research could transform lab automation and material science by enabling robots to build predictive models simply by observing experiments.