Jiatong Gu
Computer and Information Science; GRASP Lab; ASSET Center
University of Pennsylvania
About Professor Jiatong Gu
Professor Jiatong Gu is an esteemed Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania, a prestigious institution renowned for its cutting-edge research and innovation in technology and engineering. Affiliated with the GRASP Lab and the ASSET Center, the department excels in areas such as artificial intelligence, robotics, and computer vision. The University of Pennsylvania fosters an intellectually stimulating environment that encourages interdisciplinary collaboration, making it an ideal setting for groundbreaking research and the development of transformative technologies. Professor Gu's role within this dynamic department positions him at the forefront of advancements in computer science.
🧬Research Focus
Professor Gu leads the Generative Multimodal Learning and Reasoning (GMLR) Lab, where his research focuses on pioneering generative models and world models that facilitate enhanced decision-making and planning in complex environments. His work in multimodal learning and reasoning encompasses a range of applications, including vision-language models and agent learning, contributing significantly to the fields of robot learning and embodied intelligence. By exploring reinforcement learning and imitation learning techniques, Professor Gu aims to develop intelligent systems capable of long-horizon prediction and sophisticated task decomposition. The potential breakthroughs in these areas promise to revolutionize how machines understand and interact with the world, leading to significant advancements in robotics and AI.
🎓Student Fit & Career
Graduate students interested in joining Professor Gu's research team will thrive in an environment that values curiosity, creativity, and collaboration. Ideal candidates should possess a strong foundation in machine learning, computer vision, or artificial intelligence, along with a passion for exploring innovative solutions in multimodal generation and reasoning. Under his academic mentorship, PhD students will have the opportunity to engage in impactful research that can lead to diverse career paths in academia, industry, or research institutions, all while contributing to the evolution of intelligent agents and embodied AI systems.
Research Areas
Rating Breakdown
Reviews (1)
I joined the lab as a short-term collaborator on multimodal agent benchmarks. Supervision included clear engineering milestones and frequent design reviews; the group emphasizes building reproducible demos. Lab resources and industry-relevant problem framing are strengths—suitable for students aiming at applied agent systems.
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