Jiayuan Mao 茅佳源

News

• To junior PhD/master/undergraduate students: if you would like to chat about life, career plan, or research ideas related to AI/ML, feel free to email me to schedule a meeting. I will dedicate 30 mins every week for such meetings. I encourage students from underrepresented groups to reach out and will prioritize these meetings.

  Underrepresented groups: including but not limited to gender/racial/ethnic minority groups.

• I'm co-organizing the Workshop on Planning in the Era of LLMs at AAAI 2024.
• I'm co-organizing the Workshop on Learning Effective Abstractions for Planning at CoRL 2024.
• I was selected as a Rising Star in EECS 2024.
• I was selected as a Rising Star in Generative AI 2024.
• I'm co-organizing and will be giving a talk at the Workshop on Visual Concepts at ECCV 2024.
• I will be giving a talk at the Bimanual Manipulation: On Kitchen Challenges workshop at ICRA 2024.

• I will be giving a talk at the Brown Robotics Talks at Brown University. (Slides: Compositional Action Representations)
• I will be giving a talk at the NSF Workshop on Hardware-Software Co-design for Neuro-Symbolic Computation.
• I will be giving a talk at the Manipulation Reading Group at the Robotics Institute at Carnegie Mellon University.
• I will be giving a talk at the Coordinated Science Laboratory Student Conference (CSLSC 2024) at the University of Illinois at Urbana-Champaign. (Slides: Integrated Learning and Planning)
• I will be giving a talk at the Robot Representations For Scene Understanding, Reasoning and Planning workshop at RSS 2023. (Slides: Neuro-Symbolic Concepts for Robotic Manipulation / Video)
• I'm co-organizing the Visually Grounded Interaction and Language (VIGIL) workshop at NAACL 2021.
• I'm co-organizing the Neuro-Symbolic Visual Reasoning and Program Synthesis tutorial at CVPR 2020.

Research Highlights

My long-term research goal is to build machines that can continually learn concepts (e.g., properties, relations, skills, rules and algorithms) from their experiences and apply them for reasoning and planning in the physical world. The central theme of my research is to decompose the learning problem into learning a vocabulary of neuro-symbolic concepts. The symbolic part describes their structures and how different concepts can be composed; the neural part handles grounding in perception and physics. I leverage structures to make learning more data-efficient, more compositionally generalizable, and also inference and planning faster.