Haoliang Wang

Haoliang Wang is a Postdoctoral Associate at the Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology (MIT), affiliated with the School of Science. His research focuses on advancing artificial intelligence through investigations into intuitive physics, generalization in physical reasoning, and human-AI collaboration. He explores how humans and machines can communicate and reason about physical dynamics, leveraging probabilistic models and structured representations.

His work emphasizes integrating computational models with cognitive science principles to bridge gaps between human understanding and AI systems. Notable areas include intrinsic motivation in learning systems, procedural abstraction communication, and part-based visual object modeling. He has contributed to interdisciplinary studies at the intersection of machine learning, natural language processing, and robotics.

While no formal awards are listed, his recent publications (2020–2024) highlight innovative approaches to generalizable physical prediction, human-agent advice integration, and natural language interfaces for physical concepts. His research often addresses challenges in generalization, trust in AI, and multimodal learning.

No advising or grant information is provided in the text.