Majd Hawasly
Papers
7
Total Citations
117
H-Index
5
About
Majd Hawasly’s research bridges topology, robotics, and human-robot interaction, with a focus on enabling autonomous systems to reason safely and intelligently in human-centered environments. His most influential work introduces topological approaches to trajectory classification, using persistent homology and simplicial complexes to allow robots to reason about motion at a high level of abstraction—a method that has garnered 46 citations and opened new avenues for automated motion planning. Hawasly also pioneers natural language grounding for robotic manipulation, developing cognitively plausible systems that learn from video and linguistic descriptions to execute commands by demonstration. His contributions extend to safe motion planning, where he addresses collision probability computation for autonomous vehicles, and lifelong learning, where agents adapt to families of related tasks. Notable achievements include the creation of CLAD, a complex, long-activity dataset with rich crowdsourced annotations, designed to capture real-life, unscripted human behaviors. With a publication record spanning top venues and a growing citation impact, Hawasly’s work is shaping how robots perceive, plan, and interact—making him a key figure in the integration of topological reasoning and language grounding for next-generation autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Multiscale Topological Trajectory Classification with Persistent Homology35 citations · 2014
- 3
- 4Grounding of Human Environments and Activities for Autonomous Robots10 citations · 2017
- 5
- 6Lifelong Learning of Structure in the Space of Policies5 citations · 2013
- 7