Causal Dynamics Lab is an AI research lab.
We build products that give machines the understanding to act on complex systems.
Mission
Our purpose
Current AI systems optimize for sounding correct. They hallucinate confidently, forget what they learned yesterday, and burn enormous resources processing context they do not need.
The mission is to build AI that reasons through cause and effect, learns from experience without forgetting, and grounds truth within the world model.
Our research lab spans causal inference, graph world models, and reinforcement learning. We turn that research into products, starting with production software, the domain with the richest causal signal and the fastest feedback loops.
Roadmap
The ambition that sets us apart
Three frontiers, pursued simultaneously
- 01
AI that shows reasoning
Shining light into the black box. Causal structure built into how the software world model evolves. Humans follow the chain, check the logic, and trust or reject the conclusion.
- 02
AI that learns without forgetting
Current systems reset, whereas ours accumulates knowledge. AI agents have to get better over time, not start over again.
- 03
AI that reasons on 20 watts, not 20 gigawatts
The human brain does not process everything all at once. Reasoning starts with small, relevant subsets, and the rest is ignored. We are closing the gap.
Team
The people that make this possible
Leadership
Hasibul Haque
CEO
Ryan Turner
CTO
Saad Siddiqui
CBO
Bowen Zhu
Head of Software Architecture
Max Mushad
Agentic Engineering Lead
Research
Dr. Xuchao Zhang
Head of Research
Dr. Liang Zhao
Founding Research Scientist
Dr. Tauhid Islam
Founding Research Scientist
Dr. Syed Ishtiaque Ahmed
Founding Research Scientist
Backed by world-leading experts and investors
Join us
Where the science and the product are the same thing
We hire selectively across research, engineering, and forward-deployment roles. If you are drawn to work where the science and the product are the same thing, we would like to hear from you.
See open roles