Daniel Layeghi
Director at Z2 Labs
London, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolDaniel Layeghi is a director and engineering leader in London with 8 years' experience building production-grade robotics and recommendation systems that surface complex user intent. He holds a PhD in Robotics and Autonomous Systems from Edinburgh, where his research fused optimal control with learning-based behaviour synthesis for autonomy. Before academia he was an early core engineer at Automata, delivering low-level torque control, time-optimal planning, and real-time diagnostics for commercial robots that helped scale the company. Now at Z2 Labs he designs bespoke retrieval and recommender architectures, translating research-grade models into robust, deployed services. He combines deep systems-level control expertise with practical ML engineering, and is comfortable taking ideas from inverse kinematics and stochastic model-driven safety into applied recommendation problems.
8 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD Robotics and Autonomous Systems, Doctor of Philosophy - PhD Robotics and Autonomous Systems at The University of EdinburghMaster of Engineering - MEng Mechanical Engineering, Master of Engineering - MEng Mechanical Engineering at Queen Mary University of London
English, Persian
Github Skills (24)
physics-engine10
deterministic10
robotics10
simulator10
bullet-physics10
dynamics10
physics10
robot-simulator10
robotics-simulation10
mujoco10
simulation9
mechanics9
toolbox8
robustness7
stable-baselines7
Programming languages (2)
Github contributions (5)
Daniellayeghi/OptimisationBasedControl
Jan 2020 - Jan 2023
Contributions:313 commits, 136 pushes, 13 branches in 3 years
Daniellayeghi/Mujoco_Python_Sandbox
Jun 2020 - May 2023
Implementations of different RL and Trajectory Optimisation algorithms
Contributions:1 PR, 155 pushes, 4 branches in 2 years 11 months
implementationsoptimizationreinforcement-learningoptimisation-algorithmstrajectory
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