Computational Physicist and Engineering Analyst
Karl Payne is a computational physicist and engineering analyst at NK Labs. His expertise is in applied physics, image processing, machine learning, and stochastic techniques for engineering optimization.
At NK Labs, Karl is applying his background in applied physics and civil engineering to enhance robot design, focusing on better understanding kinematics and robot vision. He is also leveraging machine learning and artificial intelligence tools to aid in the design of engineered products. Karl has over ten years of research and development experience, where he has developed novel algorithms to simulate complex multiphysics phenomena, applied non-intrusive electrical resistivity tomography and image processing techniques for geophysical imaging, and utilized genetic algorithms and neural networks for solving complex engineering design problems.
Karl's work in physics and engineering has been published in high-impact journals, including Physical Review E and the American Chemical Society's Environmental Science & Technology journal. He holds a Master's in Physics, Master's in Civil Engineering, and a PhD in Civil Engineering from the University of South Florida.