is Associate Professor of Computer Science and Engineering at Cornell University. Among his research interests is the building of machines able to simulate, design and make other machines, (actively) learning from experiments to close the Reality Gap. He received many awards including the NSF Career and Darpa Awards.
Hod Lipson hold his PhD from the Technion - Israel Institute of Technology and he spent several years as a research engineer in the mechanical, electronic and software industries. He has had research positions at Brandeis University and Massachusetts Institute of Technology, at the crossroad of Computer Science and Mechanical Engineering.
Mining experimental data for dynamical invariants - from cognitive robotics to computational biology
For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalence of computing power, the process of finding natural laws and their corresponding equations has resisted automation. A key challenge to finding analytic relations automatically is defining algorithmically what makes a correlation in observed data important and insightful. By seeking dynamical
invariants, we go from finding just predictive models to finding deeper conservation laws. We demonstrated this approach by automatically searching motion-tracking data captured from various physical systems, ranging from simple harmonic oscillators to chaotic double-pendula. Without any prior knowledge about physics, kinematics, or geometry, the algorithm discovered Hamiltonians, Lagrangians, and other laws of geometric and momentum
conservation. The discovery rate accelerated as laws found for simpler systems were used to bootstrap explanations for more complex systems, gradually uncovering the "alphabet" used to describe those systems. Applications to modeling physical and biological systems will be shown.