Talk Title:
Mining Experimental data

for Dynamical Invariants - from Cognitive Robotics to Computational Biology

Talk Abstract:
This talk will describe new active learning proce

sses for
automated modeling of dynamical systems across a number
of
disciplines. One of the long-standing challenges in robotics
is achiev

ing robust and adaptive performance under uncertainty.
The talk will de

scribe an approach to adaptive behavior based
on self-modeling where a
system continuously evolves multiple
simulators of itself in order to

make useful predictions. The robot
is rewarded for actions that cause d

isagreement among predictions
of different candidate simulators thereb

y elucidating uncertainties.
The concept of self modeling will then be

generalized to other systems
demonstrating how analytical invariants ca

n be derived automatically
for physical systems purely from observation

. Application to modeling
physical and biological systems will be shown

.

Speaker Bio:
Hod Lipson is an Associate Professor of Mechanical
& Aerospace
Engineering and Computing & Information Science at Cornell

University in Ithaca NY. He directs the Computational Synthesis
g

roup which focuses on novel ways for automatic design fabrication
and
adaptation of virtual and physical machines. He has led work in
areas

such as evolutionary robotics multi-material functional rapid
prototyp

ing machine self-replication and programmable self-assembly.
Lipson re

ceived his Ph.D. from the Technion - Israel Institute of Tech-
nology in
1998 and continued to a postdoc at Brandeis University and
MIT. His r

esearch focuses primarily on biologically-inspired approaches
as they

bring new ideas to engineering and new engineering insights
into biology