Machine Learning for Robot Locomotion: Grounded Learning and Adaptive Parameter Learning
Dr.Peter Stone Robot locomotion continues to remain a challenging problem in spite of the advances in the field. To gain more insights on the topic, a talk on “Machine Learning for Robot Locomotion: Grounded Learning and Adaptive Parameter Learning” was organized as a part of the fourth latent colloquium on 30th September 2021. The talk was delivered by Prof. Peter Stone, who is the David Bruton, Jr. Centennial Professor and Associate Chair of Computer Science, as well as Director of Texas Robotics, at the University of Texas at Austin.
3 min read
Predicting Essential Genes through Network Approach: Deciphering basis of Life
A classic challenge in biology is to study the function of proteins. Of various functions, essential functions are very interesting, as they map to important indispensable genes in an organism. Experimentally identifying these genes is rather expensive and challenging. Computational predictions can help point in the right direction, to prioritise experiments. To date, experimental data are available for <100 organisms! On the other hand, sequencing data are available for 1000s of organisms, as also interactome (networks of interactions) data.
4 min read