PCoM- Perception for Human-Robot Teams
The 4th industrial revolution will be driven by AI- with human-robot collaboration at forefront. In order to ensure safe and effective collaboration with humans, the Perception for CoManipulation (PCoM) project was established to develop robots with the ability to sense and work with humans on industrial tasks. project website
Influenza Prediction with Deep Learning
The type of host that a virus can infect, referred to as host specificity or tropism, influences infectivity and thus is important for disease diagnosis, epidemic response, and prevention. Advances in DNA sequencing technology have enabled rapid metagenomic analyses of viruses, but the prediction of virus phenotype from genome sequences is an active area of research.
We designed deep CNN models to identify host tropism for human and avian influenza A viruses based on protein sequences and performed a detailed analysis of the results. Our findings show that deep CNN techniques work as well as existing approaches (with 99% mean accuracy on the binary prediction task) while performing end-to-end learning of the prediction model (without the need to specify handcrafted features). Results published in Health Security.
Dynamic Deep Learning
Modern deep learning is handicapped by the immense amount of time needed to find the optimal network structure to solve a given problem. This project, featured at CVPR 2018, not only automatically selects the optimal architecture, but can also be used to shrink the network to a fraction of its original size with little loss in performance. This work has also yielded a patent for the network optimization process (currently pending).
Deep Learning for Black Box Detection
Sonar imagery utilizes sonic reverberations to detect objects. In low-visibility undersea environments, this is very helpful for finding the black boxes of crashed airplanes. This project, presented at Acoustic Society America 2017, showed the use of deep learning for this task. This work also yielded a patent (pending) and won MIT Lincoln Laboratory’s Tech Office Challenge 2016.