Projects : applications

We investigate algorithms for detecting human face and headpose, eyes, and head pose. We focus on a neural network based architecture.

We investigate the difficult problem of 3D object detection, in the case of recognizing a large number of objects in differing lighting conditions.

Machine translation is the problem of converting from one human language to another, typically at the sentence level. We focus on an end-to-end machine learning approach, which is an instance of structured output learning.

Semantic extraction is the task of extracting semantic information from a document that is in a human-readable format. We are investigating the ability of machine learning algorithms to extract such semantic information in the form of semantic tags.

Mass spectrometry, a core technology in the field of proteomics, is commonly used in a high-throughput fashion to identify proteins in a mixture. Currently, the primary bottleneck in this type of experiment is computational. Existing algorithms for interpreting mass spectra are slow and fail to identify a large proportion of given spectra.

Machine learning algorithms can be used to solve the problem of classifying proteins into superfamilies and folds from sequence data, or returning a ranked list of sequences that are likely to be evolutionarily related to a query sequence. This is of interest because two sequences that are descended from a common ancestral sequence are likely to fill similar functional roles in the cell.