Dr Akira Saito, head of the BioMedical Imaging and Informatics Group in the Medical Solutions Division will give the keynote address at the Digital Pathology conference during the SPIE Medical Imaging symposium to be held in DisneyWorld resort in Florida, February 14 2013. Dr Saito will touch on the latest developments in digital pathology and the e-Pathologist system currently commercialized by NEC in Japan. He will also describe the current collaborations of the group with academic and medical institutions in Japan.
Created:01/04/122012 Benjamin Franklin Medal
Vladimir Vapnik is the recipient of The 2012 Benjamin Franklin Medal in Computer and Cognitive Science
with the following citation: "For his fundamental contributions to our understanding of machine learning, which allows computers to classify new data based on statistical models derived from earlier examples, and for his invention of widely used machine learning techniques."
Created:01/03/122012 Frank Rosenblatt Award
Vladimir Vapnik has been named recipient of the 2012 IEEE Frank Rosenblatt Award
with the following citation:
"For development of support vector machines and statistical learning theory as a foundation of biologically inspired learning." Congratulations Vladimir!
Sustainable production of renewable energy is hotly debated, and biofuels are an attempt at diminishing our dependence on fossil fuel. High lipid productivity is a key desirable characteristic of choosing species for biofuel production. We study and apply statistical network learning strategies to understand the cellular signaling / metabolic mechanisms that promote lipid production.
Unsupervised learning models capture the underlying structure of the data without relying on label information. We focus on sparse modeling algorithms and topic models to extract feature representations from text, image and video data. We also apply unsupervised learning models to learn hierarchical feature representation for object recognition and video action classification.
Most of the biomedical discoveries are communicated through
publications, or reports. We propose a range of text mining and natural language
processing strategies to convert human language in bio-literature text into
formal computer representations for sophisticated information access.
Transduction is a new learning principle which
combines induction and deduction in a single step, and is related
to the field of semi-supervised learning where one uses unlabeled data
during learning.
By eliminating the need to construct an accurate model,
transduction provides opportunities to achieve greater accuracy,
as has been demonstrated in text analysis and bio-informatics applications.
We study learning problems where the predictions are structured objects rather than vectors,
for example parse trees or strings.
We study a new framework introduced by (Vapnik 1998)
that is an alternative capacity concept to the large margin approach of SVMs.
In this setting, one is given a set of
labeled examples, and a collection of "non-examples" that do not belong
to either class of interest. This collection, called the Universum,
allows one to encode prior knowledge by representing meaningful
concepts in the same domain as the problem at hand.
SVM+ is a new approach to use hidden information within the learning framework.
Example data for download:
We explore algorithms for implementing large scale learning algorithms
as parallel computation.
We are currently developing parallelization approaches for increasing the
ability of SVM (Support Vector Machines) to solve large-scale
problems. As target systems, we consider shared memory processors,
clusters of processors, vector processors, and SIMD (Single
Instruction Multiple Data) processors. On a given system the speed of
an SVM is limited by the compute performance of the processor as well
as by the size of the memory. Efficient parallelizations have to
overcome both of these limitations while not getting bogged down in
communication overhead.
Online learning gives the promise of dealing with very
large datasets because data can be streamed off of a
disk or another source, and the entire dataset does not
have to be held in memory. We investigate fast online
algorithms that achieve good generalization ability
after only one pass of the data.
Transduction and semi-supervised learning methods can help
improve generalization ability in learning problems through
the use of the test labels, or unlabeled data, during learning.
However, many
algorithms are unfeasibly slow. We investigate how to make
large scale algorithms in this domain.
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.
SENNA is a fast neural-network architecture for semantic extraction from text.
Torch 5 provides a matlab-like environment
for state-of-the-art machine
learning algorithms. It is easy to use and provides a very efficient
implementation,
thanks to a easy and fast scripting language (Lua) and a underlying
C implementation.
SVM-FOLD is a web server that makes predictions of family, superfamily and fold level
classifications of proteins based on the 
RankProp is a ranking algorithm that exploits the entire network structure of
similarity relationships among proteins in a sequence database by performing a
diffusion operation on a pre-computed, weighted network. The resulting ranking
algorithm, evaluated using a human-curated database of protein structures,
is efficient and provides significantly better rankings than a local network
search algorithm such as PSI-BLAST.