HARVIST

Collaboration with Stephan Sain of CU Denver. We are developing and demonstrating a machine learning analysis toolkit that uses Support Vector Machines, clustering, and multivariate spatial models to identify the connections between weather and agriculture (e.g., crop yield). We will integrate data from orbiting satellites, weather stations on the ground, land cover types, soil properties, and historical crop yield archives. Funded by a two-year grant from NASA's Earth Science Technology Office.

HSA19

The MLS Group is developing a variety of bioinformatics tools to support Caltech and LLNL biologists' efforts to produce a complete functional annotation genes and regulatory sequences in the unusually gene-dense human chromosome HSA19. The tools include automated image analysis software that enables the high-throughput interpretation of tissue arrays, and systems for integrated analysis of diverse sequence annotation and transcript expression data.

MISR Automated Cloud Classification

The Multi-angle Imaging SpectroRadiometer (MISR) instrument captures images of the earth at moderate resolution (275 m or 1.1 km) from nine different angles, ranging from straight down to 70 degrees in either direction. By comparing images of the same area of the earth from different angles, scientists are able to identify thin clouds and determine approximate cloud heights with unprecedented accuracy, leading to greater understanding of the planet's global distribution of clouds, and how that affects the global climate. Automating the process of detecting clouds and distinguishing between different types of clouds and aerosols remains a challenge, and we are applying machine learning technology to this problem to complement the Physics-based algorithms currently being used by scientists.

Cellerator

Cellerator is a Mathematica® package designed to facilitate biological modeling via automated equation generation. Cellerator was designed with the intent of simulating at least the following essential biological processes:

1. signal transduction networks (STNs);
2. cells that are represented by interacting signal transduction networks; and
3. multi-cellular tissues that are represented by interacting networks of cells that may themselves contain internal STNs.

These processes combine to form an obvious hierarchy that can be further subdivided for notational simplicity (e.g., STNs as elements of STNs, and so forth). In the past it has been necessary to manually translate chemical networks from cartoon-diagrams to chemical equations and thence to ordinary differential equations. This process is tedious and highly error prone, and impractical for all but the simplest of systems because of the combinatoric increase in the number of equations with the number of chemical species. Click here for an example of a simple cascade (3-stage MAPK in solution). Cellerator provides a framework for generating, translating, and numerically solving a potentially unlimited number of biochemical interactions.

OASIS

Rover traverse distances are increasing at a faster rate than downlink capacity is increasing. As this trend continues, the quantity of data that can be returned to Earth per meter traversed is reduced. The capacity of the rover to collect data, however, remains high. This circumstance leads to an opportunity to increase mission science return by carefully selecting the data with the highest science interest for downlink. We have developed an onboard science analysis technology for increasing science return from missions. Our technology evaluates the geologic data gather by the rover. This analysis is used to prioritize the data for transmission, so that the data with the highest science value is transmitted to Earth. In addition, the onboard analysis results are used to identify science opportunities. A planning and scheduling component of the system enables the rover to take advantage of the identified science opportunity.