We apply complexity theory and pattern recognition to large data sets, which are usually derived from molecular profiles, physiologic metrics, clinical measures, and meta-data in human subjects. Data can be further generated from in vitro modeling and human tissue analogue studies.
The primary focus of our group is to use these methods to develop general countermeasures for humans working, living, and performing in these demanding conditions. In the case of translational medicine on earth, the focus is on pattern analysis directed toward solutions in a range of clinical unmet needs. An additional goal is to develop personalized countermeasures, taking into consideration that each individual astronaut, athlete, or human will have a unique genetic, molecular, and phenotypic profile. This carries the additional advantage of optimizing to the mission, performance environment, or clinical condition.