Modern drug discovery using in silico techniques is about sifting through large amounts of data to find signal in a sea of noise, and developing methodologies that do this efficiently. My educational background is as a molecular evolutionary geneticist, finding faint signatures of positive selection in a sea of genomic noise. This led me to become involved with large scale genomic, and later proteomic, projects utilizing a bioinformatic framework. At UMKC I have developed collaborations with structural biology faculty, in part to extend my knowledge of how to apply large-scale screening techniques to structure-based problems. This has been fruitful and allowed me to extend my knowledge in drug discovery towards chemical information signatures useful for high-throughput screening of compounds. All of these problems have as a common theme the development of tools to make sense out of large scale data where noise is high and signal is low. I also have the demonstrated ability to work with people in diverse fields, including structural biology and within medicinal chemistry.