Daniel Grady

Daniel Grady is a seasoned data scientist with extensive experience in algorithm development and complex data analysis. Currently serving as Principal Data Scientist at Rockley Photonics Inc., Daniel designs and implements algorithms for predicting various health biomarkers using advanced laser-based technology. Previously, at Elsevier, Daniel enhanced the accuracy of Scopus.com author profiles, and while working at ID Analytics, Daniel developed a significant unsupervised clustering system for identity resolution. Earlier roles include contributions to deep learning architectures at SPAWAR and network analysis methods during a postdoctoral position at Northwestern University. Daniel holds a Ph.D. in Engineering Science and Applied Mathematics from Northwestern University, alongside a Master’s degree and Bachelor’s degree in the same field from Northwestern University and William & Mary, respectively.