Daniel is an expert in data mining and predictive modeling, where he helps our clients manage large datasets and deliver actionable insights and recommendations from them.

Daniel has more than 20 years experience in statistics and data analysis in fields ranging from consumer package goods to autos to health care informatics.  He is well versed in using sophisticated analytic techniques and modeling to aid political campaigns in their voter targeting efforts.

Daniel received his B.A. and Ph.D. from Stanford University, and served as a graduate fellow in the lab of Nobel Laureate, Eric Kandel, at Columbia University.

He completed post-doctoral work at the Keck Center for Integrative Neuroscience at UCSF, as well as the Rosenstiel Basic Medical Science Research Center at Brandeis University.  Daniel has taught psychology, statistics, and neuroscience at Stanford and Tufts universities. He is the recipient of numerous awards for his work and is the author of papers in the fields of physiology, neuroscience, and artificial intelligence.