From single-cell expression profiles to 3D MRI time courses, advances in wet-lab automation and throughput have given scientists a treasure trove of high-dimensional data. Ben develops automated computational and statistical methods that synthesize large datasets and meaningfully condense them into insights that push aging research forward.
Prior to Calico, Ben was a Principal Scientist at the synthetic biology company Amyris. There, he led several efforts to learn from data using machine learning techniques and helped build infrastructure designed to store and learn from diverse experimental streams. He also led the High Throughput Screening team at Amyris, spearheading efforts to develop new bioassays, quality control systems and statistical models to speed the company’s strain development pipeline.
During his postdoc at the Broad Institute of Harvard & MIT and Massachusetts General Hospital, Ben studied how pathogenic mycobacteria evade drug treatment by evolving genetically or by entering into dormant physiological states.
- Postdoc, Broad Institute of Harvard and MIT, and Massachusetts General Hospital
- Ph.D. in Applied Physics, Harvard University
- M.S. in Materials Science, California Institute of Technology
- B.S. in Engineering Physics, University of California at Berkeley
- Heiser Fellowship, Program for Research in Leprosy and Tuberculosis, NY Community Trust (2009-2011)
- Fund for Medical Discovery Award, Massachusetts General Hospital Executive Committee on Research (2009-2010)