Jamie Sherman, Ph.D.
Engineer, Modeling and Theory
Jamie Sherman joined the Institute for Cell Science on the Modeling Team in 2018 focusing on Machine Learning and Biophysical Modeling.
Prior to joining the Institute, Jamie was a Postdoc at the Australian Proteome Analysis Facility and UCSF where he developed a novel protein identification algorithm based on information theory. Unlike previous score-based methods detects deterministic combinations of ions generated by peptides and given an uncertainty in the observation, this method can directly compute the confidence in the observation of the protein/peptide of interest. Later while working as a Research Lecturer at the University of Sydney and subsequently as a Research Scientist at SCIEX he focused on rigorous mathematical solutions to challenges associated with biological mass spectrometry and other data.
Jamie received his BS and MS in Applied Mathematics, with a concentration on Mathematical Biology, from the California Institute of Technology and the University of Washington respectively. He contributed to work on Glioblastoma treatment and tumor resection, marriage modeling, and force generation in muscle tissue. Jamie continued to do a PhD in Molecular Biotechnology focusing on Proteomics with a bent towards acquiring high-throughput quantitative data for modeling.
Jamie’s focus has and continues to be in Mathematical Biology, where he attempts to fuse rigorous mathematical approaches with experimental data. Subtopics of particular interest are protein quantitation, cellular signaling, cellular transport, and both cyclic and divergent behaviors in cell differentiation/maturation space.
- Applied Math
- Biological Mass Spectrometry
- Information Theory
- Protein preparation and Separation
- Protein Chemistry
- Software Development