Michael Zeifman, Ph.D.

Principal Research Scientist
  • Modeling and simulations of systems and components (physics-based, Monte Carlo, data-driven)
  • Machine learning and data analytics (feature extraction, supervised and unsupervised machine learning, testing statistical hypotheses, signal processing)
  • Energy performance monitoring, modeling, and evaluation
  • Reliability (systems and components) and quality control (SPC, quality management)
  • Iris-based biometrics
  • Occupant injury criteria
  • Time-dependent failure of materials (creep, fatigue, accelerated testing) and lifetime assessment
  • Cluster formation and evolution from the gas phase and/or in laser-material interaction




Michael Zeifman is a Principal Research Scientist at the Fraunhofer USA Center for Manufacturing Innovation. Michael is an R&D specialist with more than 20 years of experience in modeling, simulation, machine learning, and data analytics as applied to a broad range of subjects.  He has been a PI/co-PI on numerous R&D projects funded by government and/or industry, including recent DOE awards to develop enabling technologies for remote characterization of home retrofit opportunities using communicating thermostat data and for prediction of residential electrical loads and their flexibility using disaggregated electric data.  He has served as an Executive/Technical Program Committee member on several IEEE conferences, as an NSF Panel Reviewer and as a reviewer for numerous archival journals. He authored about 80 scientific and technical papers.

Michael received his undergraduate degree in Engineering Physics from Peter the Great St. Petersburg Polytechnic University, Russia, and his M.Sc. and Ph.D. degrees in Industrial Engineering from Technicon, Israel. Michael is a Senior Member of both IEEE and AIAA.

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