Bedros Afeyan

CEO of Polymath Research Inc.

Bedros Afeyan earned his B.S. degree in Electrical Engineering from Concordia University, Montreal, Quebec, Canada in 1980. He then earned a Masters and a Ph.D. in Theoretical Plasma Physics from the University of Rochester working at LLE on modeling laser–plasma interaction problems. His thesis advisors were Edward A. Williams, head of the Plasma Theory group at LLE, and Prof. Albert Simon.

Afeyan moved to Lawrence Livermore National Laboratory in 1984 to finish his Ph.D. after Williams went there in 1983. He subsequently worked at the University of Maryland as a post-doctoral researcher under the supervision of C. S. Liu. He moved back to Livermore in 1993, working in inertial confinement fusion and magnetic fusion experiment programs until 1996, followed by two years at the UC Davis-Livermore Applied Science Department when he also started his own company, Polymath Associates, which then became Polymath Research Inc. (PRI) in 1999. He has run that company for the last 20 years. PRI dedicates itself to research in laser–plasma interactions, photonics, harmonic multiresolution analysis, kinetic theory of plasmas and nonlinear self-organization in complex dynamical systems. He is the inventor of the Spike Trains of Uneven Duration and Delay (STUD) pulse program for LPI control, and the inventor or the Variational Approach to Parametric Instabilities in Inhomogeneous Plasmas, which was his Ph.D. research work. Afeyan also discovered kinetic electrostatic electron nonlinear (KEEN) waves, created the MDF (modified distribution function) paradigm to explain stimulated Raman scattering inflation and stimulated Brillouin scattering-filamentation entanglement, and coinvented image analysis tools such as MODEM (morphological diversity extraction method), a high road to denoising and feature extraction used in Z pinch, inertial confinement fusion, and target-fabrication modalities. He is also the inventor of recent algorithms that introduce machine learning into high-performance scientific computing, including nearby skeleton constrained accelerated recomputing (NSCAR), bidirectional adaptive refinement scheme (BARS), self-healing atomistic dynamics (SHAD), and adaptive particle orbit sampling technique for Lagrangian evolution (APOSTLE). These are methods that may largely address the ills of kinetic equation modeling methods that have hampered progress in the past.

Afeyan works with students and postdocs from Stanford and Berkeley and collaborates with all the national labs and government agencies such as the Departments of Energy and Defense. He is a poet and painter in his spare time, as well as a chess player and a musical improvisor. He is a member of APS, OSA, IEEE, and SIAM. He is also a founding member of HEDSA, the High Energy Density Science Association, which he has led as its president for three terms. HEDSA’s goal is to develop a healthy ecosystem for HEDP in the U.S. and to nurture next-generation researchers in this exciting arena of scientific discovery and invention where many fields meet and clash to produce new ideas and new technologies.