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Biography
Salman Habib is the Director of Argonne’s Computational Science (CPS)
Division and an Argonne Distinguished Fellow. He holds joint positions
in Argonne's High Energy Physics (HEP) Division and at The University
of Chicago and Northwestern University. He is a Fellow of the American
Physical Society. Salman moved to Argonne in 2011, after 20 years in
Los Alamos National Laboratory's Theoretical Division. He received his
PhD in physics from the University of Maryland in 1988, and was a CITA
National Fellow at the University of British Columbia before moving to
Los Alamos, where he first encountered parallel supercomputers -- the
Thinking Machines CM-200 and later the CM-5, which was then the
world's fastest system.



Ever since those early days, a fortunate nexus of scientific
possibilities, interesting ideas, computational power, and
opportunities to learn from gifted friends and collaborators have led Salman to
supercomputing-driven forays in diverse areas: dynamics of coherent
nonlinear structures, beam dynamics in accelerators, nonequilibrum
quantum field theory, stochastic ODEs and PDEs, the quantum-classical
transition, quantum control theory, and the formation of structure in
the Universe. He regards the role of supercomputers as engines of
scientific exploration and discovery to be one of key prominence --
then as now.



Supercomputers have continued to evolve rapidly, not only as powerful
tools to solve canonically "hard" problems, but also to attack complex
end-to-end modeling challenges. In this area, Salman has been deeply
involved in the development of large-scale cosmological simulations
(the MC2 and HACC codes) and related analysis tools. In particular,
solving scientific inference problems in cosmology using a finite set
of supercomputer simulations has seen significant development via a
long-term -- and very enjoyable -- collaboration with cosmologists and
statisticians. Salman's current interests also cover other issues in
data-intensive computing, including applying ideas from statistics and
machine learning to elucidating problems in physics.
Presentations
Awards Presentation
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