Mark G. Alford

Professor Alford's research seeks to understand the properties of matter at ultra-high density, such as might be reached in the core of a neutron star, where atoms and even nuclei are crushed into exotic phases of unimaginable density.

One of the main aims of Alford's work is to find signatures of the presence of color superconducting quark matter in neutron stars. His work therefore incorporates aspects of particle physics, nuclear physics, condensed matter physics, and astrophysics. In the past he has worked on numerical lattice QCD calculations, developing improved actions that greatly increase the efficiency of these computations, and also looking for ways to apply numerical methods at high density.

Professional History

2010-present: Professor, Washington University
2007-2010: Associate Professor, Washington University
2003-2007: Assistant Professor, Washington University
2000-2002: Lecturer, University of Glasgow
1998-2000: Research Scientist, Massachusetts Institute of Technology
1995-1998: Member, Institute for Advanced Study, Princeton
1992-1995: Research Associate, Laboratory of Nuclear Studies, Cornell University
1990-1992: Postdoctoral researcher, Institute for Theoretical Physics, University of California, Santa Barbara

recent courses

Physics and Society (Physics 171)

Introduction to the physics underlying the world we have built for ourselves. Energy as a unifying principle of physics, and society's use of energy. Atoms, heat, and power. Essentials of conventional and alternative forms of energy. Nuclear energy, including radiation, waste, and weapons. Global climate change. Some very basic quantum mechanics. Intended for science and non-science majors.

Computational Methods (Physics 584)

This course provides an introduction to the computational techniques that are most widely used in both theoretical and experimental research in physics. Each lecture will use a realistic research problem to introduce the algorithms, software packages and numerical techniques that will be used by the students to develop a solution on the computer. Topics include Monte Carlo techniques, symbolic analysis with Mathematica, data acquisition software used in the laboratory, the numerical solution of quantum mechanical problems, and an introduction to general purpose frameworks based on Python.