Fonseca continues Howard Rowlee lecture April 17

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Fonseca continues Howard Rowlee lecture April 17

Irene Fonseca

The 2015 Howard Rowlee Lecture will be given by Irene Fonseca, a professor at Carnegie Mellon University, at 4 p.m. April 17 in Avery Hall, Room 115. All faculty, staff and students are welcome to attend.

Fonseca’s talk, “Variational Methods in Materials and Image Processing,” will address how several questions in applied analysis motivated by issues in computer vision, physics, materials sciences and other areas of engineering may be treated variationally leading to higher order problems and to models involving lower dimension density measures. Their study often requires state-of-the-art techniques, new ideas and the introduction of innovative tools in partial differential equations, geometric measure theory and the calculus of variations. In this talk, it will be shown how some of these questions may be reduced to well-understood, first-order problems, while in others, higher order terms play a fundamental role. Applications to quantum dots in epitaxy deposition and re-colorization and de-noising in imaging science will be addressed.

As an internationally respected educator and researcher in applied mathematics, Irene Fonseca is the director of Carnegie Mellon’s Center for Nonlinear Analysis. In recognition for her contributions to the advancement of research in her area of expertise, Fonseca was bestowed a knighthood in the Military Order of St. James in 1997 for her teaching and research contributions to Carnegie Mellon University,

Fonseca was honored with the Mellon College of Science endowed chair in 2003 and named a university professor in 2014. In 2012, she was elected president of the Society for Industrial and Applied Mathematics. Her recent work focused on variational techniques as they apply to contemporary problems in materials sciences and computer vision, including the mathematical study of shape memory alloys, ferroelectric and magnetic materials, composites, liquid crystals, thin structures, phase transitions, epitaxy, image segmentation, stair casing and re-colorization in computer vision.

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