Lai-Sang Young
New York University
|
Primary Section: 11, Mathematics Secondary Section: 32, Applied Mathematical Sciences Membership Type:
Member
(elected 2020)
|
Biosketch
Lai-Sang Young is a mathematician recognized for her work in the theory and applications of dynamical systems, a branch of modern mathematics concerned with time evolutions of processes natural and engineered. She is best known for her work in chaotic dynamical systems, and has made contributions to mathematical physics and computational neuroscience. Young was born in Hong Kong. She graduated from the University of Wisconsin, Madison, in 1973 and from the University of California at Berkeley with a Ph.D. in Mathematics in 1978. She held faculty positions at Northwestern University, Michigan State University, the University of Arizona and UCLA before joining the Courant Institute of Mathematical Sciences, New York University, in 1999. She is Professor of Mathematics and Neural Science, and the Henry and Lucy Moses Professor of Science at NYU. She is currently also Distinguished Visiting Professor at the Institute for Advanced Study, Princeton. Her research has been funded by the National Science Foundation since 1979. She has given plenary lectures at the International Congress of Mathematicians and International Congress on Mathematical Physics. She is a member of the American Academy of Arts and Sciences and the National Academy of Sciences.
Research Interests
Lai-Sang Young's work in dynamical systems is centered around chaotic phenomena; she combines geometric and probabilistic approaches to produce a rigorous theory of chaotic systems. She has contributed to clarifying the relation between basic concepts such as Lyapunov exponents, entropy and fractal dimension. A hallmark of unpredictability is loss of memory or fast decay of time correlations. Young proposed a unified approach to capturing the rate of correlation decay for large classes of dynamical systems, connecting it to the system's geometry. She is working towards a theory of strange attractors, offering a statistical description without seeking to control individual trajectories. A recurring theme of her work is that deterministic, chaotic systems produce statistics much like those arising from random processes. Young works with both abstract theory and concrete models, including particle systems and forced oscillators. Since the early 2000s, she has expanded her research to include infinite dimensional systems and dynamical networks, systems with stochastic components, nonequilibrium statistical mechanics, and mathematical biology. Fascinated by the brain as a large and complex dynamical system, she has developed a strong interest in neuroscience. She is currently leading a multi-year project to build a biologically realistic computational model of the visual cortex, seeking to unravel the cortical mechanisms behind human vision.
