The CCMA Distinguished Lecture Series

The CCMA Distinguished Lecture Series are aimed at broadening the educational and research experiences of Penn State students and research community by bringing distinguished researchers to campus to give a sequence of lectures on forefront research topics in theoretical, computational and applied mathematics and engineering. The CCMA Distinguished Lecture Series will be combined with the weekly CAM Colloquium and CCMA Luncheon Seminar.

 

Prof. Russel Caflisch will give the next CCMA distinguished lecture series with three lectures:

 

             Dr. Russel Caflisch

The Director of the Courant Institute of Mathematical Sciences at New York University (NYU), and a Professor in the Mathematics Department. He received his bachelors degree from Michigan State University in 1975. He earned a masters degree and Ph.D. in Mathematics from the Courant Institute of Mathematical Sciences at NYU. He has served as PhD advisor for 22 students, with 52 descendants. Up until August 2017, Caflisch was the Director of the Institute for Pure & Applied Mathematics (IPAM) and a professor in the Mathematics Department at UCLA,  where he also held a joint appointment in the Department of Materials Science and Engineering. He has also held a faculty position at Stanford.

Caflisch was a founding member of California NanoSystems Institute (CNSI). His expertise includes topics in the field of applied mathematics, including partial differential equations, fluid dynamics, plasma physics, materials science, Monte Carlo methods, and computational finance.

  • First Lecture: From Differential Equations to Data Science and Back (114 McAllister Building, Tuesday, September 4, 2:30 – 3:30pm) 

The arrival of massive amounts of data from imaging, sensors, computation and the internet brought with it significant challenges for data science. New methods for analysis and manipulation of big data have come from many scientific disciplines. The first focus of this presentation is the application of ideas from differential equations, such as variational principles and nonlinear diffusion, to image and data analysis. Examples include denoising, segmentation and inpainting for images. The second focus is the development of new ideas in information science, such as compressed sensing and machine learning. The subsequent application of these ideas to differential equations and numerical computation is the third focus of this talk. Examples include solutions with compact support and “compressed modes” for differential equations that come from variational principles, and applications of machine learning to differential equations and physics.

  • Second lecture: Accelerated Simulation for Plasma Kinetics (114 McAllister Building, Wednesday, September 5, 2:30 – 3:30pm)

This presentation will describe acceleration of simulation methods for the Landau-Fokker-Planck equation, with a focus on a binary collision model that is solved using a Direct Simulation Monte Carlo (DSMC) method. Acceleration of this method is achieved by coupling the particle method to a continuum fluid description. Efficiency of the method is greatly increased by inclusion of particles with negative weights. This significantly complicates the simulation, and many difficulties have plagued earlier efforts to use negatively weighted particles. This talk will describe significant progress that has been made in overcoming those difficulties.

  • Third lecture: Signal Fragmentation for Low Frequency Radio Transmission (114 McAllister Building, Thursday, October 6, 2:30pm – 3:30 pm)

Signal fragmentation is a method for transmitting a low frequency signal over a collection of small antennas through a modal expansion (similar to one level of a wavelet expansion), in which the mode has compact support in time. We analyze the spectral leakage and optimality of signal fragmentation. For a special choice of mode, the spectral leakage can be eliminated for sinusoidal signals and minimized for bandlimited or AM signals. We derive the optimal mode for either support size or for energy efficiency. The derivation of these results uses the Poisson summation formula and the Shannon Interpolation Formula.

Skip to toolbar