Arguably the most exciting part of planning the annual meeting is choosing the invited speakers.
The SIAM Annual Meeting will be held in Boston, MA, from July 11 to 15. This post is part 4 in a series of blog posts that highlight what you should know about Annual Meeting from the eyes of the technical co-chairs, Mary Silber and David Gleich.
The invited talks at the SIAM Annual Meeting cover quite a broad range of themes and application areas; some bring mathematical advances to the fore, some bring applied mathematical research opportunities from their disciplinary fields, and some are defining new directions identified in industry, including the challenges that have emerged with 21st century, data-rich science.
We couldn’t be more excited about the invited talks!
In an order determined by “rng(1); randperm(17)” (i.e. a random order) let us share a reason to attend each and every talk! (These summaries are entirely our own writing and we take responsibility for any errors!)
The analysis of models of liquid crystals are assessed through the lens of capturing the variety of their defects in a lecture by Lia Bronsard. Here we’ll find out about a singularity called “Saturn Ring” defect, which sounds very beautiful.
What is an eigenvector of a tensor (a 3d-matrix)? Come hear Bernd Sturmfels tell us about this exciting new theory that’s been developed over the last decade and how algebraic geometry plays a central role.
Returning to 2D-matrices, Mark Embree will discuss the physics and numerical models of vibrating strings and the full life-cycle of the eigenvalue and eigenvector problems that result. He also promises to discuss designing strings to produce a given sound.
With the explosion of data recently, privacy is a central concern. Cynthia Dwork will present a recently proposed, and quickly adopted, idea called differential privacy that attempts to protect privacy and prevent false discoveries based on those data.
Gary Froyland will combine an impressive range of mathematical approaches to address the role of coherent structures in geophysical flows. He will describe how coherent structures manifest in the ocean, including, we expect, examples like the Great Pacific Garbage Patch.
The educators at the meeting will want to hear the lecture by Karen Saxe, who co-authored the Common Vision report for Undergraduate Mathematical Sciences Programs in 2025.
Vittoria Colizza will present a fascinating epidemiological case study of MERS, the Middle East Respiratory Syndrome, which has spread to 26 countries since its identification in 2012.
Theoretical physicist Nigel Goldenfeld will, we promise, give a tour de force lecture that addresses problems from population ecology with a twist – his approach draws on his recent advances in understanding the transition to turbulence in fluid flows.
Bryan Grenfell has the distinction of being the joint speaker for the SIAM annual meeting and the SIAM Life Sciences meeting. His lecture fits perfectly with a meeting theme on epidemiology, as he addresses designing vaccination programs for tackling epidemics of childhood diseases.
Tanya Berger-Wolf is going to speak about the important challenges in analyzing data from the multitude of sensing methodologies in order to understand the behaviors of animals including zebras, baboons, and those pesky homo sapiens.
Stefan Wild will speak about minimizing functions, and in particular, black box optimization that can only see the result of evaluating the function. Well, almost. Wild will discuss making a black box into a grey box by assuming some structure on the function and how this enables global optimization of specific function classes.
What unifies numerical linear algebra with the theory of databases? Pablo Parrilo is going to tell us how treewidth and chordality arise in these two areas as a means to get fast algorithms to study systems of polynomials in what promises to be a wide ranging talk.
Heard of deep learning? Yann LeCun is one of the fathers of deep learning, the incredible methodology behind the resurgence of machine learning and artificial intelligence in recent years. And he’ll speak about the mysteries posed by this methodology that will form the basis for the next generation of research on these systems.
Marc Teboulle tackles the very large scale, nonsmooth, and nonconvex optimization problems that appear when doing signal processing and machine learning and will describe recent advances in the study of some of the simplest algorithms to solve these problems.
Matthew Salganik will discuss how the field of social science is evolving and changing in the era of big data and areas where social scientists need help from computational scientists in order to tackle an emerging class of critical problems.
Freddy Bouchet will describe applications of large deviation theory to problems in climate science, including rare events that represent climate extremes, which are so challenging and so important.
Last, but very much not least, reproducibility in scientific research has finally made a spotlight. We’ve invited a researcher in this field, Victoria Stodden, to tell us about best practices and how these are carried out in computational science.
The hashtag for this meeting is #SIAMAN16. See you on Twitter and at Annual Meeting!