Ocean models and observations are combined to produce estimates of the past and future ocean. I will discuss how these models and data are synthesized, highlighting issues faced in the ocean optimization problem. A case study in the Tropical Pacific Ocean is used to demonstrate the skill of modern ocean numerical models, and how the realism of represented physics determines which oceanic processes can
be considered deterministic as opposed to stochastic. The work
highlights the promise of extending weather forecast skill with
coupled ocean-atmosphere models.

(Pizza, coffee & tea will be provided at 11:20am)

A 1987 paper by Arjeh Cohen and Bob Griess proposed a list of candidate finite simple subgroups that might embed in exceptional Lie groups. Over the next 15 years the status of all these candidate subgroups was resolved in papers of Cohen, Griess, Kleidman, Lisser, Ryba, Serre and Wales. Recent work of Craven and Litterick on maximal subgroups of finite groups of Lie type has raised the more delicate question of the classification of embeddings up to conjugacy. In this talk, I will explain the origins of the Cohen-Griess list and the various strategies used to verify it. In particular, I will discuss the mainly computational strategies that I applied to construct particular embeddings. I will then describe recent work with Darrin Frey and Moty Katzman to settle certain conjugacy problems.

I will be discussing how deep learning may be used to enhance image resolution in optical microscopy. It is thought that this approach might be particularly useful for cases where the microscope's point spread function is not space-invariant.  However, concerns exist about the occurrence potential artifacts.

By now there exist very powerful and general solution theories for singular SPDEs, based on regularity structures and related pathwise theories. But the evolution of the laws of these equations is still poorly understood because the usual probabilistic tools break down. In my talk, I will present a martingale theory for a class of singular SPDEs of Burgers type. We construct a domain of controlled (and non-smooth) test functions for the infinitesimal generator and based on that we study the Kolmogorov forward and backward equations and the martingale problem. In combination with works by Goncalves, Jara, Sethuraman and others this leads to weak universality results for generalized stochastic Burgers equations, extending the by now classical weak KPZ universality. Joint work with Massimiliano Gubinelli.

Cheryl and Tina are in the process of team teaching Math 112 online this term, and are revamping the structure of the online environment.  During this session, they will share what they are doing, and what they've learned so far.  They will show some actual examples of course material and student work.  The hope is that you may find some ideas in the strategies that you can implement with your own students, whether you are teaching in-person or online.

In some areas of mathematical physics such as infinite-dimensional Langevin dynamics or dynamics near criticality one encounters so called “singular stochastic PDEs,” a class of equations that are ill posed due to resonances which may arise from the interplay of irregular noise and nonlinearities. Recent years saw a number of mathematical developments allowing us to solve and study such equations for the first time. In my talk I will motivate the appearance of some singular SPDEs, present the main ideas of our new solution theories, and discuss some recent developments in the field.

(Refreshments will be served in the Math Commons Room at 3:30 PM)

There is an avalanche of new data on the brain’s activity, revealing the collective dynamics of vast numbers of neurons.  In principle, these collective dynamics can be of almost arbitrarily high dimension, with many independent degrees of freedom — and this may reflect powerful capacities for general computing or information.  In practice, neural datasets reveal a range of outcomes, including collective dynamics of much lower dimension — and this may reflect other desiderata for neural codes.  For what networks does each case occur?  Our contribution to the answer is a new graphical framework, based on "motif cumulants," that links tractable statistical properties of network connectivity with the dimension of the activity that they produce.  In tandem, we study how features of connectivity and dynamics that impact dimension arise as networks learn to perform basic tasks.  I’ll describe where we have succeeded, where we have failed, and the many avenues that remain.

This is a series of NSF Noyce Seminars for the AZ Noyce Mathematics Teaching (MaTh) Scholars Project in Secondary Mathematics Education (PI: Cynthia Anhalt; Co-PI: Marta Civil; Co-PI: Jennifer Eli; Co-PI: Rebecca McGraw).

Conference will run March 29-31, 2019.

Registration held in ENR 2 courtyard + Cate, 5:30p, March 29th.

For more details, see https://sunmarc19.math.arizona.edu

Abstract: Statistical network analysis typically deals with inference concerning various parameters of an observed network. In several applications, especially those from social sciences, behavioral information concerning groups of subjects are observed.  In such data sets, even though a network structure is present it is not typically observed. These are referred to as implicit networks. In this presentation, we describe a model-based framework to uncover the implicit network structure and address related inferential questions. We also describe extensions of the methodology to time series of grouped observations.

(Refreshments will be served.)

Sports analytics has gathered tremendous momentum as one of the most dynamic fields. Diving deep into the numbers of sports can be game changing or simply a fun exercise for fans. How do you get in the game with numbers? What questions can be explored? What actionable insights can be gleaned? From March Madness to national media broadcasts, analytics are becoming increasingly indispensable. Dr. Tim Chartier will discuss outlooks that help with successful analytics, and the variety of questions that can be tackled. He will also share how he leads students to dig into sports using math and computer science, and their great success across the NBA, NFL, NASCAR, ESPN and his own college teams. Learn how to get in the game — as a sports analyst!

Memorial Lecture gives the public a window into the nature of mathematics by illustrating the work of renowned mathematicians to general audiences. The lecture, made possible by contributions to the Bartlett Memorial Endowment, was established to memorialize Daniel Bartlett, a remarkable rising mathematician who passed away suddenly in 2006. At the time of his death, Daniel was beginning work in algebraic number theory, the area he envisioned would ground his dissertation work at UA. Daniel’s passion for, and dedication to mathematics, led his family to also establish the Daniel Bartlett Memorial Scholarship, supporting outstanding graduate research in mathematics every summer. The inaugural Bartlett Memorial Lecture was delivered in 2008 by Barry Mazur, Daniel’s undergraduate advisor at Harvard University. 

(Dessert and Hosted Bar Reception in ENR2 courtyard following the lecture)

In February and March of 2014, the ranking methods developed by Tim Chartier and Amy Langville received extensive media coverage from such national outlets as the New York Times, USA Today and CBS Evening News. Since that year, the corresponding website March MATHness has enabled users from around the world to create custom-designed methods in a search to predict the NCAA Division 1 basketball tournament. First, what are the methods of March MATHness? What research questions remain? How are such methods adapted to other sports? What ranking applications exist outside predicting game outcomes?

Hands-on workshop on recognizing and combatting Impostor Syndrome. Moderated by Laura Hunter and Mika Galilee-Belfer from the Office for Diversity and Inclusive Excellence. Accessible to all, including undergraduate and graduate students, instructors, staff, and faculty. Will be followed by a luncheon (must RSVP for the luncheon). 

(Refreshments will be served.)

This is a series of NSF Noyce Seminars for the AZ Noyce Mathematics Teaching (MaTh) Scholars Project in Secondary Mathematics Education (PI: Cynthia Anhalt; Co-PI: Marta Civil; Co-PI: Jennifer Eli; Co-PI: Rebecca McGraw).

Distributionally robust optimization (DRO) has gained increasing popularity because it offers a way to overcome the conservativeness of robust optimization without requiring the specificity of stochastic optimization. On the computational side, many practical DRO instances can be equivalently (or approximately) formulated as semidefinite programming (SDP) problems via conic duality of the moment problem. However, despite being theoretically solvable in polynomial time, SDP problems in practice are computationally challenging and quickly become intractable with increasing problem size. In this talk, we adopt the principal component analysis (PCA) approach to solve DRO problems with different types of ambiguity sets. We show that the PCA approximation yields a relaxation of the original problem and derive theoretical bounds on the gap between the original problem and its PCA approximation. Furthermore, an extensive numerical study shows the strength of the proposed approximation method in terms of solution quality and runtime.

(Refreshments will be served.)

(Refreshments will be served in the Math Commons Room at 3:30 PM)

For details, see http://cos.arizona.edu/content/graduation-and-commencement