Title: The Joy of Functional Programming in Haskell

Speaker: Jake Fennick

Time: 3pm Tuesday April 18, 2017

Location: Keller 401

Abstract:

The goal of this talk is to convey to you the experience of pure joy

and excitement when using Haskell, and to help you actually get

started programming in Haskell. After a basic introduction to the

language, we will cover

1. The development environment and getting set up

2. More advanced language features

3. Some mathematical patterns and functional programming idioms

4. Lots of fancy demos such as plotting/visualization, LaTeX, algorithmic music generation, high performance computing, etc.

5. A little bit of theory

This will be a coding talk, so we will primarily be walking through

code and actually getting set up. The code (including installation

scripts) is at https://github.com/TypeFunc/uh-mfc I hope to cover a

lot of material, so I encourage you to check it out but it isn’t

strictly necessary.

Speaker: Ruth Haas (UHM)

How to get an academic job in the mathematical sciences

Getting a tenure track job is great! How can you increase your chances of getting a job you like?

The time to act is now- when you are not actively looking, and when you have time to gain a variety of professional experiences that will help you attract the attention of employers.

Mathematicians get jobs at all sorts of different kinds of colleges and universities. In this talk I’ll describe some of the different kinds of institutions, both what it would be like to work there, and what they look for in job applicants. I’ll also discuss the parts of a job application and how you can (and should) tailor your application for different jobs. Industy and government jobs will be discussed briefly as well.

This talk is especially for graduate students and postdocs.

Title: Short-term Forecasting of Weather and Cancer: Finding

Initial Conditions and Parameters for Dynamical Models from Noisy Data

Speaker: Eric Kostelich, School of Mathematical & Statistical Sciences,

Arizona State University

Abstract: Computer models are essential to modern weather prediction.

They implement numerical methods to approximate the solutions of the

so-called primitive equations of atmospheric flow, but like any

differential equations, initial conditions must be supplied. However,

it is not possible to measure the state of the atmosphere at every model

grid point. Data assimilation refers to a class of methods to infer the

initial conditions from a sparse set of initial conditions and a set

of numerical forecasts. I will provide an overview of the problem

and describe a particular data assimilation method that is highly

accurate and efficient for numerical weather prediction and related

models. In addition, I will survey some potential applications

(and inherent difficulties) of data assimilation in mathematical

biology, especially differential equation models of prostate cancer

and glioma (brain tumors).

Bio: Eric Kostelich is President’s Professor of Mathematics at

Arizona State University. He received his Ph.D. degree in applied

mathematics from the University of Maryland at College Park and completed

postdoctoral work in physics at the University of Texas, Austin.

His research interests are in nonlinear dynamical systems, mathematical

biology, and high-performance computing, including data assimilation

for geophysical flows. Professor Kostelich was one of the principal

investigators in the Mathematics and Climate Research Network,

supported by the National Science Foundation. He has directed

undergraduate research program in computational mathematics at ASU

since 2008. He is a member of the Society for Industrial and Applied

Mathematics, the American Mathematical Society, and the American

Meteorological Society.

Title: The Joy of Functional Programming in Haskell

Speaker: Jake Fennick

Time: 3pm Tuesday April 18, 2017

Location: Keller 401

Abstract:

The goal of this talk is to convey to you the experience of pure joy

and excitement when using Haskell, and to help you actually get

started programming in Haskell. After a basic introduction to the

language, we will cover

1. The development environment and getting set up

2. More advanced language features

3. Some mathematical patterns and functional programming idioms

4. Lots of fancy demos such as plotting/visualization, LaTeX,

algorithmic music generation, high performance computing, etc.

5. A little bit of theory

This will be a coding talk, so we will primarily be walking through

code and actually getting set up. The code (including installation

scripts) is at https://github.com/TypeFunc/uh-mfc I hope to cover a

lot of material, so I encourage you to check it out but it isn’t

strictly necessary.

Inverting the Radon Transform using Summability Kernels

**Abstract.** We study an inversion technique of the Radon Transform using Summability Kernels and consider the problem of numerically implementing this algorithm. In doing so we investigate the tradeoff between the various analytical and discretization parameters involved and propose a simple framework using recent results in literature for integrating over $mathbb{S}^{n-1}$ to estimate the rate of convergence of our numerical implementation to the analytical inversion technique as well as offer a heuristic in parameter selection which would considerably reduce a brute force search over a large search space. We also discuss how the smoothness of the phantom to be estimated controls the convergence in the numerical inversion algorithm and have numerical experiments to validate our theoretical findings.

View your event at https://www.google.com/calendar/event?action=VIEW&eid=YXY0NmE1NDkxYTFvOXM4NzdrOTNhbGJzZjQgaGF3YWlpLmVkdV9hcGdwazdtbzE0ZDNpc3JxajA4Ym1rbmIyMEBn.

View your event at https://www.google.com/calendar/event?action=VIEW&eid=YXY0NmE1NDkxYTFvOXM4NzdrOTNhbGJzZjQgaGF3YWlpLmVkdV9hcGdwazdtbzE0ZDNpc3JxajA4Ym1rbmIyMEBn.

Jack Yoon will continue his explication of Proof Mining.