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.
TITLE
Dose-volume requirements modeling for radiotherapy optimization
ABSTRACT
Radiation therapy is an important modality in cancer treatment. To find a good treatment plan, optimization models
and methods are typically used, while dose-volume requirements play an important role in plan’s quality evaluation.
We compare four different optimization approaches to incorporate the so-called dose-volume constraints into the
fluence map optimization problem for intensity modulated radiotherapy. Namely, we investigate (1) conventional
emph{Mixed Integer Programming} (MIP) approach, (2) emph{Linear Programming} (LP) approach to partial volume
constraints, (3) emph{Constrained Convex Moment} (CCM) approach, and (4) emph{Unconstrained Convex Moment
Penalty} (UCMP) approach. The performance of the respective optimization models is assessed using anonymized data
corresponding to eight previously treated prostate cancer patients. Several benchmarks are compared, with the goal
to evaluate the relative effectiveness of each method to quickly generate a good initial plan, with emphasis on
conformity to DVH-type constraints, suitable for further, possibly manual, improvement.
BIO
Dr. Zinchenko received his PhD from Cornell University on 2005 under supervision of Prof. James Renegar.
From 2005 to 2008 he held a PDF position at the Advanced Optimization Lab at McMaster University, working
with Prof. Tamas Terlaky and Prof. Antone Deza, and spent portion of his fellowship with radiation oncology group
at the Princess Margaret Hospital in Toronto. Currently, Yuriy is an Associate Professor of Mathematics & Stat at the
University of Calgary.
Dr. Zinchenko’s primary research interest lies in convex optimization, and particularly, the curvature of the central path for
interior-point methods, and applications. Yuriy’s work on optimal radiotherapy design was recognized by 2008 MITACS
Award for Best Novel Use of Mathematics in Technology Transfer, and in 2012-2015 he served as one of the PIs for
PIMS Collaborative Research Group grant on optimization.