Make Introduction

November 11, 2015

Meeting Info

Technical: “Make Introduction”

Gregory R. Hart

Make is software that allows you to carry out a series of tasks in a way that respects the dependencies amongst files or tasks. While it was develovoped for bulding software (i.e. compiling C++ code, linking the files, and placing the executable) It can be used for more then that. The Make manual states:

You can use make with any programming language whose compiler can be run with a shell command. Indeed, make is not limited to programs. You can use it to describe any task where some files must be updated automatically from others whenever the others change.

I will cover the basics of Make and give an idea of how powerful and complex it can be.

Lightning Research Talks:

###”Towards uncertainty quantification of groundwater models with structural error” Tianfang Xu (CSE Fellow)

Physically-based numerical models of groundwater flow are powerful quantitative tools to manage scarce groundwater resources. Inherent uncertainties associated with model structure and parameter lead to both random and systematic errors even in the output of a calibrated model. In this talk, I will present a complementary data-driven modeling framework to quantify the predictive uncertainty of groundwater models. The framework constructs error models based on machine learning techniques to correct for model structural error. The postprocessor and fully Bayesian implementations of the framework are illustrated using synthetic and real-world case studies.

###”Introduction to prostate cancer diagnosis using quantitative phase imaging and machine learning” Tan Nguyen

I will give a brief introduction to the problem of prostate cancer diagnosis using the standard-of-care staining method in contrast with our new method using the high throughput none-invasived Quantitative Phase Imaging (QPI) and machine learning. A machine learning algorithm is implemented to learn textural behaviors of prostate samples imaged under QPI and produce labeled maps of different regions for testing biopsies (e.g. gland, stroma, lumen etc.). From these maps, morphological and textural features are calculated to produce diagnosis results of the testing samples.




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