AG2PI Workshop #11 - April 8, 2022 | April 15, 2022 | April 22, 2022

Introduction to Scientific Computing

April 8, 2022 | April 15, 2022 | April 22, 2022 @ 11:00 AM - 1:00 PM (US Central Time)
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April 8, 2022 | April 15, 2022 | April 22, 2022
11:00 AM - 1:00 PM
(US Central Time)


Learn the basics of select computing environments and essential data analytics.


(Virtual Zoom Meeting)

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Workshop Registration

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Workshop Resources and Recording

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Google Colab and GitHub
Day 1: Google Colab Notebook Day 1: Homework Day 1: Homework Solution
GitHub Repository

Recent technological advancements in computer science, data analytics, and data management have resulted in the acquisition and storage of massive amounts of data across various disciplines. These collections do not reach their full potential unless they are analyzed thoroughly using information extraction, data mining, and knowledge discovery techniques. In this workshop series, we will introduce a comprehensive set of essential methods and approaches in data analytics and scientific computing.

This three-week series will include:

  1. An introduction to Python and Jupyter notebooks;
  2. An introduction to Shell scripts, data analysis and discovery, and machine learning using Python packages including NumPy, OpenCV, Pandas, and Plotly; and
  3. An introduction to version control using GitHub.

Computing environments used during the workshop series include Google Colab and the Windows Subsystem for Linux (WSL).

After completion of the workshop series, participants will be able to integrate various scientific computing methods within their workflows to explore image data, extract meaningful patterns from numerical datasets, and perform preliminary analyses. These workflows can be generalized to multiple domains and across biological scales, from individual organismal parts to the whole organisms themselves. In addition, participants will learn the essentials of computing: data preprocessing, statistical analysis, machine learning, data visualization, collaboration, code sharing, and computing environments.

About Presenters

Ariyan Zarei

Ariyan Zarei is a Ph.D. candidate in the department of computer science at the University of Arizona. He is part of the PhytoOracle project, designing machine learning, computer vision, and statistical models for preprocessing and analyzing high-resolution RGB image data and 3D point clouds. His research interests include the applications of computer vision and machine learning in plant sciences and remote sensing.

Travis Simmons

Travis Simmons is a senior undergraduate student majoring in Biological Sciences at the College of Coastal Georgia. He joined The University of Arizona's Pauli Lab as a virtual intern and is now a Research Data Support Specialist. Over the past two years at the Pauli Lab, Travis has worked with a transdisciplinary team to develop plant detection and tracking methods, 3D phenotype extraction software, and an agricultural virtual reality (VR) environment. After graduation, Travis will pursue a career in phenomics and is excited to continue collaborating with transdisciplinary teams to help solve some of plant phenomics' most interesting questions.

Emmanuel Gonzalez

Emmanuel Gonzalez earned a Bachelor's of Science in Biology with a minor in Chemistry from Pacific Lutheran University and is now a doctoral student in Dr. Duke Pauli's lab at the University of Arizona. His research involves leveraging sensor technology, high-performance computing, and machine learning to extract and model phenotypic trait data at scale. His work results in time series phenomic datasets of high spatial and temporal resolutions, enabling the study of the genetic factors contributing to dynamic stress-adaptive traits.

Nathan Hendler

Nathan Hendler spent a decade and a half working in the private sector, mostly as a computer programmer. He received my bachelor's degree at the University of Arizona in geology. As a graduate student at the UA's Lunar and Planetary Laboratory, he studied planet formation via observations of protoplanetary disks. His graduate career led me to my passion for data science and applying statistics and machine learning to large data sets.