Difference between revisions of "User:ANoecker"

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(Weekly Log)
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**Capturing food images
**Capturing food images
**Quantifying food volume
**Quantifying food volume
*Prepared brief summary paper and [https://docs.google.com/presentation/d/1-VlLcket_pBv7QaKb1Vt3xQTnX4SO8UdVm-_DYxVohY/edit?usp=sharing concise presentation] for mentors and overall project team
*Prepared brief summary paper and concise [https://docs.google.com/presentation/d/1-VlLcket_pBv7QaKb1Vt3xQTnX4SO8UdVm-_DYxVohY/edit?usp=sharing presentation] for mentors and overall project team
=== Week 2: 6/7-6/11===
=== Week 2: 6/7-6/11===

Revision as of 15:54, 14 June 2021

Executive Summary

As a double major in Math and Computer Science with a concentration in Statistics and Data Science, participating in Marquette University's Research Experience for Undergraduate Students (REU) will help fill the gap of data science researchers who can think creatively to solve problems in interdisciplinary fields such as healthcare and nutrition. A collaborative team including myself and mentorship from Dr. Bialkowski and Dr. Gretebeck will towards Capturing nutritional value at the point of consumption using accessible and inexpensive technologies.

Project Description

The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) seeks to optimize metabolic response in individuals or population subgroups through tailored dietary approaches to promote health and prevent and treat disease. Accurate assessment of nutritional intake among community-dwelling individuals at the point of intake is a major obstacle in precision nutrition evaluation. Without this fundamental information about nutritional lifestyle factors, a highly modifiable environmental condition, disparities across Race, Ethnicity, socioeconomic status, and age will persist. Our team of cross-disciplinary researchers is combining expertise in hyperspectral imaging, image processing, mobile technology, analytics, nutrition, psychology and human motivation to develop a robust and accurate platform of nutrition evaluation at the point of consumption. This important translational step forward will empower researchers across disciplines with the information needed to facilitate meaningful change and more equitable access to health.

Main Objectives

  • Understanding and capturing the information that we're working on and being able to tell people why this is important and why it matters
  • Develop robust skills in data management and data architecture
  • Utilize visual analytics to communicate a story about this effort to capture nutritional value at the point of consumption

Weekly Log

Week 1: 6/1-6/4

  • Attended orientation
  • Read grant proposal in order to get up to speed on the project
  • Met with mentors to discuss project
  • Learned about and set up wiki page
  • Developed list of desired skills that will be grown throughout the summer; these will be used to map out goals/milestones for the next 8-9 weeks in collaboration with Dr. Bialkowski and Dr. Gretebeck
  • Performed literature review of available technology for:
    • Capturing food images
    • Quantifying food volume
  • Prepared brief summary paper and concise presentation for mentors and overall project team

Week 2: 6/7-6/11

  • Completed Responsible Conduction of Research Training for Federal Researchers (synchronous with Dr. Brylow on Monday, asynchronous modules on Tuesday)
  • Performed Literature Review on technology currently available for quantifying food nutrient composition, specifically examining hyperspectral imaging
  • Met with mentors to discuss how hyperspectral imaging is used for crops and harvest
  • Discussed opportunities to use this technology at the point of consumption
  • Met with REU fellow from 2020 to hear about her experience and advice she had for the program

Week 3: 6/14-6/18

  • Meet with Daniel Pinto to discuss BAP data export and merging
  • Begin process of designing dataset
  • Data Architecture: Codebook, specification, example of how we'll use the data analytics for the grant proposal

Week 4

  • Finish up data architecture process
  • Start working with pilot data

Weeks 5-8

  • Create pilot visuals that can be included in grant proposal
  • These are crucial and are based on how we present preliminary data
  • Important to show what sort of analysis and visuals can be achieved for the grant
  • Continue to modify and touch up dataset design as needed

Week 9/10:

  • Write final research paper
  • Create final poster for presentation