Difference between revisions of "User:Dakota.Sullivan"
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* Created algorithm for n-1 linear regressions | * Created algorithm for n-1 linear regressions | ||
− | ==== Thursday, June | + | ==== Thursday, June 25 ==== |
* Met with Dr. Spiller | * Met with Dr. Spiller | ||
* Attended talk by Dr. Spiller | * Attended talk by Dr. Spiller | ||
* Modified Linear Regression | * Modified Linear Regression | ||
* Plotted new approximation | * Plotted new approximation |
Revision as of 15:49, 26 June 2015
Contents
Personal Info
Dakota Sullivan is a Computational Mathematics and Social Welfare and Justice double major at Marquette University.
Research Topic
Using statistical surrogates to estimate the behavior of storm surge during a hurricane or tropical storm. See here for more details.
Milestones
Weekly milestones can be found on our main project page, located here.
Daily Log
Week 1 (6/1 - 6/5)
Monday, June 1
- Attended introductory session; learned about REU program
- Met with Dr. Spiller and research group members
- Attended Systems lab orientation with Dr. Dennis Brylow
- Took pre-REU NSF survey
- Began introductory reading on Gaussian Stochastic Process
Tuesday, June 2
- Toured campus
- Filed paperwork with HR
- Attended library resources orientation with Heather James
- Met with Dr. Spiller
Wednesday, June 3
- Installed Python
- Met with Dr. Spiller to review Gaussian Stochastic Process
- Met with Dr. Brylow to learn about Marquette email and time keeping
- Tested optimization routines in Python
Thursday, June 4
- Determined routine for plotting in Python
- Attended talk by Dr. Factor
- Met with Dr. Spiller
- Determined research goals and project timeline
- Continued developing optimization routine and plotting algorithms
Friday, June 5
- Met with research group to finalize optimization algorithm
- Reviewed objectives of project
- Documented Goals and Milestones
- Downloaded LaTex
Week 2 (6/5 - 6/12)
Monday, June 8
- Met with Dr. Spiller
- Created methods to begin modeling maximum likelihood equation
- Developed a plot of our approximation in Python
Tuesday, June 9
- Created maximum likelihood function
- Worked through difficulties with singularity in our matrices
- Plotted approximation of theta optimization
Wednesday, June 10
- Corrected errors in the maximum likelihood function
- Determined optimal theta value
- Modified maximum likelihood equation to optimize multiple variables
Thursday, June 11
- Developed variance function
- Listened to sample presentation by Dr. Factor
- Met with Dr. Spiller, updated her on our progress and discussed paper by Westerink et al.
Friday, June 12
- Modified code to account for all approximated variables
- Tested our approximation methods for other functions
- Began reading Westerink et al.
Week 3
Monday, June 15
- Read through research paper (Westerink et al.)
Tuesday, June 16
- Met with Dr. Spiller
- Introduced to additional notes on GaSP
- Attended talk by Dr. Brylow
Wednesday, June 17
- Compared previous and new maximum likelihood equations
- Began developing a least squares model for our approximation
- Met with Dr. Spiller to discuss new maximum likelihood equation
Thursday, June 18
- Completed Responsible Conduct of Research (RCR) training
- Began implementing new maximum likelihood equation
- Integrated least squares model into maximum into approximation
Friday, June 19
- Finalized implementation of least squares model
- Tested approximation with multiple functions and inputs
- Added reference prior to maximum likelihood equation
- Met with Dr. Spiller
- Watched RCR training videos
Week 4
Monday, June 22
- Modified approximation function to allow multiple dimensions
- Developed algorithm to import test data
- Updated personal wiki page
Tuesday, June 23
- Tested import algorithm
- Met with Dr. Spiller
- Searched for errors in code
- Modified m(x) and v(x)
Wednesday, June 24
- Began optimizing theta for multiple dimensions
- Created algorithm for n-1 linear regressions
Thursday, June 25
- Met with Dr. Spiller
- Attended talk by Dr. Spiller
- Modified Linear Regression
- Plotted new approximation