Application for Legislative Commission for Martha Morrissey

Contact Information

Full Name

Martha Morrissey

Eligible for commission

ineligible

Party Affiliation

Democrat

Occupation

Machine Learning Engineer at Development Seed

Educational Background

BA Geography University of California, Berkeley MA Geography University of Colorado, Boulder

Zip Code

80303

Congressional District
2

Background

Past Political Activity

Political organizations: Fair Fight, Act Blue Political Campaigns: Hilary Clinton, Elizabeth Warren, Biden, Mark Kelly, Cal Cunningham, Steven Buccini, Theresa Greenfield, Sara Gideon, Jaime Harrison

List of Political and Civic Organizations belonged to

N/A

Organization and Advocacy Experience

N/A

Statement

I would be honored to serve on the commission to represent, and advocate for all Coloradans during this redistricting process. I am excited to live in a state that has a citizen redistricting committee. I believe that citizen redistricting committees yield maps that are more inclusive and transparent than those determined by politicians.

Statement on Working with Consensus

I will strive to make sure that all members of the commissioners explore and understand our differences to work towards a solution, while recognizing that there are six of us for three different political affiliations, we are the voice for all citizens of Colorado. Additionally, I will try to promote consensus by providing a strong data driven geographical perspective. An important part of being fair and impartial that I can contribute is recognizing and reducing my own implicit bias.

Relevant Analytic Skills for Commission

Through my work, as a machine learning engineer, I frequently interact with geospatial data and satellite imagery. I use machine learning to solve urban challenges by helping detect urban infrastructure like specific building types and energy grids faster and more efficiently. I have hands-on knowledge of how inputs into models affect the model results, and how model results can affect real work consequences. I am committed to thinking through bias in the data, algorithms, and using geospatial data and Machine Learning to have a positive impact on people and the planet. My graduate school research involved modeling bike commuting at high spatial and temporal resolutions in urban areas using a variety of data sources including crowdsourced cycling data and OSM data.

Demographics

Racial categories the applicant identifies with:
Caucasian
Applicant identifies with the following gender

Female