Get to know me

​

Here's the address to my RPubs account: https://rpubs.com/Saina_Sheini

​

I'm a data scientist. My main research interest is urban resilience. I'm also interested in sustainability, green buildings, and energy efficiency. I admire nature, I enjoy reading and I love cinema when it involves Christopher Nolan, Woody Allen, or Asghar Farhadi. In my free time, I do yoga and watercolor painting. 

 

​

Northeastern University, Boston, MA                   January 2017 – Present

Graduate Research Assistant, Boston Area Research Initiative (BARI)

 

• Managed Boston Data Portal, maintained, created, analysed and published administrative and private datasets as part of team

• Designed and created Geographical Infrastructure, a data infrastructure for Greater Boston area, published on Boston Data Portal as lead data researcher of team

• Updated improved versions of administrative datasets as building permits and tax assessors data annually and semi-annually as lead data researcher of team

• Defined and examined social capital metrics using Twitter data with size of 1 TB by quantitative methods, advanced data mining, and regression analysis

• Built “BARI Parcel Explorer” Twitter bot designed to post one tweet each hour about Boston parcels with information from public data sets as a contributor to team

• Studied, deliberated and compared urban development in six cities in Massachusetts using Permits data by GIS tools and statistical computing methods

• Evaluated level of resilience in energy and transport sectors in Metro-Boston region under normal operating conditions and in light of a shock to these sectors

​

Global Resilience Institute (2017-present)

Critical Infrastructure Resilience Institute Energy and Transportation Governance Project

 

Teaching Assistant (2016 - present)

​

Information Design and Visual Analytics, the Fall semester of 2016

Information Big Data for Cities, the Fall semester of 2018, Spring semester of 2019 and fall semester of 2019

​

COMPUTER SKILLS

​

• Programming Languages: R Studio, Python

• Technical Software: QGIS, ArcGIS, Stella, Nvivo, Gephi, Stata

• Techniques: Supervised and unsupervised machine learning, sentiment analysis, spatial and non-spatial modeling, and clustering, time series, decision trees, nearest neighbors, Monte Carlo analysis, multilevel modeling and imputation
   

PAPERS
 

Identifying Environmental Sustainability Indicators of Urban Construction Applied in Tehran Municipality Projects