dlee226 @ uic . edu
mmoham55 @ uic . edu
amovah2 @ uic . edu
plapam2 @ uic . edu
kalnaj2 @ uic . edu
rtiwar3 @ uic . edu
yijingxiao2014 @ u.northwestern . edu
École Nationale des Sciences Géographiques (ENSG) - France
guillemette . fonteix @ laposte . net
mshah96 @ uic . edu
djain24 @ uic . edu
sali78 @ uic . edu
jmigue2 @ uic . edu
pcairo2 @ uic . edu
davidmklawitter @ gmail . com
Post-Doc: Amirhassan Kermanshah (2018), Farid Peiravi (2016)
Ph.D.: Sk Nasir Ahmad (2018), Amirhassan Kermanshah (2017), Farid Peiravi (2014)
Master: Scot Maciver (2018), Dena Delpazir (2018), Eva Chancé (2016 - exchange student), John Mulrow (2016), Sk Nasir Ahmad (2014), SrimaniTejaswini Bulusu (2013 - non-thesis)
Research Affiliate: Alireza Karduni (2016)
Undergraduate: Daniel Kessler (2018), Saulius Imsha (2017), Kathy Machaj (2017), Joshua Deanes (2017), Kathy Sitko (2017), Ian Hill (2017), Jovaun Shaw (2016), Paloma Morais de Souza (2016), Kimberly White (2015), Matthew Reeder (2015), Eduardo Schaefer Sombrio (2015), Flavio Irã Godinho Jr. (2015), Laura Nainggolan (2014), Anthony Nedumgottil (2014), Felipe Pereira (2014), Gary (Joe) Andrews (2014), Paul Jacobs (2014), Marian Agamy (2013)
First of all, thank you very much for your interest. CSUN is a constantly evolving place and we are always eager to welcome bright minds who aspire to change the world (in fact, make sure to read our mission statement since everything we do must contribute to this mission). Although we would very much like to accept more people, funding is limited, and many deserving applicants do not get offers. There are definitely skills that we seek in our candidates however, and if you do not have these skills, we can only encourage you to acquire them. Most of these skills are not typical in civil engineering, but to quote Einstein: "We cannot solve our problems with the same thinking we used when we created them." The civil engineer of the 21st century must therefore acquire new skills.
Here is a short list of skills we look for:
# Python programming: In this era of Big Data, there are definitely many ways to change our thinking, and coding can help us greatly. Python can be insalled directly from the python.org website, but we recommend you install Anaconda, which installs both python and a set of necessary libraries (that would have to be installed manually otherwise). To learn how to code in python, we recommend, learnpython.org, A Byte of Python and CodeAcademy, but many more tutorials exist. So many libraries exist that we can now do some very sophisticated calculations with very little coding background. Amazing cheat sheets are also available to use many of the libraries. Naturally, we welcome applicants with knowledge of other languages, including R.
# Geographic Information Systems (GIS): GIS enables the mapping of just about everything. When coupled with python, we can nearly calculate anything we set our mind to. Knowledge of QGIS (free) or ArcGIS (license needed) is encouraged.
# Network Science: Network Science is a relatively new science that offers a practical way to measure and analyze the properties of networks, and it is increasingly being used in civil engineering (after all, most civil engineering systems are networks). Resources to learn Network Science include the textbooks Network Science (free), Networks: An Introduction, and Networks, Crowds, and Markets: Reasoning About a Highly Connected World (free), or some popular books that include Nexus, Linked, and Six Degrees. For practical use, different resources exist that include Gephi (free software), NodeXL (Microsoft Excel Add-On), igraph (R and python library), graph-tool (python library), NetworkX (python library). We use python-igraph extensively at CSUN.
# Machine Learning (ML): We do not necessarily expect new members to have any prior knowledge of machine learning, but it would likely become very useful if you join us. To learn about ML, the books Data Mining: Concepts and Techniques by Han, Kamber, and Pei (2011) and Introduction to Statistical Learning by James et. al (2013) are incredibly easy to read and offer an amazing and fairly in-depth introduction to the world of ML. To actually use ML, we recommend the Python libraries: Pandas for database handling, NumPy for matrix manipulation, Scikit-learn for many ML techniques (python library), and Theano for deep learning. Cheat sheets are available here to use many useful libraries.
In addition, we can recommend reading resources including publications, textbooks, and popular books to get better acquainted with the topic of cities, infrastructure, and urban science. A brief list of relevant resources include:
# Publications: Urban infrastructure is not a tree: Integrating and decentralizing urban infrastructure systems (Derrible, 2016) and Complexity in future cities: the rise of networked infrastructure (Derrible, 2016) - publications from CSUN Director Sybil Derrible on urban infrastructure systems (please contact us if you would like a free copy); Re-engineering cities: a framework for adaptation to global change (Dawson 2007); Infrastructure ecology: an evolving paradigm for sustainable urban development (Pandit et al., 2015).
# Popular Books: Science and the City: The Mechanics Behind the Metropolis (Winkless 2016); The Evolution of Great World Cities: Urban Wealth and Economic Growth (Kennedy 2011); Triumph of the City (Glaeser 2012).
# Textbooks: The New Science of Cities (Batty 2013) - Prof. M. Batty is one of the fathers of Urban Science; The Structure and Dynamics of Cities: Urban Data Analysis and Theoretical Modeling (Barthelemy 2016).