Join the Lab

We are always interested in talking/meeting with potential students and researchers interested in joining our team!

Before contacting us about advising, funding, or enrolment opportunities, please take some time to read through the information below.

As a starting point, please review the wealth of information on the following three websites.

These contain a range of detailed information about the enrolment process, life as a graduate student, funding opportunities, and application deadlines.

If you are looking to join the lab as a graduate researcher pursing a Masters or Doctoral degree, or an undergraduate honours student, please include the following information in your initial email.

Your Curriculum Vitae
A statement of research interest
Level of experience with programming and/or spatial statistics
An (unofficial) copy of your academic transcript

Finally, please clearly state how you think your research interests align with the lab’s. What can you bring to the lab and what goals can we help you attain (i.e., what are your career goals)?

Please send all relevant documents along with your inquiry to Professor Grant McKenzie at grant.mckenzie@mcgill.ca.

Links that may be of interest to future Montrealers

Suggested general reading for graduate students

Zhang, Hongyu; McKenzie, Grant

Geoprivacy knowledge, attitudes, and behaviours in contemporary China Journal Article

In: Geographical Review, 2024.

Abstract | Links | BibTeX

Qiang, Dan; McKenzie, Grant

Navigating the Post-Pandemic Urban Landscape: Disparities in Transportation Recovery & Regional Insights from New York City Journal Article

In: Computers, Environment and Urban Systems, vol. 110, pp. 102111, 2024.

Abstract | Links | BibTeX

Verma, Priyanka; McKenzie, Grant

Regional Comparison of Socio-Demographic Variation in Urban E-scooter Usage Journal Article

In: Environment and Planning B: Urban Analytics and City Science, 2024.

Abstract | Links | BibTeX

@article{Verma2024,

title = {Regional Comparison of Socio-Demographic Variation in Urban E-scooter Usage},

author = {Priyanka Verma and Grant McKenzie},

url = {https://grantmckenzie.com/academics/Verma_2024.pdf},

year  = {2024},

date = {2024-03-26},

urldate = {2024-03-09},

journal = {Environment and Planning B: Urban Analytics and City Science},

abstract = {In recent years we have witnessed explosive growth in the shared, free-floating, electric scooter industry. While still controversial in many North American cities, a number of large e-scooter operators have managed to carve out a piece of the urban transportation landscape. As these vehicles shift from novelty services to increasingly reliable modes of short personal travel, the discussion has turned to investigating who exactly benefits from these micromobility services and who are being left behind. Though population surveys have been administered to identify the socio-demographic characteristics of e-scooter riders in the past, little work has linked these characteristics through trips, or investigated the regional variation in these demographic factors. In this work we explore the variability and similarities in e-scooter rider characteristics across three major U.S. cities. To accomplish this, we apply a Moran’s Eigenvector Spatial Filtering linear regression model and compare our results to more commonly used spatial regression approaches. Our results indicate that the spatial filtering approach outperforms other methods in identifying socio-demographic characteristics of e-scooter users, across multiple regions. We find that many socio-demographics associated with e-scooter usage are regionally variant, despite younger users making up the core user base in all cities. There are variations in usage based on gender, income, and race across cities with Black and Hispanic populations remaining underserved. The implications of these findings are discussed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Brunila, Mikael; LaViolette, Jack; CH-Wang, Sky; Verma, Priyanka; Féré, Clara; McKenzie, Grant

Toward a Critical Toponymy Framework for Named Entity Recognition: A Case Study of Airbnb in New York City Best Paper Proceedings Article

In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP2023), 2023.

Abstract | Links | BibTeX