Year: 2024
Researcher:
Gary Bader, Terrence Donnely Centre for Cellular & Biomedical Research, University of Toronto
Student:
Faisal Melfy Alkulaib, King Abdullah University of Science & Technology
The growth and assembly of galaxies involves many complex processes, which culminate in the diverse collection of galaxies we observe today. One of the best ways of understanding these processes is through “Galactic Paleontology”, which tries to reconstruct the assembly history of nearby galaxies through their surviving “fossils” (which are their present-day surviving stars!). Using this data, we simulate the birth, evolution, and death of many thousands/millions of stars, compare the end result with the stars we observe today, and repeat this process many times for many different evolutionary pathways to see which ones match the observed data better.
For the past few years, astronomers have largely relied on simulation studies and more “ad hoc” approaches to try to compare simulated data with real data, often involving “binning” the data into larger groups. In this project, co-supervised with Prof. Ting Li, we will develop a new, more principled approach based on Inhomogeneous Poisson Point Processes (IPPP) that will allow us to utilize all of the available data. If time/interest permits, we will also try to compare these results with traditional approaches and potentially explore new probabilistic machine learning-driven methods. The main responsibilities of the student will be to review relevant literature, lead coding and data analysis efforts (using simulation studies and/or real data), and meet regularly with me and various collaborators to discuss progress on the project.
Year: 2024
Researcher:
Joshua Speagle, Department of Statistical Sciences, Faculty of Arts & Science, University of Toronto
Student:
Luke Weizhi, University of Toronto
Physical rehabilitation is central to the recovery process after many type of musculoskeletal and neurological injuries. Considering limited resources in the healthcare system, delivering effective rehabilitation at home is crucial to achieving optimal outcomes. For this reason, tools to track rehabilitation activities in different environments are needed to generate data that will assist with individualized treatment planning, performance monitoring, and the development of evidence to support the most effective approaches.
Systems that combine video data with deep learning are achieving impressive performance in tracking posture and recognizing activities. However, individuals with disabilities may perform movement exercises in varied ways. In order to support effective tracking of rehabilitation, systems are needed that recognize activities without requiring large numbers of examples of the same activity being performed in exactly the same way. As an important step towards this goal, the objective of this project will be to develop a video-based deep learning approach that can perform few-shot action recognition.
The student will be responsible for:
Year: 2024
Researcher:
Jose Zariffa, University Health Network
Student:
Muhammad Enrizky Brillian, University of Toronto
Road traffic collisions pose a significant public health challenge, and controlling vehicle speed is a crucial factor in enhancing road safety. Excessive speed not only endangers drivers but also presents substantial risks to vulnerable road users. Automatic Speed Enforcement (ASE), employing cameras and sensors to detect speeding vehicles, has emerged as an effective strategy. This research focuses on evaluating the impact of ASE in a medium-sized Canadian city, considering the socio-economic diversity across neighborhoods.
The city of Guelph, reflecting varying collision rates and safety infrastructure distributions, serves as a key location for this study. Leveraging diverse data sources, including speed records, offender postal codes, and marginalization indices, the research employs quasi-experimental analyses to: assess pre-existing traffic speed differences; explore the impact of ASE across neighborhoods with different levels of marginalization, and determine whether offenders reside in the camera-equipped neighborhoods.
Led by a multidisciplinary team with expertise in public health, epidemiology, and biostatistics, this research aims to contribute valuable insights into ASE deployment, particularly in terms of social equity. Partnering with city authorities ensures the translation of findings into practical policies, making the study a potential nationwide reference for equitable ASE implementation initiatives. The expected responsibilities of the student will be:
Year: 2024
Researcher:
Equitable Streets: Unveiling the Impact of Automatic Speed Enforcement on Road Safety Disparities in Guelph
Student:
Nevan Opp, Carleton University
We are proposing a novel gender-inclusive approach focusing on understanding barriers faced by women, Indigenous people, youth, and other underrepresented groups to increase recruitment, improve retention, expand and stabilize the construction and industrial workforces across Ontario. This proposal builds on our prior research on workplace factors associated with health professions’ workplace stressors, injuries and retention, and my former collaborative professional practice with injured miners, employers, and unions on workers’ return to work. Our research will develop and implement strategies to increase worker participation and retention in the construction and industrial workforce, based on gender-, age-, and ethnicity-informed systematic analysis of barriers to recruitment and retention.
The SUDS scholar will play a crucial role in several aspects of the project, contributing to both data analysis and project development:
This research provides a unique learning experience for the SUDS scholar, combining data science techniques with insights into social and workplace dynamics. The SUDS scholar will join the ReSTORE lab and be part of a multidisciplinary team. They will gain hands-on experience in advanced analytical methods while contributing to a project with real-world implications.
Year: 2024
Researcher:
Behdin Nowrouzi-Kia, Department of Occupational Science and Occupational Therapy, Temerty Faculty of Medicine, University of Toronto
Student:
Advika Gudi, University of Toronto
