The public transportation system is crucial in alleviating urban congestion. The widespread of smart card automated fare collection (AFC) system produces massive data recording passengers’ day-to-day transport dynamic, which provides unprecedented opportunities to researchers and practitioners to understand and improve transit services. This project aims to make full use of the transit operational data (mainly smart card data) to enhance transit services. The main body of the research project is spatiotemporal behavior patterns mining.
The explosion of data from personal phones, apps, and sensors have enabled powerful machine learning algorithms to help computers identify, categorize, and evaluate information without the help of humans. However, teaching computers how to identify, categorize, and evaluate information usually requires feeding the computers a lot of data pre-labelled by humans. The pre-labelling process is costly and time consuming. The goal of this project is to develop new algorithms to teach computers to identify, categorize, and evaluate information with less pre-labelled data.
D-Wave Systems develops and manufactures quantum annealing processors. These processors implement a model of quantum computation that seeks to solve hard problems by exploiting quantum effects such as tunneling and superposition. The aim of this project is to study and improve the performance of quantum annealing processors by mitigating inherent and implementation-dependent failure mechanisms for near-term quantum annealing devices.
In a historic United Nations (UN) summit, world leaders adopted 17 Sustainable Development Goals (SDGs) as a universal call to action to address the global challenges we face by the year 2030, including those related to poverty, inequity, environmental degradation, prosperity, and peace and justice. Together, the UN and their partners have underscored the importance of evidence-based and transparent long-term pathways, in which sound metrics and data are critical for turning the SDGs into practical tools for problem solving, tracking progress and accountability.
In this project, our goal is to set up a framework of data collection to support user profiling which could be used to identify influential users in decision-making. The profile will be built based on the information of individual users obtained by collecting user activities in rewarding challenges that encourage employees, customers and partners to participate. In order to derive the profile, natural language processing tools are applied to extract useful information.
Models used for Wildfire catastrophe insurance as of today are not considering substantial information, such as geographic information and environmental constraints. The objective of the project is to establish a theoretical framework and an empirical process to enhance Aviva Canada’s current Wildfire Economic Capital (EC) model, to be able to determine the amount of capital needed to be allocated to ensure the company remains solvent, in case of occurrence of risks.
Estimates of the population density of marine mammals in an area and the change in population over space and time are critical inputs for managing the interactions of human activity and mammal populations. Visual surveys from boats, shore stations, and aircraft have served as the basis for most population estimates currently used by managers. However, these survey methods are generally only performed in good weather conditions and require many trained observers.
Hockey has long been shown to be among the least predictable of all professional sports. Recent developments in data collection methods have created the demand for more detailed and advanced predictive modelling techniques to extract value from and apply the data to real world problems. This project focuses on predicting important outcomes in hockey at both team and player levels. Game winners and scores will be predicted using Bayesian approaches tailored to accommodate evaluative statistics and relevant pre-game factors.
Devhaus Corporation operates 20 early child education centers across 5 countries including Canada, USA, Singapore, Cambodia and Philippines. To further implement its principle of Observation to Education, Devhaus is partnering with York University research team to develop an Artificial Intelligence Recommender System to help teachers across the world select optimal lesson plans for each kid based on the learner's behaviors.
There are numerous financial goals that most Canadians face. Retirement, funding post high school education, managing debt, purchasing appropriate amounts of insurance and saving for lump sum purchases. Each of these goals has various accounts and savings vehicles associated with them. The research projects we are proposing will help Canadians define their own financial situation, focus on their goals in the optimal order, and best utilize savings vehicles and government benefits to best meet their goals. Glencairn Financial Inc.