Science Menu
Supervisors
The faculty members listed below are eligible to supervise graduate students in the Master of Science in Data Science. They may be contacted directly to discuss potential opportunities for studying under their supervision.
Note: Only students choosing the thesis option require a supervisor prior to their application to the program.
| Name | Department | Research Interest |
|---|---|---|
| Rick Brewster | Mathematics and Statistics | Discrete mathematics, theoretical Computing Science, computational complexity, graph theory, polynomial time algorithms and good characterizations |
| John Church | Natural Resource Science | Sustainability of cattle industry, range management, meat production, beef production |
| Michael Flannigan | Predictive Services, Emergency Management and Fire Science | Fire weather, climate change, fire behaviour, fire growth modelling, fire management, machine learning |
| Avninder Gill | Management, Info and Supply Chain | Business analytics, fuzzy Logics, optimization and meta-heuristics (genetic algorithms and simulated annealing) in supply chain, operations and other business areas |
| Claudia Gonzalez | Psychology | Using physiological data including brain activity, eye tracking, body kinematics and other physiological measures, to investigate human behavior and the links with brain function across human populations |
| James Gu | Architectural and Engineering Technology | Reliability and probability analysis, numerical modeling of engineering systems, disaster resilient built environments, etc. |
| Muhammad Hanif | Engineering | Wireless communications, 5G and beyond communications, internet of things, machine learning, signal processing |
| David Hill | Geography | Data mining and modeling methods, complex environmental systems problems, fate and transport of contaminants, decision trees, artificial neural networks, etc. |
| Erfanul Hoque | Mathematics and Statistics | Longitudinal data analysis; Statistical learning/Machine learning; Spatial statistics; Generalized (General) linear Mixed models; Measurement errors; Missing data analysis; Biostatistics; Statistical computing; Time series analysis; and Dynamic data science with applications in transportation, electricity demand, supply chain, algorithmic trading, option pricing and medical research. |
| Piper Jackson | Computing Science | Data science, computational social science, simulation, transdisciplinary research, programming languages, formal methods, gerontology, software development |
| Salman Kimiagari | Management, Info and Supply Chain | International Business and Quantitative Marketing, International Marketing, International Entrepreneurship, Market Engineering and Operation Research |
| Mila Kwiatkowska | Computing Science | Medical decision support systems, database systems and data mining, biomedical informatics |
| Stan Miles | Bob Gaglardi School of Business and Economics | Computational Economics, Computational Finance, Genetic Programming |
| Emad Mohammed | Engineering | Artificial Intelligence, Information Representation, Deep Learning, Big Data Analytics, Medical Image Analysis |
| Yana Nec | Mathematics and Statistics | Applied analysis, modelling and optimization, numerical and graphical simulation, applications in natural sciences and engineering |
| Catherine Ortner | Psychology | Multi-level modelling to analyze mouse-tracking, eye-tracking, daily diaries, and mobile application data, to assess cognitive process underlying emotion regulation choices and predictors of emotion regulation |
| Musfiq Rahman | Computing Science | Research in wireless technologies such as Near Field Communication (NFC), Internet of Things (IoT), smartphone applications, Radio Frequency Identification (RFID), use machine learning and big data analysis to predict forest fire |
| Matthew Reudink | Biology | Ornithology, animal ecology, evolution of plumage in birds, animal behaviour |
| Mateen Shaikh | Mathematics and Statistics | Computational statistics, clustering, classification, statistical learning, machine learning, data mining, association rules, pattern recognition, visualization, longitudinal data, biostatistics, microarray analysis, next-generation sequencing analysis |
| Mohamed Tawhid | Mathematics and Statistics | Metaheuristic algorithms, inspired computing, computational intelligence, evolutionary computation, artificial intelligence and their applications to data analysis, Big Data, Operations Research, computational optimization, simulations, modelling, scientific computation |
| Jabed Tomal | Mathematics and Statistics | Statistical machine learning, big data, Bayesian statistical inference and statistical ecology |
| Jonathan Van Hamme | Biological Sciences | Environmental microbiology, biodegradation, bioremediation, pollutants, microbiology, biocatalyst |
| Omer Waqar | Engineering and Applied Science | Deep Learning for next generation communication systems, wireless powered networks, physical layer security and software-controlled metasurfaces |
| Yan Yan | Computing Science | Bioinformatics, Machine Learning, Complex Network and Big Data Analysis |
| Roger Yu | Mathematics and Statistics | Graph theory, discrete optimization, data analysis, network optimization, operations research |
| Yue Zhang | Mathematics and Statistics | Bioinformatics, Comparative genomics, Applied statistics, Big data analysis, Statistical inference, Mathematical and statistical methods for evolutionary biology and genetics |
