TRU Science

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
Ajay Dhruv Computing Science Statistical machine learning, deep learning, hyperparameter tuning and optimization, generative AI, fine-tuning large language models, recommender systems, computer vision
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
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