Recruitment
We recruit year-round for postdocs, MASc and PhD students, visiting students and undergraduate students. Please see our Opportunities page for more information.
Research Overview
The DAIS Lab at the University of British Columbia (UBC) conducts research at the intersection of process control, data analytics and machine learning.
On the applied research side, we develop novel algorithms and computational tools to solve industrial process control problems. We also explore fundamental problems in control theory and machine learning for theoretical insights.
Research Topics:
We are interested in the development of smart plants and intelligent processes, which can be distinguished from traditional industrial plants by:
- Large-scale dynamic models
- Maintenance-free controllers
- Automatic detection and diagnosis of faults
- Predictive analytics
Past Projects:
Please see our publications list for more information on our past projects on:
- Nonlinear System Identification
- Fault Detection and Diagnosis in Nonlinear Stochastic Systems
- Metabolic Flux Analysis
- Pulp & Paper Control
- Diabetes Modeling and Control
- Experiment Design for Mammalian Cell Cultures
Application Domains
A big subset of our research projects have an applied flavour with useful and immediate applications in industry. We often collaborate with industry partners and other academic researchers for problem-solving in specific domains.
Our current and past projects include collaborations with:
Medical Biology
- Pharmaceuticals (Amgen)
- Healthcare
- Bioengineering (Piret Group)
Manufacturing
- Refining (Parkland)
- Pulp and Paper (Canfor, Spartan Controls)
- Process Control and Optimization (Honeywell, Loewen Group)
Natural Resources
- Mining (BBA, Teck, Baldwin Group)
- Biomass (Sokhansanj Group)
Energy
- Oil and Gas
- Power (BCHydro)
- Electrochemical Systems (Wetton Group, Cao Group)
- Renewables