UBC Data Analytics and Intelligent Systems Lab

Paving the way for the next industrial revolution through data.


We develop novel algorithms and computational tools to bring a new level of automation to the process industry.

Recent Papers. All Publications.

Meta-Reinforcement Learning for Adaptive Control of Second Order Systems by Daniel G. McClement, Nathan P. Lawrence, Michael G. Forbes, Philip D. Loewen, Johan U. Backström, R. Bhushan Gopaluni
Meta-Reinforcement Learning for Adaptive Control of Second Order Systems
, , , , , | In Proceedings of the 7th International Symposium on Advanced Control of Industrial Processes (AdCONIP). 2022 [PDF] [Video]
Causal Discovery based on Observational Data and Process Knowledge in Industrial Processes by Liang Cao, Jianping Su, Yixiu Wang, Yankai Cao, Lim C. Siang, Jin Li, Jack Nicholas Saddler, and Bhushan Gopaluni
Causal Discovery based on Observational Data and Process Knowledge in Industrial Processes
, , , , , , , | Industrial & Engineering Chemistry Research. 2022 [PDF]
Soft Sensor Change Point Detection and Root Cause Analysis by Liang Cao, R. Bhushan Gopaluni, Lim C. Siang, Yankai Cao, Jin Li
Soft Sensor Change Point Detection and Root Cause Analysis
, , , , | Society of Instrument and Control Engineers (SICE) Annual Conference, Kumamoto, Japan (To Appear). 2022 [PDF] [Slides]

Welcome to the UBC DAIS Lab

Bhushan Gopaluni leads process control, machine learning and data analytics research at the UBC DAIS Lab

Prof. Bhushan Gopaluni leads research activities at the UBC DAIS Lab. He is a Professor in the Department of Chemical and Biological Engineering and Associate Dean, Education and Professional Development in the Faculty of Applied Science at the University of British Columbia (UBC). Read More.


DAIS Lab Research

Our research lies at the intersection of industrial process control, data analytics and machine learning.

Recent representative publications in these areas:

Machine Learning

  • Journal Paper Top 10% Most Downloaded
    Towards Self-Driving Processes: A Deep Reinforcement Learning Approach to Control
    , , , ,
    AIChE Journal. 2019 [PDF]
  • Data Analytics

  • Journal Paper
    Data-Driven Dynamic Modeling and Online Monitoring for Multiphase and Multimode Batch Processes with Uneven Batch Durations
    , , , ,
    Industrial & Engineering Chemistry Research. 2019 [PDF]
  • Alarm Analytics

  • Journal Paper
    Univariate model-based deadband alarm design for nonlinear processes
    ,
    Industrial & Engineering Chemistry Research. 2019 [PDF]
  • Process Control

  • Journal Paper
    Machine Direction Adaptive Control on a Paper Machine
    , , , , ,
    Industrial & Engineering Chemistry Research. 2019 [PDF]

  • Research Themes

    We are at a unique historical moment with conditions ripe for a new industrial revolution that is going to take us to a level of automation that had never been seen before. This revolution is driven by a serendipitous confluence of ubiquitous cyber-physical systems or internet of things, gargantuan computing power, inexpensive memory and major algorithmic developments in machine learning and artificial intelligence.

    It is happening all around us in the form of self-driving cars to human-like robots.

    The process industries are in possession of treasure troves of heterogenous data that are gravely under utilized. These incredible volumes of data that industries already possess are poised to provide a level of insight and information never realized before, and thus alleviate economic and competitive pressures.

      DAIS Lab Research

    We often collaborate with industry partners and other academic researchers for problem-solving in specific domains. For a list of our projects and collaborators, please visit our Research page, check out our Publications and see our Team members:


    DAIS Lab Recruitment

    Please see our publications list for more information on our research on process control, machine learning and data analytics. Our team members and some examples of current and past projects are also available on our team page. We upload our presentations and workshops to the resources page.

    Our group is recruiting year-round for postdocs, MASc and PhD students, visiting students and undergraduate students. Please see our Opportunities page for more information.


    DAIS Lab Highlights

    16 June 2022

    Ibrahim receives DYCOPS Young Author Award for his paper on Visual Analytics. Congratulations!

    Link
    02 April 2022

    Yixiu's paper on battery capacity estimation has been accepted in Nature Communications. Congratulations!

    Link

    DAIS Lab News

    07 May 2022
    >> Successful thesis defense

    Daniel and Seoun successfully defended their MASc thesis and MEng project respectively

    02 April 2022
    >> New paper in Nature Communications

    New paper on 'data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation' accepted in Nature Communications.

    See more