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.

Showing recent papers. See all publications.

Data-driven battery capacity estimation using support vector regression and model bagging under fast-charging conditions by Yixiu Wang, Qiyue Luo, Liang Cao, Arpan Seth, Jianfeng Liu, Bhushan Gopaluni, Yankai Cao
Data-driven battery capacity estimation using support vector regression and model bagging under fast-charging conditions
, , , , , , | Canadian Journal Of Chemical Engineering. 2024 [PDF]
Interpretable Data-Driven Capacity Estimation of Lithium-ion Batteries by Yixiu Wang, Anurakt Kumar, Jiayang Ren, Pufan You, Arpan Seth, R. Bhushan Gopaluni, Yankai Cao
Interpretable Data-Driven Capacity Estimation of Lithium-ion Batteries
, , , , , , | In Proceedings of the 12th IFAC International Symposium on Advanced Control of Chemical Processes (ADCHEM). 2024 [PDF]
Greenhouse Gas Emissions Reduction of a Hybrid-Powered Ferry using Deep Reinforcement Learning for Power Load Distribution by Ahmed Abdalla, Bhushan Gopaluni, Patrick Kirchen
Greenhouse Gas Emissions Reduction of a Hybrid-Powered Ferry using Deep Reinforcement Learning for Power Load Distribution
, , | In Proceedings of the 12th IFAC International Symposium on Advanced Control of Chemical Processes (ADCHEM). 2024 [PDF]

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 Vice-Provost and Associate Vice-President, Faculty Planning in the Office of the Provost and Vice-President Academic, UBC Vancouver. From 2017 to 2022, he was the Associate Dean for Education and Professional Development in the UBC Faculty of Applied Science. 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
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    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
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    Industrial & Engineering Chemistry Research. 2019 [PDF]
  • Process Control

  • Journal Paper
    Machine Direction Adaptive Control on a Paper Machine
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    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.

    We recruit year-round for postdocs, MASc and PhD students, visiting students and undergraduate students. Please see our Opportunities page for more information.


    DAIS Lab Highlights

    21 January 2024

    DAIS Lab publishes its 100th journal article in Automatica!

    Link

    DAIS Lab News

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