About the lab

Computational methods for capable, interpretable intelligence.

The Laboratory for Computational Vision and Intelligence, or CVI Lab, is a research group at the University of British Columbia’s Okanagan campus led by Dr. Shan Du. Established in 2020, the lab investigates computational methods that enable intelligent systems to perceive, model, generate, and reason about complex visual and multimodal information.

Laboratory overview

Connecting foundational research with real-world questions

Our work brings together computer vision, computer graphics, image and video processing, machine learning, deep learning, pattern recognition, and signal analysis. Current research includes generative models for three-dimensional faces, human motion, and scenes; multimodal methods for remote sensing and environmental monitoring; intelligent analysis of visual and acoustic signals; and trustworthy and explainable artificial intelligence.

By combining foundational research with application-driven development, the lab seeks to create intelligent systems that are technically capable, interpretable, reliable, and useful in real-world environments.

Mission

Rigorous research with meaningful impact

Our mission is to advance computational vision and artificial intelligence through innovative, rigorous, and application-oriented research. We develop intelligent systems that can generate and understand complex visual and multimodal data while remaining reliable, interpretable, and responsive to real-world needs. Through interdisciplinary collaboration and student mentorship, we aim to translate foundational research into technologies with meaningful scientific and societal impact.

Vision

Intelligence worthy of human trust

We envision a future in which artificial-intelligence systems can perceive, model, and interact with complex environments in ways that are both highly capable and worthy of human trust. CVI Lab aims to contribute to this future by unifying generative modelling, multimodal perception, and responsible AI, enabling intelligent technologies that are adaptable, understandable, and beneficial across scientific, industrial, environmental, and societal applications.

Research themes

Three Areas, One Connected Agenda

Our work moves between generative modelling, multimodal sensing, and responsible deployment.

A tactile 3D scanning scene with a half-mesh head bust, articulated human figure, and depth camera.

Generative 3D Vision and Graphics

Our research in generative 3D vision and graphics investigates computational methods for modelling complex visual structures and dynamic environments. Current topics include neural representations, 3D head avatars, controllable human motion generation, scene generation, reconstruction, rendering, and related applications in computer graphics and computer vision.

3D face and head-avatar generationHuman motion generation3D scene generationNeural rendering3D Gaussian representationsControllable generative modelsVisual reconstruction

Camera, microphone, satellite imagery, and environmental sensor streams combining inside one intelligent sensing hub.

Multimodal Perception and Intelligent Sensing

Our research in multimodal perception and intelligent sensing develops deep-learning methods for extracting meaningful information from heterogeneous data sources. We study the joint understanding of visual, acoustic, spatial, and sensor observations, with applications including remote-sensing image analysis, environmental monitoring, anomaly detection, gas-leak detection, audio processing, and intelligent surveillance.

Remote-sensing image analysisImage and video understandingAudio and acoustic signal analysisGas-leak detectionEnvironmental monitoringMultisensor data fusionAnomaly and event detectionIntelligent surveillance

A transparent AI model protected by a shield and examined with a magnifying glass beside a balance scale.

Trustworthy and Explainable AI

Our research in trustworthy and explainable AI examines how learning-based systems make decisions, how their behaviour can be interpreted, and how their reliability can be improved. We are interested in explainability, robustness, uncertainty, safety, fairness, and responsible deployment across vision, sensing, and multimodal applications.

Explainable AIInterpretable machine learningAI safetyModel robustnessReliability and uncertaintyResponsible AIBias and fairnessEvaluation of high-stakes AI systems

How we work

Research Approach

Our research combines theoretical development, data-driven modelling, system implementation, and empirical evaluation. We study both foundational machine-learning problems and application-specific challenges, using insights from computer vision, computer graphics, image and signal processing, pattern recognition, and multimodal learning.

We place particular emphasis on methods that offer meaningful control, generalize to complex data, and can be evaluated beyond a single benchmark. Where appropriate, we collaborate across disciplines to connect computational innovation with practical sensing, analysis, and decision-making needs.

Application areas

Our research supports a broad range of applications involving visual, spatial, acoustic, and multimodal data. Potential application areas include digital humans and virtual environments, animation and content creation, remote sensing, environmental monitoring, industrial inspection, gas-leak detection, intelligent surveillance, audio-event analysis, anomaly detection, human-centred computing, and decision-support systems.

Principal investigator

Dr. Shan Du

Assistant Professor, Computer Science
The University of British Columbia, Okanagan Campus

Dr. Shan Du received her PhD in Electrical and Computer Engineering from the University of British Columbia. She is an Assistant Professor of Computer Science at UBC’s Okanagan campus and leads the Laboratory for Computational Vision and Intelligence.

Before joining UBC, she was an Assistant Professor in the Department of Computer Science at Lakehead University and worked as a Research Scientist and Software Engineer at IntelliView Technologies Inc. She has more than 15 years of research and development experience spanning image and video processing, computer vision and graphics, pattern recognition, machine learning, biometrics, and intelligent surveillance systems.

Her research focuses on developing innovative technologies for challenging problems in computer vision, computer graphics, machine and deep learning, image and video processing, and multimodal intelligent systems. Her work combines foundational algorithm development with real-world applications in visual analysis, sensing, environmental monitoring, and related fields.

Email Dr. Du Meet the lab

Work with us

Join the Lab

The CVI Lab welcomes inquiries from motivated students and researchers interested in computer vision, computer graphics, generative modelling, multimodal learning, intelligent sensing, and trustworthy AI. Opportunities may be available for PhD students, MSc students, undergraduate researchers, visiting students, postdoctoral researchers, and research collaborators.

Contact Dr. Shan Du

PhD and MSc students

Prospective graduate students should have a strong interest in one or more of the lab’s research areas and a suitable background in computer science, electrical or computer engineering, mathematics, data science, or a related discipline. Experience with machine learning, deep learning, computer vision, computer graphics, image or signal processing, or scientific programming is valuable. Interested applicants may contact Dr. Shan Du by email. Please include a concise description of your research interests, explain how they relate to the lab’s current work, and attach relevant application materials.

Undergraduate researchers

Undergraduate students interested in research experience, directed studies, capstone collaboration, or volunteer and funded research opportunities are welcome to inquire. Applicants should briefly describe their academic background, relevant coursework, technical skills, availability, and the research topics that interest them.

Visiting students and researchers

The lab welcomes inquiries from visiting students and researchers whose work aligns with its research themes. Requests should include the proposed visit period, research goals, current affiliation, potential funding arrangements, and the expected form of collaboration.

Postdoctoral researchers

Researchers interested in postdoctoral collaboration are encouraged to contact the lab with a curriculum vitae, publication list, research statement, proposed project direction, expected timeline, and information about available or prospective funding.

Academic and industry collaboration

The CVI Lab is open to collaborations with academic groups, public-sector organizations, and industry partners on research involving visual computing, generative modelling, multimodal sensing, environmental intelligence, and responsible AI. Potential collaborators are invited to provide a brief description of the problem, available data or resources, expected outcomes, and anticipated collaboration model.

Application materials

Curriculum vitae
Academic transcripts
Brief research-interest statement
Relevant publications or research reports, if available
Portfolio, project page, or GitHub profile, if applicable
Expected start date
Funding or scholarship information, if applicable

Suggested email subject
Prospective [PhD/MSc] Student — [Applicant Name] — [Research Area]

Availability

Opportunities depend on research alignment, supervision capacity, project requirements, and funding availability. An inquiry does not guarantee an available position or admission. Formal graduate admission decisions are made through the University of British Columbia’s established application process.