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Pierre Boulanger, Ph.D., P. Eng
Emeritus Professor
University of Alberta
Athabasca Hall, Room 411 Edmonton, Alberta T6G 2E8, CANADA
Email: pierreb@ualberta.ca |
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Who am I?
Date of Birth:
April 24, 1957
Hometown: Beautiful Quebec City
I am a man who loves life, music, fine food, and most importantly, ideas.
My new Best Friend
A Short CV
Dr.
Boulanger is an Emeritus Professor at the University of Alberta with dual
appointments in Computing Science and Radiology. During his 42-year career he
was a senior research officer at the National Research Council of Canada for 18
years (1983-2001) and spent 24 years (2001-2025) at the University of Alberta
as a full professor. He was the director of the Advanced Human Computer
Interface Laboratory and is still the scientific director of the SERVIER
Virtual Cardiac Centre. From 2013-2023, he held the CISCO Chair in Healthcare Solutions,
a $2M investment by CISCO Systems which focused on the application of network
technologies and AI for healthcare. A final report on the activities of the
CISCO Chair can be found in Report-2022.
During
his research career, he focused on 3D sensing, geometric modeling, reverse
engineering, patient-specific modeling, medical imaging, and in the past four
years quantum image processing. To date, his work has resulted in 410 published
papers and 8 patents. He is still serving on various editorial boards and
international committees. He is currently the CTO of Naiad Lab Inc.,
a company which focuses on AI technologies for
healthcare applications.
In
January 2025, he retired from the university but continues research in image
processing using quantum manifolds. He is also working on the development of a new
generation of geometry-based neural networks.
University Education
Ph.D. in
Electrical Engineering (1994), University of Montreal (Ecole Polytechnique),
Department of Electrical Engineering, Montreal, Canada.
Advising
Professor: Dr. P. Cohen
Dissertation: Multi-Scale Extraction of Geometric Elements
MSc in Physics
(1982), Laval University, Department of Physics Quebec City, Canada.
Advising
Professor: Dr. M. Baril
Dissertation: Multi-Passage Mass Spectrometer
BSc in
Engineering Physics (1980), Laval University, Department of Engineering
Physics, Quebec City, Canada.
Dissertation: Design and Construction of a Multi-Channel
Analyzer for an Electron Spectrometer
Research
Interests
3D Computer Vision
Neural Networks
Virtualized Reality Systems
Medical Imaging
Physical Modeling
Sensor-Based Geometric Modeling
Tele-Medicine
Sensor Fusion
Quantum Image Processing
Geometric Signal
Processing
Parallel Computing
Current and Past Projects
See Advance Human-Computer Interfaces Laboratory Website
Publication List
The most recent publication list: Publications
Recent Committee Work
Scientific
Director of the SERVIER Virtual Heart Center
Chief Technology Officer of Naiad Lab Inc.
Member of the editorial board of the Journal SENSORS
Review Editor of Frontiers in Virtual Reality
Member of the CIHR Reviewers Board
Current Grants
Heart and Stroke Foundation
Alberta Innovates
Past Course Subjects (2001-2024)
Introduction to Computer Graphics
This course introduces computer graphics concentrating on two- and three-dimensional graphics and interactive techniques. Course topics include fundamental concepts of raster graphics, simple output primitives, windowing, clipping, 2D transformations, 3D transformations, modelling and viewing, hidden-line and hidden-surface removal, illumination and shading models, morphing, etc., warping, texture mapping, raytracing, radiosity, and introduction to animation.
Introduction to Multimedia Technology
This course introduces basic principles and algorithms used in the current technologies of multimedia systems. One of these course goals is to give the student hands-on experience in multimedia data representation, compression, processing, and retrieval. The course also addresses sound transmission, music streaming, 2-D and 3-D graphics, image, and video. It also explores human perceptual problems associated with multimedia technologies.
Introduction to Scientific Visualization
Among the most significant scientific challenges of the 21st century will be effectively understanding and using the vast amount of information produced by supercomputers, sensors, and extensive simulations. By its very nature, visualization addresses the challenges created by such excess: too many data points, too many variables, too many time steps, and too many potential explanations. Thus, as we work to tame the accelerating information explosion and employ it to advance scientific, biomedical, and engineering research, visualization will be among our most essential tools. This course introduces scientists, engineers, and practitioners in medicine to data visualization fundamentals.
This graduate-level course introduces haptics, focusing on teleoperated and virtual environments displayed through the sense of touch. Topics covered include human haptic sensing and control, design of haptic interfaces (tactile and force), haptics for teleoperation, haptic rendering and modelling of virtual environments, control and stability issues, and medical applications such as telesurgery and surgical simulation. In addition, this course addresses students with interests in robotics, virtual reality, or computer-integrated surgical systems.
This course presents the latest research results in point-based computer graphics. After an overview of the critical research issues, we will discuss 3D scanning devices and novel concepts for the mathematical representation of point-sampled shapes. Next, the course describes high-performance and high-quality point model rendering, including advanced shading, anti-aliasing, and transparency. It also offers efficient data structures for hierarchical rendering on modern graphics processors and summarizes geometric processing methods, filtering, re-sampling point models, and physical modelling.
In recent years, sensors and algorithms for three-dimensional (3D) imaging and modelling of natural objects have received significant attention. The computer vision and graphics research communities are also increasingly used as tools for various applications in medicine, manufacturing, archaeology, and any field requiring 3D modelling of natural environments. This course's primary goal is to present a general overview of digital 3D imaging technology from photogrammetry to tomographic systems, and the various modelling techniques necessary to create 3D models of large and small structures compatible with multiple manufacturing and medical applications.
Advanced Signal Processing for Computer Scientists
This class addresses the representation, analysis, and design of discrete-time signals and systems. The central concepts covered include discrete-time processing of continuous-time signals; decimation, interpolation, and sampling rate conversion; flow-graph structures for DT systems; time-and frequency-domain design techniques for recursive (IIR) and non-recursive (FIR) filters; linear prediction; discrete Fourier transform, FFT algorithm; short-time Fourier analysis and filter banks; Wavelet Transform; Wiener and Kalman Filters, and various applications. This course qualifies as a breadth requirement in theory.
Real-time Digital Signal Processing Using GPU
This class addresses the representation, analysis, and design of discrete-time signals and systems. The central concepts covered include discrete-time processing of continuous-time signals; decimation, interpolation, and sampling rate conversion; flow-graph structures for DT systems; time-and frequency-domain design techniques for recursive (IIR) and non-recursive (FIR) filters; linear prediction; discrete Fourier transform, FFT algorithm; short-time Fourier analysis and filter banks; multivariate techniques; Wavelet Transform; Cepstral analysis, Wiener and Kalman Filters, and various applications. We also discuss and analyse the GPU implementations of many of these algorithms.
Fundamentals of Medical Imaging
After reviewing one-dimensional signal processing and sampling, the course will first review the two-dimensional signal processing theory. We will then study four general medical imaging modalities: projection radiography, computed tomography, magnetic resonance imaging, and ultrasound. The goal will be to understand these modalities in terms familiar to engineers and physicists. Flexibility exists to vary each topic area's depth and penetration after determining the students' general background and experience.
The course deals with moral, legal, and social issues of computer technology. Many ethical problems that did not exist before are now omnipresent. For example, one can still get news from many mainstream sources that employ professional reporters who gather and validate the information before publishing. Unfortunately, numerous online news media deliver rumours and false news with dubious political agendas. Social media are a great way to interact with your family and friends, but they can threaten personal privacy. This course explores these issues and more.
Introduction to Human-Computer Interaction
This course introduces students to human-computer interaction topics, focusing on human capabilities and limitations, interaction design, current and future interactive systems and devices, and evaluating their usability.
Introduction to GPU Programming
This course introduces how to program heterogeneous parallel computing systems such as GPUs. The course covers CUDA language, functionality, and maintainability of GPU, how to deal with scalability, portability issues, technical subjects, parallel programming API, tools and techniques, principles and patterns of parallel algorithms, processor architecture features, and constraints.
Introduction to Virtual/Augmented Reality and Telepresence
Virtual and augmented reality can provide an immersive environment for testing scenarios, games, and training. For example, manufacturing and engineering tasks, medical planning and training, art and design, rehabilitation, Physics, Biology and Chemistry concept exploration, and many others can benefit from a virtual reality environment. This course focuses on the challenges of setting up a user-friendly virtual reality scene where users can interact intuitively and naturally. Interactive techniques and sensor-based devices, such as haptic and head-mount displays, create a virtual environment for scientific analysis, visualisation exploration, and Tele-presence. How mobile users can participate in these applications will be discussed.
Quantum Computing for Computer Scientists
This course introduces the theory and applications of quantum information and quantum computation from the computer science perspective. The course will cover classical information theory, compression of quantum information, quantum entanglement, efficient quantum algorithms, quantum error-correcting codes, fault-tolerant quantum computation, and quantum machine learning. The course will also cover quantum computation physical implementations into real quantum computers. We also explore programming languages using the real-world utilising state-of-the-art quantum technologies through the IBM Q Experience, TensorFlow Quantum, Microsoft Quantum Development Kit, and D-Wave.
Deep Learning for Medical Image Analysis
The past twenty years of clinical applications of multimodal medical imaging (CT, MRI, US, PET/CT/MR, etc.) have revolutionised how medicine is practised today by improving disease diagnostics and treatment. In the last decade, Deep Neural Networks (DNN) usage in this field has opened new doors to process those images, allowing automatic segmentation, multimodal sensor fusion and registration, and computer-aided diagnosis. This course will review the various DNN architectures found in the literature and explore their practical clinical applications. Coursework includes homework, programming assignments, reading, and discussion of research papers, presentations, and a final project.
(As
I have retired, I no longer take new graduate students.)
Remaining Graduate Students
Ph.D. Students
Bernal Manzanilla, Ph.D. CS, Sparse Methods for Image Denoising and Feature Selection in Lung Ultrasound and Echocardiography
Master Students
Scott Assen, MSc CS, Early Detection of Pancreatic Cancer in CT
Sadid Bin Hasan, MSc CS, Geometric Approach to Signal Processing
Naiad Lab Inc.
Big Data Analysis and AI Solutions
Naiad Lab Inc. is committed to using the latest advancements in artificial intelligence to solve complex problems and drive innovation. We have a team of experienced and skilled professionals who specialize in Big Data Analytics, AI/ML, Signal Processing, and Computer Vision. We understand the unique challenges facing industries. Our team provides cutting-edge solutions based on big data analysis that help our clients make informed decisions and achieve their goals.
Esmatullah Naikyar, CEO in charge of project management and business development
Pierre Boulanger, CTO in Charge of R&D and new software development
Michael Feist, Chief Software Architect
Talwinder Punni, CFO in charge of marketing and financial management
Azal Mansouri, Business development manager
Last Update January 2025