Contact
Professor, Faculty of Science - Computing Science
- pierreb@ualberta.ca
- Phone
- (780) 492-3031
- Address
-
411 Athabasca Hall
9119 - 116 St NWEdmonton ABT6G 2E8
- Availability
- By appointment only using Zoom
Overview
Area of Study / Keywords
Advanced Human-Computer Interfaces Virtual and Augmented Reality Medical Imaging Telemedicine GPU Computing
About
Education
- B.Sc., Engineering Physics, Laval University, 1980
- M.Sc., Physics, Laval University, 1982
- Ph.D., Electrical Engineering, University of Montreal, 1994
Chair Positions
- Cisco Research Chair in Healthcare Systems
Bio
Dr. Boulanger cumulates more than 40 years of experience in 3D computer vision, rapid product development, and the applications of virtual reality systems to medicine and industrial manufacturing. Dr. Boulanger worked for 18 years at the National Research Council of Canada as a senior research officer. His primary research interest was in 3D computer vision, rapid product development, and virtualized reality systems. He now has a double appointment as a professor at the University of Alberta Department of Computing Science and the Department of Radiology and Diagnostic Imaging. He is currently the Director of the Advanced Human-Computer Interfaces (AHCI) Laboratory and the SERVIER Virtual Cardiac Centre's Scientific Director. In 2013, Dr. Boulanger was awarded the CISCO chair in healthcare solutions, a ten-year investment by CISCO systems to develop new healthcare IT technologies in Canada. The chair ended in March 2022 and most of the MedRoad activities will be transferred to Naiad Lab Inc where he will continue his research work as the CTO of the company. Naiad Lab Inc is a start-up dedicated to using advanced technology solutions to enhance our clientele's health and quality of life worldwide and to commercialize the technologies developed during the CISCO chair. His work has contributed to gaining international recognition in this field, publishing more than 400 scientific papers and collaborating with numerous universities, research labs, and industrial companies worldwide. He is on the editorial board of two major academic journals.
Research
Areas
- Advanced Human-Computer Interfaces
- Virtual and Augmented Reality for Medical Training
- Medical Imaging in the Field of Radiology
- Telehealth and Cloud-based Medical Analytics
- GPU Computing for Image Processing
- Quantum von Neumann Computer Architectures
Interests
New Neural Network Architectures, Tele-Immersion, Sensor-Based Geometric Modeling, Sensor Fusion, Quantum Computing, Parallel Computing
Summary
As part of the CISCO Chair, my research topics consist of developing new telemedicine techniques, patient-specific modelling using sensor fusion, and applying telepresence technologies to medical training, simulation, and collaborative diagnostics. I also work with the Department of Radiology on medical imaging ranging from image-guided surgery, surgical planning using patient-specific models, and robotics-based multiview ultrasound imaging. I am recently doing some work on the design of new quantum von Neumann architectures.
List of the most recent publications Publications
Teaching
Every year I teach one undergrad and one graduate course from this list:
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 warping, texture mapping, ray-tracing, 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 for 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. In the computer vision and graphics research communities, they are also increasingly used as tools for various applications in medicine, manufacturing, archeology, and any field requiring 3D modelling of real 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.
Biomedical Digital Signal Processing
This class addresses the representation, processing, and analysis of biomedical discrete-time signals like ECG, EEG, etc. The central concepts covered include the biological basis of biomedical signals, discrete-time processing of continuous-time signals; decimation, interpolation, and sampling rate conversion, 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, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and various applications.
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 analyze 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, visualization 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, 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 utilizing 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 revolutionized today's medicine 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. Course work includes homework, programming assignments, reading and discussing research papers, presentations, and a final project.
Courses
CMPUT 604 - Topics in Computing Science
MM 807 - Multimedia Project I
Sections offered at an increased rate of fee assessment; refer to the Tuition and Fees page in the University Regulations sections of the Calendar.
MM 808 - Multimedia Project II
Sections offered at an increased rate of fee assessment; refer to the Tuition and Fees page in the University Regulations sections of the Calendar.
MM 809 - Multimedia Supplementary Project
Sections offered at an increased rate of fee assessment; refer to the Tuition and Fees page in the University Regulations sections of the Calendar.