Video Coding Professors and Supervisors in Canada

Feng Liuمشاهده پروفایل

دانشیار

Saint Mary's UniversityCanada

Feng Liu is an Associate Professor in the Department of Management at the Sobey School of Business, Saint Mary's University. His research focuses on emotional dynamics in strategic management, top management team behavior, and qualitative research methodologies, particularly video ethnography and observational techniques. He holds a PhD and has contributed extensively to understanding how emotions influence strategic decision-making processes in organizational contexts. Key research interests include the role of emotions in strategizing, top management team constellations, and the application of video ethnography in organizational studies. His work bridges strategic management theory with qualitative research practices, emphasizing methodological rigor in capturing emotional dimensions of organizational phenomena. Publications span topics from emotional dynamics in strategic conversations to participatory approaches in video ethnography, reflecting a sustained focus on understanding human elements in organizational decision-making. His research has been published in strategic management and organizational behavior journals, contributing to both theoretical and methodological advancements.

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Sirisha Rambhatlaمشاهده پروفایل

استادیار

University of WaterlooCanada

Sirisha Rambhatla is an Assistant Professor at the University of Waterloo, holding appointments in the Management Sciences Department (Faculty of Engineering), Systems Design Engineering, and the David R. Cheriton School of Computer Science (Faculty of Mathematics). She leads the CriticalML lab, focusing on reliable AI for healthcare, aviation, and climate change, leveraging deep learning and spatiotemporal analysis. Her work bridges theory and practice, with publications at top venues like NeurIPS, ICLR, and clinical journals. Affiliations include the Waterloo AI Institute and the Waterloo Institute for Sustainable Aeronautics. Education: Ph.D. and M.S. in Electrical Engineering, University of Minnesota (2019, 2012) B.Tech in Electronics and Telecommunication Engineering, College of Engineering Roorkee (2010) Research Interests: Representation Learning, Spatiotemporal Data Analysis, AI for Healthcare, Computer Vision, and provable algorithms. Her work emphasizes interpretable AI and applications in surgery, intelligent automation, and climate modeling. Awards: 2021 Merit Award for Excellence in Postdoctoral Research E. Bruce Lee Memorial Fellowship University Bronze Medalist (2010) Teaching: Courses include Advanced Machine Learning (MSCI 546/MSE 546), Big Data Analytics (MSCI 623), and Special Topics in Management Engineering (MSCI 598/MSE 598). Labs/Teams: Leads the CriticalML lab, focusing on trustworthy AI systems for critical applications.

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Renjie Liaoمشاهده پروفایل

استادیار

University of British ColumbiaCanada

Renjie Liao is an Assistant Professor (tenure-track) in the Department of Electrical and Computer Engineering (ECE) at the University of British Columbia (UBC), with an associated appointment in the Department of Computer Science. He is also a Faculty Member at the Vector Institute and a Canada CIFAR AI Chair. Prior to UBC, Dr. Liao was a Visiting Faculty Researcher at Google Brain and held a Senior Research Scientist position at Uber Advanced Technologies Group during his PhD. He earned his B.Eng. (Automation) from Beihang University, M.Phil. (CS) from the Chinese University of Hong Kong, and PhD (CS) from the University of Toronto. His research focuses on probabilistic and geometric deep learning , with key contributions in deep generative models, geometric deep learning, neural algorithmic reasoning, and generalization bounds. Notable areas include 3D point cloud analysis, self-driving systems, and healthcare applications using graph neural networks. His work bridges theoretical foundations (e.g., PAC-Bayes bounds) with practical applications like motion forecasting and medical imaging. Education: B.Eng. in Automation, Beihang University (2011) M.Phil. in Computer Science, CUHK (2015) PhD in Computer Science, UofT (2021) Dr. Liao has received awards such as the RBC Graduate Fellowship and Connaught International Scholarship. His lab (Deep Structured Learning Lab) emphasizes principled mathematical approaches to solving complex problems. He advises students in machine learning, computer vision, and robotics, encouraging applications from those with strong coding/mathematical backgrounds. Labs/Teams: Deep Structured Learning Lab (UBC) Vector Institute Collaboration

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Sheldon Linمشاهده پروفایل

استاد

University of TorontoCanada

X. Sheldon Lin is a Professor of Actuarial Science at the Department of Statistical Sciences, University of Toronto. His research focuses on actuarial science, specifically in areas like Loss Modelling , Insurance Risk Management , and Financial Insurance . He has authored two influential books in the field: Introductory Stochastic Analysis for Finance and Insurance and Lundberg Approximations for Compound Distributions With Insurance Applications . Research Interests : Actuarial science, mathematical finance, applied probability, loss modeling, insurance risk management, financial insurance, and algorithmic trading. His work includes developing computational models like the GEM-CMM algorithm for fitting Erlang mixture distributions to insurance data and creating R codes for loss modeling. Teaching : Sheldon Lin teaches advanced courses in actuarial science, including ACT452H1S: Loss Models II , where he provides lecture notes, videos, and practice problems for actuarial exams. Scientific Awards ASA (Associate of the Society of Actuaries) ACIA (Associate of the Canadian Institute of Actuaries) Books Authored : Introductory Stochastic Analysis for Finance and Insurance (Wiley Series in Probability and Statistics) Lundberg Approximations for Compound Distributions With Insurance Applications (Lecture Notes in Statistics 156, Springer with Gordon E. Willmot)

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André Zaccarinمشاهده پروفایل

استاد

Laval UniversityCanada

André Zaccarin is a Professor in the Department of Electrical and Computer Engineering at the College of Engineering, Université Laval, where he has been a faculty member since 1991. He holds a Ph.D. in Electrical Engineering from Princeton University and has maintained a strong research presence in image and video processing, computer vision, and coding algorithms. Ph.D. in Electrical Engineering, Princeton University (1991) M.A., Princeton University (1988) M.Sc. in Electrical Engineering, Université Laval (1987) B.Sc.A. in Electrical Engineering, Université Laval (1985) His research focuses on the study and development of advanced coding algorithms for still images and video sequences. Key areas include dense motion field estimation, model-based coding, 3D motion models, segmentation-based coding, and fast coding algorithms. He also investigates image segmentation, analysis and modeling, with applications in medical imaging and motion estimation for computer vision. Hyperspectral image processing is another significant area of interest. With 61 available publications, his scholarly output reflects sustained contributions in signal and image processing, particularly in compression and computer vision. The body of work shows consistent engagement with algorithmic innovation, motion analysis, and practical implementations in imaging systems. While specific scientific awards are not listed in the provided text, his long-standing academic career and industrial research role suggest recognition within the field. André Zaccarin has supervised multiple student projects, though specific names are not provided. He was also affiliated with Intel Corp. as a Senior Staff Researcher at the Microprocessor Research Labs from 2000 to 2001, indicating industry collaboration. He served as an Invited Researcher at Princeton University during summer 1992. He is affiliated with the Computer Vision and Systems Laboratory at Université Laval, where his research group conducts work in vision systems and image processing technologies.

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Michael D. Adamsمشاهده پروفایل

دانشیار

University of VictoriaCanada

Michael D. Adams is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Victoria in Victoria, British Columbia, Canada. He holds a BASc from the University of Waterloo, a MASc from the University of Victoria, and a Ph.D. from the University of British Columbia, with PEng professional registration. His research focuses on digital signal processing, wavelets and filter banks, image/video/audio coding, multimedia systems, and the efficient implementation of signal-processing algorithms. His expertise spans software development, geometry processing, educational methodologies, and security systems. No recent articles or scientific awards are explicitly listed in the provided materials. His academic profile emphasizes technical education and applied research in electrical and computer engineering disciplines.

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Julien Arinoمشاهده پروفایل

استاد

University of ManitobaCanada

Julien Arino is a Professor in the Department of Mathematics at the University of Manitoba , specializing in Mathematical Epidemiology and Mathematical Population Dynamics . He is an active member of the Mathematical Biology group and focuses on the role of movement in disease transmission, with applications to both human and animal health. Department of Mathematics Faculty of Science University of Manitoba His research spans: Metapopulation dynamics with explicit movement Multi-pathogen and multi-species transmission modeling Computational epidemiology with data science integration Evaluation of travel control measures for disease variants Recent work includes publications on: Cholera transmission in Chad Multi-species bovine tuberculosis dynamics Measles resurgence patterns Software tools for R compilation and data visualization He maintains a public YouTube channel with lecture videos on Mathematical Epidemiology and contributes open-source code for scientific computing in R and Python. His methodological contributions include novel approaches to plot formatting and computational efficiency in epidemic modeling.

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Amir Michalovichمشاهده پروفایل

استادیار

University of ManitobaCanada

Amir Michalovich is an Assistant Professor of Literacies Education at the Faculty of Education, University of Manitoba. He specializes in digital and multimodal literacies, focusing on newcomer youth from refugee and marginalized backgrounds. Education: PhD (2023) and MA (2017) from Tel Aviv University, BFA (2015) from Tel Aviv University His research explores digital multimodal composing as a tool for language and literacy learning, teacher professional development in multiliteracies, and participatory media. Recent work examines refugee-background youth's use of video production for identity expression and ethical challenges in research-based theatre. Amir's publications highlight trends in digital storytelling, multimodal data analysis, and multilingual education. He integrates arts-based research and qualitative methodologies into classroom practices. Scientific Awards: SSHRC Postdoctoral Fellowship (2023) He is affiliated with the American Educational Research Association, Canadian Association of Applied Linguistics, and other professional networks. His background as a filmmaker and multilingual education policy manager in Israel informs his interdisciplinary approach.

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