Nicole Almeida is an Assistant Professor in the Department of 3D Animation at Emily Carr University of Art + Design. Her role involves teaching and research in 3D animation and related digital media disciplines. While no formal education details are provided here, her position suggests advanced qualifications in animation or computer graphics. Research interests likely center around cutting-edge techniques in 3D modeling, visual effects, and interactive design applications. No specific grants, awards, or published articles are listed in the provided text. Her professional focus appears to align with the technical and creative aspects of 3D animation within academic and industry contexts.
John C. Bowman is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. He has maintained an active teaching career with courses spanning undergraduate to graduate levels, including Honours Calculus (Math 117/118), Real Variables (Math 417), Coding Theory (Math 422), Linear Algebra II (Math 225), and Statistical Theories of Turbulence (Math 655). His office is located in CAB 521, and he has consistently provided regular office hours for student consultation across multiple courses. Dr. Bowman earned his BS in Engineering from the University of Alberta, followed by an MA and PhD from Princeton University. His educational background has informed his diverse research trajectory spanning mathematical physics, computational mathematics, and scientific visualization. His teaching philosophy emphasizes deep conceptual understanding, as evidenced by his development of comprehensive hyperlinked 3D lecture notes for Honours Calculus. His primary research focuses on statistical theories of turbulence, particularly his development of Spectral Reduction as a reduced statistical description of turbulence. His work shows remarkable agreement with full numerical simulations, even in flows containing long-lived coherent structures. Additional research interests include 3D vector graphics (notably the Asymptote language he developed), implicit dealiasing of convolutions, exponential integrators, and exactly conservative integrators for numerical simulations. He has been involved with the University of Alberta's Geophysical Fluid Dynamics Research Group, where his work bridges theoretical mathematics with practical computational applications. Dr. Bowman's publication record demonstrates consistent contributions to computational mathematics and fluid dynamics over several decades. His recent work (2015-2024) shows continued innovation in computational methods for turbulence simulation, with particular attention to dealiasing techniques, efficient convolution algorithms, and novel approaches to spectral methods. His research has practical applications in assessing dissipation mechanisms in large-eddy simulations and modeling high-Reynolds number turbulence. As an educator, Dr. Bowman has developed comprehensive teaching materials and maintains an active engagement with students through office hours and detailed course websites. His commitment to mathematical education extends to high school outreach through contributions to the "Pi in the Sky" mathematics magazine. His work on the Asymptote vector graphics language has created a valuable tool for mathematical visualization and scientific publishing that integrates seamlessly with TeX.
Roles and Affiliations: Dorothea Blostein is a Professor in the School of Computing at Queen's University, part of the Faculty of Arts and Science. She has held this position since 1988 after earning her Ph.D. (1987) and B.Sc. (1978) from the University of Illinois and an M.Sc. (1980) from Carnegie Mellon University. Research: Her work focuses on the interface between paper and electronic documents, with specialties in graphics recognition, document analysis, and biomedical computing. Key areas include tensegrity structures for biomechanical modeling, adaptive systems, and artificial fascial networks. She also explores music notation recognition and software engineering document classification. Collaborations include projects with NASA on tensegrity robotics and studies on concussion prevention through helmet fitting systems. Teaching: She teaches courses such as CISC 859 (Pattern Recognition) and CISC 324 (Operating Systems). Her pedagogical approach emphasizes hands-on projects and theoretical foundations in AI and algorithms. Labs and Teams: Her research leverages tools like the NASA Tensegrity Robotics Toolkit and ArtiSynth. She collaborates with institutions like the Gordon Lab for embryonic modeling and biomechanical engineers on cytoskeleton simulations.
Dr. Paul Hungler is an Associate Professor in the Department of Chemical Engineering and a member of the Ingenuity Labs Research Institute at Queen’s University. With a PhD and P.Eng designation, he specializes in adaptive simulation technologies, engineering education innovation, and the application of machine learning to virtual/augmented reality (VR/AR) systems. His career includes prior service as a Major in the Royal Canadian Air Force, where he developed VR/AR training solutions for the Canadian Armed Forces. His research focuses on dynamically adaptive simulations that enhance education and training through real-time cognitive load assessment and expertise classification. This work leverages wearable sensors, multimodal data fusion, and deep learning techniques. Notable areas of interest include VR-based capstone design tools for engineering students and interdisciplinary initiatives to improve STEM education outcomes. Dr. Hungler’s lab (The Hungler Lab) develops open-source VR chemical processing platforms and explores the triple-bottom-line framework in engineering design education. His publications span cognitive load measurement, gaze estimation, and emotion recognition systems, often with applications in healthcare and transportation safety. His work bridges engineering education, computer science, and human factors to create next-generation adaptive learning environments.
Christopher Batty is an Associate Professor and Director of Infrastructure at the University of Waterloo's Department of Computer Science. His research focuses on computer graphics and scientific computing, with an emphasis on physics-based numerical simulation of fluids and solids for applications in animation, visual effects, and interactive environments. He holds a Ph.D. from the University of British Columbia (2010) and a B.C.Sc. from the University of Manitoba (2004). His work spans fluid dynamics, solid mechanics, and geometry processing, addressing challenges like surface reconstruction, multi-scale simulations, and efficient solvers for complex fluid-solid interactions. Recent contributions include novel methods for divergence-free fluid editing, discrete elastic rod optimization, and Monte Carlo-based approaches for PDEs on surfaces. Batty’s research integrates computational geometry, numerical analysis, and optimization to create scalable and accurate tools for procedural fluid and solid simulation. His articles emphasize robustness in handling thin obstacles, narrow gaps, and intricate boundary conditions, often leveraging advanced techniques like closest point methods and monolithic solvers. No scientific awards are listed, though his extensive publication record reflects significant contributions to the field. He leads projects on adaptive liquid simulations, surface-only deformable models, and high-resolution embedded fluid surfaces.
David Lau is a Lab Instructor/Supervisor at the University of Waterloo, specializing in computer vision and machine learning applications across sports analytics and environmental monitoring. His work focuses on developing algorithms for hockey puck localization, player tracking, and sea ice classification using SAR imagery. He contributes to datasets like AI4Arctic and explores synthetic data augmentation techniques for improving model robustness. Research interests include deep learning architectures for multi-task learning, contextual cue utilization in vision systems, and the integration of remote sensing data with climate models. Key areas of exploration involve improving hockey video analysis through homographic projections and leveraging Bayesian methods for uncertainty quantification in environmental predictions. Recent work emphasizes domain-specific adaptations like sports field localization and ice hockey event detection. His contributions span from foundational algorithms (e.g., IceGCN for SAR imagery) to practical systems like PuckNet for puck location estimation in broadcast videos. Advising and grants: No specific grants or advisees listed in available information. Collaborates on projects involving hockey analytics teams and environmental monitoring initiatives, though specific team names are not disclosed.
Lila Kari is a Professor and Cheriton Faculty Fellow at the School of Computer Science , part of the University of Waterloo in Ontario, Canada. Her research focuses on Biodiversity informatics , data science , and machine learning applications in comparative genomics and metagenomics , particularly for analyzing genomic signatures through Chaos Game Representation (CGR) and alignment-free methods. 2023 : Environment and taxonomy shape genomic signatures of extremophiles 2020 : Machine learning for rapid pathogen classification during pandemics 2019 : Ultrafast DNA sequence classification with ML-DSP 2016 : Additive genomic signatures for enhanced taxonomic differentiation 2015 : Mapping genomic signature spaces for molecular distance analysis 2009-2005 : Foundational work in DNA language theory and computational biology Her work has been instrumental in developing composite DNA signatures that combine nuclear and organellar genomic data for improved species differentiation, and assembled DNA signatures that enable analysis from fragmented sequencing data. She contributes to global initiatives like BIOSCAN for biodiversity surveillance and has created tools such as MLDSP-GUI for accessible DNA sequence analysis.
George Labahn is Professor and Director of the Symbolic Computation Group at the University of Waterloo's David R. Cheriton School of Computer Science. His research spans computer algebra, computational finance, and pen-based mathematical interfaces. Key contributions include fundamental algorithms for matrix normal forms (Hermite/Smith forms), symbolic-numeric polynomial computation, and development of the MathBrush system for handwritten mathematics recognition. Research focuses on efficient algorithms for polynomial matrix arithmetic, rational approximation, and solving differential equations with elliptic coefficients. Computational finance work develops numerical methods for option pricing under jump diffusion models. Current projects include fast Hermite form computation and rank-sensitive matrix algorithms. Serves as associate editor for Journal of Symbolic Computation (JSC) and previously for ACM Transactions on Mathematical Software (TOMS). Authored core Maple programming guides and implemented key packages for symbolic integration, differential equations, and graphics in Maple.
Jack Callaghan is a Professor and Canada Research Chair in Spine Biomechanics and Injury Prevention at the University of Waterloo's Department of Kinesiology. His research focuses on spinal mechanics, ergonomics, and injury prevention, with a particular emphasis on lumbar spine kinematics, seated postures, and occupational health interventions. He has contributed to studies on intervertebral disc herniation mechanisms, biomechanical responses to spinal loading, and the design of active sitting chairs to reduce musculoskeletal strain. Education details are not explicitly provided in the text, but his academic position suggests advanced training in biomechanics or related fields. His research interests include analyzing lumbar spine dynamics during prolonged sitting/standing, evaluating ergonomic office environments, and investigating spinal injury mechanisms using porcine models. Recent work highlights his exploration of facet joint orientation effects on spinal mobility, the role of axial twist in workplace posture, and optimizing force plate data for jump performance analysis. His studies often bridge clinical and biomechanical approaches, aiming to reduce low back pain through evidence-based interventions. Notable contributions include field studies on office workers' kinematics and experimental models to dissociate spinal injury mechanisms. While no specific awards are listed, his Canada Research Chair designation reflects academic recognition.
Justin W.L. Wan is a Professor at the David R. Cheriton School of Computer Science, University of Waterloo. He holds a Canada Research Chair in Scientific Computing and is currently Co-Director for the Computing and Financial Management Program. He earned his BSc from Hong Kong (1992), MA (1995) and PhD (1998) from UCLA's Applied Math Program. Research interests include: Scientific computing Medical image processing (CT scans, cell segmentation, image registration) Computational finance (option pricing, regime switching, hedging parameters) Computer graphics simulation of natural phenomena Development of robust multigrid methods for PDEs Recent publications span: 2021: Multigrid methods for mean-field games and optimal mass transport 2020: Deep learning approaches for high-dimensional finance and CT artifact reduction 2019: Multigrid for Monge-Ampere equations and multi-asset options 2018: Cellular image segmentation algorithms Research team includes: PhD students: Hossein Aboutalebi, Andrew Na, Connor Tannahill, Chris West Master's students: Ying Kit (Marco) Hui, Shujie Liu, Lufan Wang Administrative roles held: Director, Centre of Computational Mathematics for Industry and Commerce (2010-2015) Associate Director, Cheriton School of Computer Science (2015-2018) Director of Graduate Studies (2019-2021) Professional service: Secretary, Canadian Applied and Industrial Mathematics Society (2015-2020) Organizer, CAIMS Annual Meeting (2021) Journal editor and program committee member
Stephen Mann is a Professor at the University of Waterloo's Department of Computer Science. His research focuses on geometric algebra, CNC machining, and spline surfaces, with applications in computer graphics and manufacturing automation. He has taught courses including CS 251 (Computer Organization), CS 488 (Computer Graphics), and specialized topics like splines in graphics. His work bridges theoretical geometric modeling with practical manufacturing challenges. Educational background includes a Doctorate from Washington University and degrees from UC Berkeley. His publications emphasize tool path optimization, neural networks in machining feature recognition, and geometric algebra frameworks. Notable contributions include books on geometric algebra and spline theory. Current research trends involve integrating machine learning with CNC processes and developing efficient algorithms for multi-axis machining. He is actively recruiting graduate students interested in computational geometry and manufacturing systems.
Pooya Ronagh is a Research Assistant Professor at the University of Waterloo, affiliated with the Department of Physics & Astronomy and the Institute for Quantum Computing (IQC). He also serves as a Scientific Lead at the Perimeter Institute Quantum Intelligence Lab (PIQuIL) and directs the Hardware Innovation Lab at 1QBit. His work bridges quantum computation, machine learning, and optimal control, focusing on quantum algorithms, error correction, and hybrid quantum-classical systems. Education: PhD in Mathematics (University of British Columbia, 2016), MSc in Mathematics (UBC, 2011), dual BSc in Mathematics and Computer Science (Sharif University of Technology, 2009). Awards include the Benjamin Franklin Fellowship (2009). Research Interests: Quantum algorithms for machine learning, reinforcement learning, fault-tolerant quantum architectures, cryogenic systems, and quantum control. He explores applications of quantum simulation to improve learning efficiency and robustness in AI systems. Recent work includes optimizing quantum error correction decoders, developing scalable superconducting architectures, and advancing neural network-based quantum state tomography. His contributions span theoretical frameworks (e.g., lattice surgery scheduling) and experimental methods (e.g., SFQ pulse control). Teaching: Courses like PHYS 490 (Machine Learning in Physics) emphasize practical coding and interdisciplinary projects. Grants and collaborations involve industry and academic partners in quantum hardware and software development. Labs: Hardware Innovation Lab (1QBit), IQC Quantum Control Group Future Work: Scaling quantum supercomputers, cryogenic neural decoders, quantum-enhanced generative AI
Matthias Schonlau is a Professor in the Department of Statistics at the University of Waterloo. He previously worked as a statistician at the RAND Corporation (1999-2011), where he led the RAND Statistical Consulting Service. He holds a PhD from the University of Waterloo (1997) and a Master's from Queen's University (1993). His research focuses on survey methodology, natural language processing for open-ended questions, data visualization, and statistical software development. Key contributions include the Hammock Plot for mixed data visualization and automated classification algorithms for open-ended survey responses. His work spans algorithmic innovation (e.g., occupation coding, multi-label classification) and statistical software tools (e.g., HAMMOCK and RFOREST modules for Stata). Recent projects address semi-automated classification, one-shot learning, and text dataset distillation. He has held sabbaticals at the University of Auckland (2015-2016) and DIW Berlin (2009-2010), collaborating with the Max Planck Institute. Major awards include the Humboldt Research Prize (2022) and ASA Fellowship. His publications emphasize bridging statistical methods with practical applications, including books like Applied Statistical Learning (2023) and peer-reviewed articles in computational statistics and machine learning.
Richard Murray is a Full Professor in the Department of Biology at the Faculty of Science. His research focuses on visual perception, particularly lightness constancy, and explores how humans and AI models perceive lightness in varied environments such as virtual reality (VR), flat-panel displays, and real-world settings. He investigates the interplay between natural lighting cues, rendering artifacts, and perceptual mechanisms, employing deep learning models and psychophysical experiments. His work bridges computer vision and neuroscience, analyzing mid-level lightness illusions, intrinsic image decomposition, and decision spaces in complex scenes. Murray has developed novel visualization techniques like the 'noise prism' and contributed to VR display calibration methodologies. His research often addresses discrepancies between human perception and computational models, emphasizing the limitations of current AI in replicating human visual constancy. No scientific awards or grants are explicitly listed in the provided information. His advising activities and student collaborations are not detailed here. Laboratory or team affiliations are not specified, though his work suggests involvement with multidisciplinary teams in vision science and computational modeling.
Eugene Belilovsky is an Associate Professor at the University of Montreal's Department of Computer Science and Operational Research and an Assistant Professor at Concordia University's Department of Computer Science and Software Engineering. He is also an Associate Member of Mila – Quebec Institute for Artificial Intelligence. His research focuses on computer vision, deep learning, and their applications in areas like continual learning and few-shot learning at the intersection of vision and natural language processing. Belilovsky's expertise includes distributed systems, federated learning, and optimization. His work addresses challenges in model generalization, spurious correlations, and efficient training strategies. He has advised numerous graduate students, including Charles-Étienne Joseph, Medric B. Djeafea Sonwa, Gwendolyne Legate, and Irene Tenison. His recent publications highlight contributions to federated learning, continual pre-training, and fairness in AI systems. Notable work includes optimizing distributed learning protocols and mitigating forgetting in evolving data streams. Belilovsky's research also explores clinical applications, such as diagnosing hepatic steatosis using ultrasound imaging through deep learning techniques. He collaborates with institutions like Mila and the DIRO department, advancing interdisciplinary projects in AI-driven healthcare, robotics, and language modeling.
