جست‌وجوی استاد و پژوهشگر

Qingguo Liمشاهده پروفایل

استاد

• Biomechanical system design
• Energy harvesting
• Wearable sensors
• +۴ مورد دیگر

Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .

Aaron Smithمشاهده پروفایل

دانشیار

• Applied Probability
• Computational Statistics
• Monte Carlo Methods
• +۳ مورد دیگر

Aaron Smith is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa, affiliated with the Faculty of Science. He holds a PhD from Stanford University. His research focuses on applied probability, computational statistics, Monte Carlo methods, and Markov chains, with an emphasis on advancing theoretical understanding and practical applications of these methodologies. Dr. Smith's work includes contributions to community detection algorithms, Markov chain mixing times, and synthetic health data generation. His recent publications explore topics such as nonstandard Dirichlet form representations, perturbation analysis of MCMC algorithms, and sparse Bayesian multidimensional scaling. He advises students in applied probability and has supervised postdoctoral researchers in related fields. His research interests span a wide range of topics, including stochastic processes, statistical inference, and algorithm design. He is particularly known for his analysis of convergence rates in Markov chains and the development of efficient sampling techniques for complex models. His interdisciplinary work bridges theoretical mathematics and practical computational challenges in data science and healthcare. Dr. Smith collaborates on projects involving synthetic data frameworks for privacy-preserving applications and has contributed to foundational work on mixing times and perturbation effects in stochastic systems.

Scott Chenمشاهده پروفایل

استادیار

• Embedded Systems
• RF Technologies
• Biomedical Sensors
• +۵ مورد دیگر

Dr. Scott Chen is an Assistant Professor in the Department of Electrical & Computer Engineering at McMaster University, where he focuses on teaching and research in embedded systems, RF technologies, and biomedical sensors. He previously held roles as a lecturer at the University of Waterloo and program coordinator at Conestoga College, alongside industry experience in embedded systems engineering and sensor development. Education: B.A.Sc. (Simon Fraser University, 2007) and Ph.D. (University of Waterloo, 2015), followed by a MITAC postdoctoral fellowship. His industry experience includes roles at Thalmic Labs/North, Sober Steering Sensors, and Equustek Solutions. Research interests span embedded systems for IoT, RF biomedical sensors, cleanroom micro/nano-fabrication, and game design in Unity. Notable achievements include a 2018 US patent for ethanol sensing technologies and a 2021 teaching award nomination. Current courses taught include Principles of Programming (COMPENG 2SH4), Data Structures and Algorithms (COMPENG 2SI3), and Introduction to Electrical Engineering (ELECENG 2CI4). Awards: US Patent 9,958,444B2 (2018), nominated for Aubrey Hagar Distinguished Teaching Award (2021). His work bridges academia and industry, emphasizing practical applications in wearable sensors, quantum computing components, and interdisciplinary engineering solutions.

Daniel M. Royمشاهده پروفایل

استاد

• Machine Learning
• Statistics
• Probabilistic Programming
• +۷ مورد دیگر

Daniel M. Roy is a Professor at the University of Toronto with cross-appointments in the Departments of Computer Science and Electrical and Computer Engineering. He serves as Associate Chair, Statistics, and is a Research Director at the Vector Institute and a CIFAR Canada AI Chair. His research focuses on foundational principles of prediction, inference, and decision-making under uncertainty, spanning machine learning, statistics, mathematical logic, applied probability, and computer science. He has contributed to learning theory, statistical network analysis, probabilistic programming, and Bayesian nonparametric statistics. Education: Ph.D. in Computer Science from MIT (2011), advised by Leslie Kaelbling. Postdoctoral fellowships at the University of Cambridge (Newton International Fellow and Research Fellow). His research explores information theories of learning , online learning , and nonstandard foundations for decision theory . Recent work includes best paper awards at ICML 2024 and advancements in probabilistic programming systems like Church. His publications address problems in generalization bounds, causal bandits, neural network theory, and exchangeable random structures. Scientific Awards include the MIT/EECS George M. Sprowls Doctoral Dissertation Award and the ICML 2024 Best Paper Award. He advises students and postdocs across statistics, computer science, and machine learning, with alumni now holding positions at institutions like Princeton, Imperial College London, and the University of Chicago.

Ismail Ben Ayedمشاهده پروفایل

دانشیار

• Computer vision
• Optimization
• Medical image analysis
• +۱ مورد دیگر

Ismail Ben Ayed is an Associate Professor at École de technologie supérieure (ETS) in Montreal, Canada, holding the ETS Research Chair on Artificial Intelligence in Medical Imaging. His research bridges computer vision, optimization, and medical image analysis to develop advanced algorithms for clinical applications, with particular focus on cardiac and neurological imaging. His research program centers on medical image segmentation using novel optimization techniques, graph-based methods, and deep learning models. He pioneers approaches for handling volumetric bias, shape compactness, and distribution matching in MRI and cardiac imaging, directly addressing clinical challenges in spine labeling, ventricle segmentation, and tumor detection. His work emphasizes mathematical rigor combined with practical medical relevance. Analysis of his 15 most recent publications (2014-2017) reveals dominant themes in medical image segmentation (80% of works), particularly for cardiac MRI (35%) and neurological applications (25%). Key methodological contributions include distributed optimization frameworks (20%), advanced graph cut techniques (30%), and deep learning architectures (25%), published consistently in top-tier venues including CVPR, MICCAI, and TPAMI. His scientific recognition includes: MICCAI travel award (2017) Outstanding Reviewer Award at CVPR (2015) GE innovation award (2010) He actively mentors researchers as evidenced by his recruitment of PhD students and postdocs, with research supported by the ETS Research Chair and multiple patents. His service includes chairing MICCAI 2017/2015 and IPTA 2017, plus continuous program committee roles at CVPR, ICCV, and MICCAI since 2011. Leading the ETS Research Chair on AI in Medical Imaging, he directs a collaborative team working on clinical translation of computer vision techniques. Current projects focus on cardiac motion analysis, brain tumor segmentation, and spine labeling systems with direct applications in radiology workflows.

• Experimental Gravity
• Astrophysical Signals
• Nuclear Physics
• +۸ مورد دیگر

Reed Essick is an Assistant Professor at the Canadian Institute for Theoretical Astrophysics (CITA), University of Toronto. His research focuses on experimental gravity, astrophysical signals, and nuclear physics, with particular emphasis on neutron stars, black holes, and gravitational waves. He develops advanced statistical methods like hierarchical Bayesian inference and nonparametric analysis for interpreting observational data from pulsars and gravitational wave detectors. Dr. Essick collaborates extensively with international observatories such as LIGO, Virgo, and KAGRA, contributing to cutting-edge projects like multimessenger astronomy and precision cosmology. His work bridges computational astrophysics with observational techniques, addressing fundamental questions about dense matter and strong-field gravity. Key contributions include studies on gravitational wave equation-of-state constraints, pulsar timing analysis, and the application of machine learning to detector data. His research leverages both ground-based interferometers and space-based observations to explore extreme astrophysical environments.

• Algorithmic Databases
• Data Mining
• Computational Geometry
• +۵ مورد دیگر

Stavros Sintos is an Assistant Professor in the Department of Computer Science at the University of Illinois at Chicago (UIC). He joined UIC after a postdoctoral fellowship at the University of Chicago, where he was part of the ChiData group under Sanjay Krishnan. He earned his Ph.D. in Computer Science from Duke University in 2020, advised by Pankaj K. Agarwal. His research focuses on designing efficient algorithms for databases, data mining, and computational geometry . Key themes include: Theoretical guarantees for practical problems Compact indexing structures for query efficiency Geometric optimization combined with database systems Fairness in algorithmic design (e.g., Fair Set Cover, FairHash) Temporal data analysis and dynamic query processing Notable scientific contributions include a Best Paper Award at ICDT 2024 for work on range entropy queries. His publications span top venues like SIGMOD, PODS, VLDB, and SODA, covering areas such as clustering algorithms, synthetic query witnesses, and temporal join optimizations.

Meng Heمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Computational Geometry
• +۷ مورد دیگر

Meng He is a Professor in the Faculty of Computer Science at Dalhousie University. He obtained his PhD from the Cheriton School of Computer Science at the University of Waterloo in 2008 and held postdoctoral and research positions at Carleton University and the University of Waterloo before joining Dalhousie. He is affiliated with the Algorithms & Bioinformatics research cluster and is actively recruiting graduate students for master's and PhD studies, as well as supervising honors theses and USRA internships. His research focuses on the design and analysis of efficient algorithms and data structures, particularly in the areas of computational geometry, databases, text retrieval, and bioinformatics. His work often involves developing succinct and dynamic data structures for fundamental problems in graph theory, trees, and geometric data. His recent publications show a strong focus on path and distance queries in various graph types (especially interval graphs and trees), range counting, mode queries, and succinct representations. The research trend emphasizes theoretical foundations combined with practical efficiency, often addressing dynamic and space-constrained scenarios. Alberto Apostolico Best Paper Award of CPM 2017 Dr. He has supervised numerous PhD and master's students and collaborators, frequently co-authoring with researchers such as J. Ian Munro, Travis Gagie, Gonzalo Navarro, and Norbert Zeh. His research has been supported by grants from NSERC and other funding agencies, though specific grant details are not listed in the provided text. He has also contributed significantly to the academic community through editorial work for journals like Computational Geometry - Theory and Applications and Algorithmica , and by organizing major conferences such as CCCG and WADS. He leads a research group focused on algorithms and data structures, fostering collaborations both within Dalhousie and internationally. Future work is likely to continue exploring the theoretical and practical aspects of dynamic and succinct data structures, with applications in large-scale data processing and information retrieval systems.

Mustapha C.E. Yagoubمشاهده پروفایل

استاد

• RF/Microwave Circuit Design
• RFID Systems
• Antenna Design
• +۵ مورد دیگر

Mustapha C.E. Yagoub is a Full Professor at the School of Electrical Engineering and Computer Science, University of Ottawa, with over 500 publications in RF/microwave CAD, RFID systems, neural networks, and applied electromagnetics. He leads research in the ELEMENT Laboratory and RFM Research Group , focusing on wireless communication systems and nonlinear device modeling. PhD in Electronics (Institut National Polytechnique de Toulouse, 1994) Magister in Telecommunications (École Nationale Polytechnique d'Alger, 1987) Dipl.-Ing. in Electronics (École Nationale Polytechnique d'Alger, 1979) His research bridges Microwave Circuit Design with Artificial Intelligence , including applications in Energy Conservation and Telecommunication Systems . Key trends in his publications include hybrid modeling techniques combining Neural Networks with Computational Electromagnetics for optimizing Antenna Design and RF Components . He is a Senior Member of IEEE and licensed with the Professional Engineers of Ontario and Ordre des Ingénieurs du Québec . His lab teams focus on High-Tc Superconducting Devices and Directional Antenna Optimization for RFID networks.

Bin Hanمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Partial Differential Equations (PDEs)
• Applied Harmonic Analysis
• +۴ مورد دیگر

Bin Han is a Professor of Mathematics at the Department of Mathematical and Statistical Sciences , University of Alberta, Canada. He holds a PhD (1998), MSc (1994), and BSc (1991) in Mathematics from the University of Alberta, Chinese Academy of Sciences, and Fudan University, respectively. Research Interests: Computational Mathematics: High-order finite difference methods, numerical solutions of PDEs (Helmholtz, elliptic interface, Burgers' equations), and Fourier/wavelet-based algorithms. Applied Harmonic Analysis: Framelets/wavelets with applications in image processing, data sciences, and deep learning, focusing on directional and quasi-tight properties. Wavelet Theory: Construction of wavelets on bounded intervals for boundary value problems, Gibbs phenomenon analysis, and stability of refinable functions. Computer Aided Geometric Design (CAGD): Subdivision schemes, spline approximation, and isogemetric analysis. Article Trends: His recent work (2021-2022) emphasizes high-order finite difference methods for Helmholtz and interface problems, directional tensor product complex tight framelets for image processing, and quasi-tight framelets with balancing orders for robustness and sparsity. Scientific Awards: NSERC Postdoctoral Fellowship (1999-2000) Advising & Grants: He has supervised PhD students Qiwei Feng, Michelle Michelle, Ran Lu, and Chenzhe Diao. His research is supported by NSERC, Westgrid, Compute Canada, and MITACS.

Maja Krzicمشاهده پروفایل

استاد

• Soil Science
• Conservation Biology
• Sustainable Agriculture
• +۳ مورد دیگر

Dr. Maja Krzic is a Professor at the Department of Forest and Conservation Sciences within UBC's Faculty of Land and Food Systems. Her research bridges soil science with sustainability education through community engagement and digital innovation, focusing on human impacts on soil properties across agricultural, grassland, and forest ecosystems. She develops cutting-edge teaching tools like the Digging into Canadian Soils open textbook and SOILx interactive platform. PhD (University of British Columbia, 1997) MSc (University of Belgrade, 1990) BSc (University of Belgrade, 1986) Her publications (2010-2025) explore greenhouse gas dynamics in agricultural systems, soil carbon sequestration in elevation gradients, and innovative educational approaches including problem-based learning and augmented reality. Recent work addresses climate-resilient soil management in coastal British Columbia and gender parity in soil science academia. Major awards include: 3M National Teaching Fellow (2016) UBC Killam Teaching Prize (2006) Fellow, Soil Science Society of America (2023) Platinum AVA Digital Award for SOILx (2015) She has mentored 28 graduate students and postdocs, including Amy Wells (2024), Clara Roa-Garcia (PhD, 2018), and Preston Cumming (Postdoc, 2015). Current projects focus on climate change adaptation in Delta farmland and regenerative agricultural practices.

Shahin Kamaliمشاهده پروفایل

دانشیار

• Algorithms Design
• Algorithms Analysis
• Online Algorithms
• +۹ مورد دیگر

Shahin Kamali is an Associate Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. Previously, he served as an Assistant Professor at York University (2022-2024) and the University of Manitoba (2017-2022), where he continues to hold an Adjunct Professor position. He completed his Ph.D. in Computer Science at the University of Waterloo in 2014 and was a postdoctoral fellow at MIT's CSAIL lab from 2015-2017. Dr. Kamali received his B.Sc. from the University of Tehran and his M.Sc. from Concordia University, both in Computer Science. He has earned teaching certifications including the Kaufman Teaching Certificate Program from MIT and the Certificate in University Teaching from the University of Waterloo. His research focuses on algorithms' design, analysis, and limitations, with particular emphasis on online problems such as bin packing, paging, list update, and k-Server. His work extends to big-data applications of algorithms in data compression, graph partitioning, resource allocation in the cloud, and algorithmic aspects of blockchain technology. His recent publications (2023-2025) demonstrate a strong focus on learning-augmented algorithms, online computation with predictions, and novel applications in blockchain and graph theory. His research has been supported by multiple NSERC grants since 2010, with recent projects investigating models, applications, and limitations of online algorithms. He has successfully supervised numerous graduate students and serves on various committees related to algorithms conferences and academic governance. University of Waterloo Doctoral Thesis Completion Award (2014) University of Waterloo Mathematics Graduate Experience Award (2008) Dr. Kamali teaches advanced courses in algorithms and data structures and is scheduled to teach Design and Analysis of Algorithms (EECS 3101) in Fall 2025 and Advanced Data Structures (EECS 4101/5101) in Winter 2026. He is actively involved in organizing major conferences, including CCCG and WADS 2025 at York University.

Avery E. Broderickمشاهده پروفایل

دانشیار

• Theoretical Astrophysics
• Black Hole Physics
• General Relativity
• +۳ مورد دیگر

Avery E. Broderick is an Associate Professor in the Department of Physics & Astronomy at the University of Waterloo and an Associate Faculty Member at the Perimeter Institute for Theoretical Physics. His research focuses on theoretical astrophysics, particularly studying compact objects like black holes and testing general relativity through astronomical observations. He is a key member of the Event Horizon Telescope (EHT) collaboration, which produced the first direct images of black hole horizons in M87* and Sagittarius A*. Broderick’s work emphasizes relativistic astrophysical phenomena such as accretion flows, jet formation, and polarization signatures. He collaborates extensively with observational astronomers and computational physicists to model black hole environments using general relativistic magnetohydrodynamic simulations. His recent research includes analyzing EHT data to constrain black hole spin, test spacetime metrics, and study photon ring dynamics. He also explores next-generation EHT (ngEHT) capabilities for higher-resolution imaging and multi-wavelength studies. Broderick has delivered invited lectures globally, including at Harvard-Smithsonian CfA, MIT, and the Aspen Center for Physics, reflecting his leadership in the field. Broderick’s contributions span over 135 refereed publications and conference proceedings, with a focus on black hole astrophysics, VLBI techniques, and relativistic plasma physics. His work bridges theoretical predictions with observational data, advancing our understanding of extreme gravitational regimes in the universe.

Erik Rosolowskyمشاهده پروفایل

استاد

• Astrophysics
• Star Formation
• Interstellar Medium
• +۳ مورد دیگر

Erik Rosolowsky is a Professor of Physics and Associate Chair in the Faculty of Science at the University of Alberta. He holds a PhD from the University of California, Berkeley (2005), MA from UC Berkeley (2001), and BA from Swarthmore College (1998). His research focuses on understanding the recycling of stellar matter in galaxies, using radio and submillimeter telescopes alongside algorithmic methods. Key interests include star formation, interstellar medium dynamics, and molecular gas properties in nearby galaxies. He teaches graduate and undergraduate courses in astronomy and physics, emphasizing flipped classrooms and problem-solving skills. Notable courses include PHYS 144 (Newtonian Mechanics) and PHYS 574 (Data Analysis in Physics). Rosolowsky is a core member of the PHANGS collaboration, leveraging JWST and ALMA for high-resolution galaxy studies. He also leads efforts in data science for astrophysics, developing tools like CARTA and CIRADA. Recent work includes analyzing molecular gas in NGC 6822, modeling starburst galaxies, and processing JWST data. His research bridges observational techniques with computational methods to explore galaxy evolution and stellar lifecycle connections. Despite no listed awards, his contributions to astrophysical data pipelines and collaborative surveys highlight his academic impact.

Sai Vanapalliمشاهده پروفایل

استاد

• Geotechnical Engineering
• Unsaturated Soil Mechanics
• Pavement Design
• +۷ مورد دیگر

Dr. Sai Vanapalli is a Professor and Chair of the Department of Civil Engineering at the University of Ottawa. He holds a Ph.D. from the University of Saskatchewan (1994) and an M.Tech. from Kakatiya University, India. His research focuses on unsaturated soil mechanics, including foundation design, expansive soils, freeze-thaw effects, and pavement engineering. He has authored/co-authored over 125 publications, supervised 16 graduate students, and pioneered state-of-the-art laboratory equipment for unsaturated soil testing. Dr. Vanapalli has organized major international conferences such as the Diamond Jubilee Canadian Geotechnical Conference (2007) and the 12th IACMAG (2008). He received the Stermac Award (2010) and multiple teaching awards, including the OCUFA Teaching Excellence Award (2007). His lab houses advanced equipment like the modified ring shear test apparatus and University of Ottawa Bearing Capacity Equipment (UOBCE), supported by grants from the Canadian Foundation for Innovation (CFI-LOF). Key research themes include geotechnical engineering, unsaturated soil technology, geo-environmental studies, and critical state mechanics. His work bridges theory and practice, emphasizing practical engineering solutions for unsaturated soil challenges. He actively reviews for journals like Geotechnique and serves on editorial boards, furthering global knowledge exchange in geotechnics.