جست‌وجوی استاد و پژوهشگر

Vijay K. Soodمشاهده پروفایل

استاد

• Power Systems
• Power Electronics
• HVDC and FACTS Controllers
• +۳ مورد دیگر

Dr. Vijay K. Sood is a Professor in the Department of Electrical, Computer and Software Engineering at Ontario Tech University. He holds a PhD in Power Electronics from Bradford University (UK), and has extensive experience in power systems, power electronics, and renewable energy integration. His research focuses on High Voltage Direct Current (HVDC) systems, Flexible AC Transmission Systems (FACTS), power electronics converters, and grid protection. He has received numerous awards, including Life Fellow of IEEE, Emeritus Fellow of the Canadian Academy of Engineers, and the 2000 IEEE Third Millennium Medal. Dr. Sood has authored/co-authored over 100 publications, including peer-reviewed articles in top journals like IEEE Transactions on Power Electronics and International Journal of Electrical Power & Energy Systems. His work addresses challenges in grid stability, renewable energy integration, and advanced control strategies for power systems. Education: PhD (Power Electronics), Bradford University, UK (1977) MASc (Electrical Machines), Strathclyde University, Scotland (1969) BSc (Electrical Engineering), Nairobi University, Kenya (1967) Awards: IEEE Third Millennium Medal (2000) Canadian Pacific Railway Engineering Award (2002) IEEE Regional Activities Board Achievement Awards (2001, 2006) His research emphasizes practical solutions for modern power systems, including HVDC controller design, synthetic inertia control for renewable grids, and advanced fault detection techniques. He has collaborated internationally on projects like the Energy System Observatory of Honduras and grid architecture models for multi-terminal DC systems.

Svetlana Yanushkevichمشاهده پروفایل

استاد

• Biometric Technologies
• Machine Learning
• Signal Processing
• +۳ مورد دیگر

Dr. Svetlana Yanushkevich is a Professor in the Department of Electrical and Software Engineering at the Schulich School of Engineering, University of Calgary. She is also a Full Member of the Hotchkiss Brain Institute and the Mathison Centre for Mental Health Research and Education. Her research focuses on biometric technologies, decision support systems, biomedical applications, and computational intelligence. She leads the Biometric Technologies Laboratory, developing strategies for risk assessment in biometric systems and healthcare monitoring through machine reasoning and signal processing. Education : BSc/MSc in Electrical Engineering (1989), State University of Informatics and Radioelectronics, Minsk PhD in Electrical Engineering (1992), same institution Dr. Habilitated in Technical Sciences (1999), Warsaw University of Technology Research Interests : Dr. Yanushkevich’s work spans biometric system design (e.g., gait analysis, facial attributes), decision support via probabilistic models (Bayesian networks, causal inference), biomedical applications (stroke rehabilitation, wearable sensors), and computational intelligence for data science. She emphasizes fairness, bias mitigation, and trustworthiness in AI systems, particularly in healthcare and accessibility contexts. Recent Research Trends : Her recent publications address causal modeling for accessibility barriers, UAV operator cognitive workload, and medical device optimization in radiation therapy. She explores AI ethics, stress contagion in human-robot teams, and cross-spectral biometric systems. Awards & Recognition : 2024 FEIC Fellow (Engineering Institute of Canada) 2019 Research Excellence Award (Schulich School of Engineering) 2001 Senior IEEE Membership Advising & Grants : She coordinates courses like ENCM 509 (Biometric Systems Design) and ENEL 610 (Biometric Technologies). Her research is supported by grants focusing on healthcare AI, accessibility technologies, and computational epidemiology. Labs & Collaborations : Her Biometric Technologies Lab collaborates with institutions like Hokkaido University and the IEEE Computational Intelligence Society. Projects include wearable health monitoring, decision support platforms, and AI-driven epidemiological modeling.

Chen Xuمشاهده پروفایل

دانشیار

• Sparse Modeling
• Statistical Learning
• Feature Selection
• +۵ مورد دیگر

Chen Xu is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa. He holds an M.A. from York University and a PhD from the University of British Columbia. His research focuses on sparse modeling, statistical learning, and big data processing, with an emphasis on both theoretical and computational advancements. Dr. Xu is affiliated with the Faculty of Science and contributes to editorial roles for journals such as the Journal of the American Statistical Association and Electronic Journal of Statistics. Education: M.A., York University PhD, University of British Columbia Research interests include feature selection, regularization methods, kernel methods, and high-dimensional regression. His work addresses computational challenges in big data, proposing efficient algorithms for tasks like singular value decomposition, clustering, and distributed feature screening. Recent publications highlight advancements in multiview PCA, low-tubal-rank tensor recovery, and model-free regression techniques. Publications span prestigious journals like the Journal of the American Statistical Association and IEEE Transactions series, focusing on statistical methodology, machine learning applications, and scalable computational solutions for complex data problems. Editorial Service: Associate Editor, Journal of American Statistical Association-T&M (2023–present) Associate Editor, Electronic Journal of Statistics (2023–present) Former Associate Editor, The Canadian Journal of Statistics (2019–2021) His research groups are Statistics and Biostatistics, and Data Science, Machine Learning, and Artificial Intelligence. He has no listed awards but maintains active editorial and academic collaborations in computational statistics and machine learning.

Simon Oyaمشاهده پروفایل

استادیار

• Privacy-enhancing technologies
• Computer Security
• Signal processing for security and privacy
• +۴ مورد دیگر

Simon Oya is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), Faculty of Applied Science. He holds a PhD in Information Technologies and Communications from the University of Vigo (Spain) and was previously a postdoctoral fellow at the Cryptography, Security and Privacy (CrySP) group at the University of Waterloo. His educational background includes: BSc, MSc, PhD from University of Vigo (Spain) Simon Oya's research focuses on designing and evaluating privacy-enhancing technologies with strong privacy and utility guarantees. He approaches privacy problems from a statistical perspective, using theoretical tools from signal processing and information theory to quantify privacy leakage and develop effective defenses. His primary research areas include: Privacy-preserving searchable encryption Machine learning privacy (particularly membership inference attacks) Anonymous communication systems Location privacy Differential privacy His publication record demonstrates a consistent focus on analyzing and improving privacy mechanisms across various domains. His recent work has particularly emphasized the intersection of machine learning and privacy, as well as advancing techniques for searchable encryption. His research methodology typically involves developing statistical models to understand privacy leakage and designing optimization-based approaches to improve privacy-utility tradeoffs. His notable scientific contributions include developing attacks against searchable encryption schemes to better understand their privacy limitations, and designing improved privacy mechanisms for location-based services. His work on statistical disclosure attacks against anonymous communication systems has also been influential in the field. As an educator, he teaches CPEN 442: Introduction to Cybersecurity at UBC. He actively seeks motivated graduate students interested in privacy research, particularly those with strong backgrounds in statistics, machine learning, or optimization.

Urs Hengartnerمشاهده پروفایل

دانشیار

• Information Privacy
• Computer Security
• Network Security
• +۳ مورد دیگر

Urs Hengartner is an Associate Professor at the Department of Computer Science, University of Waterloo. His research focuses on information privacy, computer and network security with emphasis on smartphones, IoT, and machine learning-based authentication systems. He holds a Ph.D. (2005) and M.Sc. (2003) from Carnegie Mellon University, and a Diploma from ETH Zürich (1997). His work spans Adaptive security attacks on ML systems Implicit user authentication frameworks Privacy-preserving technologies for location and genomic data Secure authentication systems resilient to voice/spoofing attacks Recent publication trends show a strong focus on adversarial attack detection (e.g., watermarking evasion, diffusion model attacks) and context-aware authentication systems . His frameworks like MRAAC and SHRIMPS address multi-stage authentication challenges in mobile ecosystems. Key contributions include frameworks for evaluating multi-user authentication systems (SHRIMPS), risk-aware access control (MRAAC), and novel defense strategies against collaborative robot traffic fingerprinting. His work bridges security mechanisms with user-centric design principles.

Pierre Duchesneمشاهده پروفایل

استاد

• Time Series Analysis
• Sampling Theory
• Multivariate Analysis
• +۲ مورد دیگر

Pierre Duchesne is a Full Professor in the Department of Mathematics and Statistics at the University of Montreal . He serves as Professor-responsibility for the M.Sc. and Ph.D. in Statistics programs (2000-2004). His research focuses on applied statistics with emphasis on: Time Series Analysis (univariate and multivariate models, serial correlation testing, portmanteau statistics) Sampling Theory (robust estimation methods, calibration estimators) Multivariate Analysis (ARCH effects, vector autoregressive models, causality testing) Applications in Econometrics and Financial Econometrics His work combines theoretical development with practical implementation through: Wavelet-based diagnostic methods Simulation studies for model validation Software development (S-PLUS/SAS) for statistical analysis Collaboration with organizations like Statistics Canada and Canadian Journal of Statistics He has served as Associate Editor for journals including Computational Statistics & Data Analysis (CSDA) and Canadian Journal of Statistics (CJS/RCS) .

Nir Rotenbergمشاهده پروفایل

دانشیار

• Quantum Photonics
• Nanophotonics
• Quantum Optics
• +۳ مورد دیگر

Dr. Nir Rotenberg is an Associate Professor in the Department of Physics, Engineering Physics, and Astronomy at Queen's University. He is affiliated with the Faculty of Arts and Science, the Condensed Matter Physics & Optics group, and the Centre for Nanophotonics. He holds a PhD from the University of Toronto and focuses on quantum photonics and nanophotonics, particularly exploring nonlinear phenomena in solid-state quantum systems. His lab develops novel quantum devices and circuits using nanophotonic platforms, emphasizing single quantum emitter interactions with few photons. Research Interests: Quantum photonics, nanophotonics, quantum optics, nonlinear optics, quantum devices, and solid-state quantum emitters. His work bridges fundamental physics with applications in quantum information processing and photonic technologies. Recent publications highlight advancements in quantum dot positioning, waveguide-QED systems, and reconfigurable photonic circuits. He collaborates internationally and is a member of the Quantum Nanophotonics Lab (QNL), based in Stirling 308H. Dr. Rotenberg's research has implications for quantum computing, secure communication, and advanced optoelectronic systems.

Benoit Champagneمشاهده پروفایل

استاد

• Statistical Signal Processing
• Multi-antenna Processing
• Signal Detection and Estimation
• +۹ مورد دیگر

Benoit Champagne is a Full Professor in the Department of Electrical and Computer Engineering at McGill University, Montreal. His research focuses on statistical signal processing, with applications in wireless communications, multi-antenna systems, and adaptive filtering. He has held academic positions since 1990, including roles at INRS-Telecom before joining McGill in 1999. He teaches graduate and undergraduate courses such as ECSE 305 (Probability and Random Signals), ECSE 512 (Digital Signal Processing), and ECSE 617 (Array Signal Processing). Education: B.Eng. (Electrical Engineering) and M.Sc. (Physics) from Université de Montréal (1983, 1985), Ph.D. in Electrical Engineering from University of Toronto (1990). His research spans signal detection/estimation, speech enhancement, MIMO systems, and physical layer security, with over 150+ publications in top journals and conferences. He has supervised numerous graduate students and holds grants from NSERC, CFI, and industry partners like Nortel and Bell Canada. His work emphasizes practical implementations, including hybrid analog/digital beamforming for mmWave systems and energy-efficient resource allocation in D2D communications. He has contributed to IEEE standards through editorial roles (e.g., IEEE Transactions on Signal Processing) and conference organization (e.g., IEEE VTC 2016). Current research explores machine learning integration with signal processing for next-generation wireless systems. Notable contributions include advancements in subspace tracking, cognitive radar systems, and distributed adaptive filtering. His lab collaborates internationally, addressing challenges in 5G/6G networks, massive MIMO, and secure communications.

Qian Liuمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Applied Statistics
• +۵ مورد دیگر

Dr. Qian Liu is an Assistant Professor in the Department of Applied Computer Science at the University of Winnipeg . She holds a PhD in Individual Interdisciplinary Studies, integrating Computer Science, Statistics, and Medical Genetics. Her research focuses on advanced data science techniques for healthcare and material science, including multi-modal data integration and interpretable AI. Education : 2023: Postdoc, Western University, Canada 2023: PhD, University of Manitoba, Canada 2019: MSc, University of Manitoba, Canada Research Interests : Machine learning, deep learning, bioinformatics, computational biology, medical imaging, and their applications in healthcare and material informatics. Her lab develops tools for genomic integration, radiogenomics, and AI-driven biomarker discovery. Recent Contributions : Her work spans AI in nanomaterial classification, breast cancer biomarker discovery, and emotion recognition. Articles highlight innovations in graph neural networks, diffusion models, and interpretable deep learning. Awards : 2024 University of Manitoba Distinguished Dissertation Award 2019 Best Oral Paper at IEEE Bioinformatics Conference Grants : NSERC 2024 Discovery Grant (Principal Investigator) CIHR 2023 Project Grant (Co-Applicant) Labs/Teams : Active in interdisciplinary collaborations across computer science, statistics, and biomedical domains. Lab members include postdocs, PhD/MS students, and undergraduate researchers.

Kyle O'Keefeمشاهده پروفایل

استاد

• Global Navigation Satellite Systems (GNSS)
• Indoor Positioning and Navigation
• Ultra-wideband Ranging
• +۶ مورد دیگر

Kyle O'Keefe is a Professor in the Department of Geomatics Engineering at the University of Calgary's Schulich School of Engineering. He holds dual B.Sc. degrees in Geomatics Engineering (University of Calgary, 2000) and Honours Physics (University of British Columbia, 1997), and a Ph.D. in Geomatics Engineering (University of Calgary, 2004). He is a Professional Engineer (P.Eng.) registered with the Association of Professional Engineers and Geoscientists of Alberta since 2005. His research focuses on positioning and navigation technologies, including Global Navigation Satellite Systems (GNSS) advancements Ultra-wideband (UWB) ranging for vehicle/pedestrian navigation Indoor positioning using wireless signals Wearable sensor integration for biomechanics and navigation GNSS spoofing detection and cybersecurity Notable projects include: Development of UWB-augmented GNSS for RTK surveying (2007–present) Wearable sensor systems for rowing/kayaking motion analysis (2017–present) CanX-2 nanosatellite GPS receiver operations (2008) Igliniit project with Inuit hunters for Arctic environmental monitoring (2006–2009) Multi-constellation GNSS evaluation across 20+ years He has received prestigious awards including the Michael Richey Medal (2011) and multiple Best Paper Awards at IPIN and ION conferences. His teaching includes courses like Advanced GNSS Theory and Wireless Location. Active in professional organizations, he co-edits special journal issues and advises industry on emerging navigation technologies.

Richard Naudمشاهده پروفایل

استادیار

• Computational Neuroscience
• Neural Coding
• Statistical Methods in Neuroscience
• +۵ مورد دیگر

Richard Naud is an Assistant Professor in the Department of Cellular and Molecular Medicine at the Faculty of Medicine, University of Ottawa, with a cross-appointment in Physics at the Faculty of Science. He holds dual research positions at the Center for Neural Dynamics (CND) and Brain and Mind Research Institute (BMRI), focusing on computational approaches to decode neural signaling mechanisms and develop brain-machine interfaces. His educational background includes: PhD in Neuroscience from École Polytechnique Fédérale de Lausanne (EPFL), 2011 MSc in Physics from McGill University, 2006 BSc in Physics from McGill University, 2004 Dr. Naud's research investigates how neurons encode information through spikes, bursts, and silences using mathematical models and statistical analysis of electrophysiology data. His lab develops computational protocols for synaptic dynamics analysis, studies dendritic computation in neurological diseases, and creates neuromorphic algorithms for spiking neural networks. Current work emphasizes serotonin system dynamics, burst coding mechanisms, and neural network simulations for demyelinating conditions. Analysis of his recent publications reveals three dominant research vectors: (1) Burst coding as an independent information channel beyond firing rates, (2) Serotonin-mediated value coding in decision systems, and (3) Neuromorphic implementation of biologically plausible learning rules. His work bridges theoretical neuroscience with clinical applications in stroke recovery and neurological disorders. Dr. Naud leads the Neural Coding Lab, which actively recruits postdoctoral fellows, graduate students, and undergraduates for projects in neural coding theory, computational psychiatry, and neuromorphic engineering. The lab maintains collaborations with experimental neuroscience groups for model validation and develops open-source tools like SRPlasticity for synaptic dynamics analysis.

Rebecca Feldmanمشاهده پروفایل

استادیار

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Pulse Sequence Design
• +۴ مورد دیگر

Rebecca Feldman is an Assistant Professor in Medical Physics and Physics at the Irving K. Barber Faculty of Science, University of British Columbia Okanagan. Her research integrates MR physics, engineering, and medical research to advance MRI pulse sequences and hardware for clinical translation, particularly in neurological disorders. University: University of British Columbia Okanagan Academic Rank: Assistant Professor PhD: University of Western Ontario Research Interests: Dr. Feldman specializes in technical innovation in MRI (accelerated imaging, spectroscopic imaging, non-proton imaging) and translational research applying MRI to neurological disease detection, characterization, and treatment. Her work leverages ultra-high-field (7T) MRI for enhanced resolution of brain structures like hippocampal subfields and perivascular spaces. Publications: Recent work includes advancements in self-supervised medical imaging backbones (MedMAE), segmentation of venous structures in epilepsy, automated MRI pulse design via neural networks, and clinical applications of 7T MRI in neurosurgical planning and psychiatric disorders like major depressive disorder. Teaching: Currently teaches courses in physics, including physics of waves.

Souradeep Duttaمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۴ مورد دیگر

Souradeep Dutta is an Assistant Professor in the Department of Electrical and Computer Engineering within the Faculty of Applied Science at the University of British Columbia (UBC), joining in Fall 2024 after postdoctoral research at the PRECISE center, University of Pennsylvania. His academic credentials include a PhD in Electrical and Computer Engineering from the University of Colorado Boulder and a BE in Instrumentation and Electronics Engineering from Jadavpur University, India. Education: PhD, Electrical and Computer Engineering, University of Colorado Boulder BE, Instrumentation and Electronics Engineering, Jadavpur University, India Dr. Dutta's research centers on artificial intelligence with emphasis on reinforcement learning, cyber-physical systems, and formal methods. He investigates fundamental challenges in efficient data-driven learning and assurance techniques for learned models, targeting applications in robotics and medical devices. His work bridges theoretical guarantees with practical implementations for safe human-machine knowledge transfer. Analysis of his 15 most recent publications reveals dominant trends in robustness verification for learning-enabled systems, memory-based adaptation techniques, and distribution shift handling. His research consistently addresses safety-critical applications, particularly in medical diagnostics (e.g., ECG analysis, acne grading) and autonomous control systems, while maintaining strong theoretical foundations in formal methods. Awards: Recognition at top-tier conferences including ICLR, CORL, HSCC, ICCPS, ICAPS, NFM, L4DC, CHASE, and ADHS Dr. Dutta actively seeks graduate students for Fall 2025 and welcomes interdisciplinary collaborations. He serves on program committees for AAAI, ICCPS, ICML, and NeurIPS, and is available for undergraduate research supervision. His advising philosophy emphasizes safe and efficient transfer of human expertise to machine systems. He leads a research group at UBC focused on developing verifiable AI frameworks for cyber-physical applications, with current projects spanning medical device assurance and adaptive robotics control systems.

Curtis Bakerمشاهده پروفایل

استاد

• Visual Perception
• Neurophysiology
• Psychophysics
• +۱۲ مورد دیگر

Curtis Baker is a Senior Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC), Montreal General Hospital site, and a Professor in the Department of Ophthalmology and Visual Sciences at McGill University. His research focuses on understanding human visual perception , particularly low-level neural mechanisms that underpin everyday visual processing of figure-ground and local depth relationships through cues like contrast, texture, and motion . Institutional Affiliation: RI-MUHC, McGill University Academic Rank: Professor His lab employs human psychophysics , electrophysiology , optical imaging , and computational modeling to study how early visual processing detects and utilizes complex cues. Key research areas include receptive field dynamics , second-order boundary perception , and machine learning applications in visual neuroscience. Recent publications highlight his work on convolutional neural networks for receptive field estimation, Y-like neuronal responses in human vision, and texture regularity models using wavelet analysis. Collaborations span computational neuroscience , optical imaging , and depth perception mechanisms. Students and researchers in his lab include graduate students in Integrated Program in Neuroscience , Physiology , and Biomedical Engineering , alongside alumni contributing to neurophysiology and computational biology projects.

Mihai Nicaمشاهده پروفایل

استادیار

• Deep Neural Networks (DNNs)
• Probability Theory
• Stochastic Processes
• +۴ مورد دیگر

Prof. Mihai Nica is an Assistant Professor in the Department of Mathematics and Statistics at the University of Guelph, affiliated with the CARE-AI institute and Vector Institute. His research focuses on probability theory, stochastic processes, and their applications to machine learning, particularly deep neural networks (DNNs). He explores scaling limits of DNNs, numerical methods using neural networks, and phase transitions in high-dimensional learning problems. Education: B.Math in Pure & Applied Math with Physics Option, University of Waterloo PhD in Mathematics, Courant Institute of Mathematical Sciences, New York University Postdoctoral Fellow at University of Toronto (supervised by Jeremy Quastel) Research Interests: His work bridges mathematical theory and practical AI applications, emphasizing topics like the neural tangent kernel, KPZ universality class, and stochastic processes in machine learning. Notable contributions include studies on neural network dynamics, random matrices, and directed polymers. Publications: Over 15 peer-reviewed articles in journals like Communications in Pure and Applied Mathematics and Electronic Journal of Probability , with a focus on theoretical foundations of AI and stochastic systems. Recent work explores infinite-width limits of neural networks and their connections to differential equations. Labs/Teams: Affiliated with CARE-AI (bridging mathematics, engineering, and philosophy) and the Vector Institute, fostering interdisciplinary collaborations.